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Early, Kirstin. "Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1117.
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As data become more pervasive and computing power increases, the opportunity for transformative use of data grows. Collecting data from individuals can be useful to the individuals (by providing them with personalized predictions) and the data collectors (by providing them with information about populations). However, collecting these data is costly: answering survey items, collecting sensed data, and computing values of interest deplete finite resources of time, battery, life, money, etc. Dynamically ordering the items to be collected, based on already known information (such as previously collected items or paradata), can lower the costs of data collection by tailoring the information-acquisition process to the individual. This thesis presents a framework for an iterative dynamic item ordering process that trades off item utility with item cost at data collection time. The exact metrics for utility and cost are application-dependent, and this frame- work can apply to many domains. The two main scenarios we consider are (1) data collection for personalized predictions and (2) data collection in surveys. We illustrate applications of this framework to multiple problems ranging from personalized prediction to questionnaire scoring to government survey collection. We compare data quality and acquisition costs of our method to fixed order approaches and show that our adaptive process obtains results of similar quality at lower cost. For the personalized prediction setting, the goal of data collection is to make a prediction based on information provided by a respondent. Since it is possible to give a reasonable prediction with only a subset of items, we are not concerned with collecting all items. Instead, we want to order the items so that the user provides information that most increases the prediction quality, while not being too costly to provide. One metric for quality is prediction certainty, which reflects how likely the true value is to coincide with the estimated value. Depending whether the prediction problem is continuous or discrete, we use prediction interval width or predicted class probability to measure the certainty of a prediction. We illustrate the results of our dynamic item ordering framework on tasks of predicting energy costs, student stress levels, and device identification in photographs and show that our adaptive process achieves equivalent error rates as a fixed order baseline with cost savings up to 45%. For the survey setting, the goal of data collection is often to gather information from a population, and it is desired to have complete responses from all samples. In this case, we want to maximize survey completion (and the quality of necessary imputations), and so we focus on ordering items to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. One item utility metric for this problem is information gain to get a “representative” set of answers from the respondent. Furthermore, paradata collected during the survey process can inform models of user engagement that can influence either the utility metric ( e.g., likelihood therespondent will continue answering questions) or the cost metric (e.g., likelihood the respondent will break off from the survey). We illustrate the benefit of dynamic item ordering for surveys on two nationwide surveys conducted by the U.S. Census Bureau: the American Community Survey and the Survey of Income and Program Participation.
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Zhang, Bo. "Machine Learning on Statistical Manifold". Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.
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This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed and useful. We present many interesting and promising empirical clustering results, which demonstrate the statistical-distance-based clustering algorithms often outperform the same algorithms with the Euclidean distance in many complex scenarios. In particular, we apply our statistical-distance-based hierarchical and k-means clustering algorithms to the univariate normal distributions with k = 2 and k = 3 clusters, the bivariate normal distributions with diagonal covariance matrix and k = 3 clusters, and the discrete Poisson distributions with k = 3 clusters. Finally, we prove the k-means clustering algorithm applied on the discrete distributions with the Hellinger distance converges not only to the partial optimal solution but also to the local minimum.
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Höstklint, Niklas, i Jesper Larsson. "Dynamic Test Case Selection using Machine Learning". Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296634.
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Testing code is a vital part at any software producing company, to ensure no faulty code slips through that could have detrimental consequences. At Ericsson, testing code before publishing is a very costly process which can take several hours. Currently, every single test is run for all submitted code. This report aims to address the issue by building a machine learning model that determines which tests need to be run, so that unnecessary tests are left out, saving time and resources. It is however important to find the failures, as having certain failures pass through into production could have all types of economic, environmental and social consequences. The result shows that there is great potential in several different types of models. A Linear Regression model found 92% of all failures within running 25% of all test categories. The linear model however plateaus before finding the final failures. If finding 100% of failures is essential, a Support Vector Regression model proved the most efficient as it was the only model to find 100% of failures within 90% of test categories being run.Testning av kod är en avgörande del för alla mjukvaruproducerande företag, för att säkerställa att ingen felaktig kod som kan ha skadlig påverkan publiceras. Hos Ericsson är testning av kod innan det ska publiceras en väldigt dyr process som kan ta flera timmar. Vid tiden denna rapport skrivs så körs varenda test för all inlämnad kod. Denna rapport har som mål att lösa/reducera problemet genom att bygga en modell med maskininlärning som avgör vilka tester som ska köras, så onödiga tester lämnas utanför vilket i sin tur sparar tid och resurser. Dock är det viktigt att hitta alla misslyckade tester, eftersom att tillåta dessa passera till produktionen kan innebära alla möjliga olika ekonomiska, miljömässiga och sociala konsekvenser. Resultaten visar att det finns stor potential i flera olika typer av modeller. En linjär regressionsmodell hittade 92% av alla fel inom att 25% av alla test kategorier körts. Den linjära modellen träffar dock en platå innan den hittar de sista felen. Om det är essentiellt att hitta 100% av felen, så visade sig en support vector regressionsmodell vara mest effektiv, då den var den enda modellen som lyckades hitta 100% av alla fel inom att 90% alla test kategorier hade körts.
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Rowe, Michael C. (Michael Charles). "A Machine Learning Method Suitable for Dynamic Domains". Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278720/.
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The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and values.
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Kelly, Michael A. "A methodology for software cost estimation using machine learning techniques". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273158.
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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1993.Thesis advisor(s): Ramesh, B. ; Abdel-Hamid, Tarek K. "September 1993." Bibliography: p. 135. Also available online.
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Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.
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The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated.Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification". Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.
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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance dans la vie réelle. La première direction de recherche que nous abordons dans le domaine de la classification non supervisée dynamique est le problème de la classification dynamique des données d'expression génétique. L'expression génétique représente le processus par lequel l'information d'un gène est convertie en produits de gènes fonctionnels : des protéines ou des ARN ayant différents rôles dans la vie d'une cellule. La technologie des micro-réseaux moderne est aujourd'hui utilisée pour détecter expérimentalement les niveaux d'expression de milliers de gènes, dans des conditions différentes et au fil du temps. Une fois que les données d'expression génétique ont été recueillies, l'étape suivante consiste à analyser et à extraire des informations biologiques utiles. L'un des algorithmes les plus populaires traitant de l'analyse des données d'expression génétique est le groupement, qui consiste à diviser un certain ensemble en groupes, où les composants de chaque groupe sont semblables les uns aux autres données. Dans le cas des ensembles de données d'expression génique, chaque gène est représenté par ses valeurs d'expression (caractéristiques), à des points distincts dans le temps, dans les conditions contrôlées. Le processus de regroupement des gènes est à la base des études génomiques qui visent à analyser les fonctions des gènes car il est supposé que les gènes qui sont similaires dans leurs niveaux d'expression sont également relativement similaires en termes de fonction biologique. Le problème que nous abordons dans le sens de la recherche de classification non supervisée dynamique est le regroupement dynamique des données d'expression génique. Dans notre cas, la dynamique à long terme indique que l'ensemble de données ne sont pas statiques, mais elle est sujette à changement. Pourtant, par opposition aux approches progressives de la littérature, où l'ensemble de données est enrichie avec de nouveaux gènes (instances) au cours du processus de regroupement, nos approches abordent les cas lorsque de nouvelles fonctionnalités (niveaux d'expression pour de nouveaux points dans le temps) sont ajoutés à la gènes déjà existants dans l'ensemble de données. À notre connaissance, il n'y a pas d'approches dans la littérature qui traitent le problème de la classification dynamique des données d'expression génétique, définis comme ci-dessus. Dans ce contexte, nous avons introduit trois algorithmes de groupement dynamiques que sont capables de gérer de nouveaux niveaux d'expression génique collectés, en partant d'une partition obtenue précédente, sans la nécessité de ré-exécuter l'algorithme à partir de zéro. L'évaluation expérimentale montre que notre méthode est plus rapide et plus précis que l'application de l'algorithme de classification à partir de zéro sur la fonctionnalité étendue ensemble de donnéesThe research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set
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Salazar, González Fernando. "A machine learning based methodology for anomaly detection in dam behaviour". Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/405808.
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Dam behaviour is difficult to predict with high accuracy. Numerical models for structural calculation solve the equations of continuum mechanics, but are subject to considerable uncertainty as to the characterisation of materials, especially with regard to the foundation. As a result, these models are often incapable to calculate dam behaviour with sufficient precision. Thus, it is difficult to determine whether a given deviation between model results and monitoring data represent a relevant anomaly or incipient failure.
By contrast, there is a tendency towards automatising dam monitoring devices, which allows for increasing the reading frequency and results in a greater amount and variety of data available, such as displacements, leakage, or interstitial pressure, among others.
This increasing volume of dam monitoring data makes it interesting to study the ability of advanced tools to extract useful information from observed variables.
In particular, in the field of Machine Learning (ML), powerful algorithms have been developed to face problems where the amount of data is much larger or the underlying phenomena is much less understood.
In this thesis, the possibilities of machine learning techniques were analysed for application to dam structural analysis based on monitoring data. The typical characteristics of the data sets available in dam safety were taking into account, as regards their nature, quality and size.
A critical literature review was performed, from which the key issues to consider for implementation of these algorithms in dam safety were identified. A comparative study of the accuracy of a set of algorithms for predicting dam behaviour was carried out, considering radial and tangential displacements and leakage flow in a 100-m high dam. The results suggested that the algorithm called ``Boosted Regression Trees'' (BRT) is the most suitable, being more accurate in general, while flexible and relatively easy to implement.
At a later stage, the possibilities of interpretation of the mentioned algorithm were evaluated, to identify the shape and intensity of the association between external variables and the dam response, as well as the effect of time. The tools were applied to the same test case, and allowed more accurate identification of the time effect than the traditional statistical method.
Finally, a methodology for the implementation of predictive models based on BRT for early detection of anomalies was developed and implemented in an interactive tool that provides information on dam behaviour, through a set of selected devices. It allows the user to easily verify whether the actual data for each of these devices are within a pre-defined normal operation interval.El comportamiento estructural de las presas de embalse es difícil de predecir con precisión. Los modelos numéricos para el cálculo estructural resuelven las ecuaciones de la mecánica de medios continuos, pero están sujetos a una gran incertidumbre en cuanto a la caracterización de los materiales, especialmente en lo que respecta a la cimentación. Como consecuencia, frecuentemente estos modelos no son capaces de calcular el comportamiento de las presas con suficiente precisión. Así, es difícil discernir si un estado que se aleja en cierta medida de la normalidad supone o no una situación de riesgo estructural. Por el contrario, muchas de las presas en operación cuentan con un gran número de aparatos de auscultación, que registran la evolución de diversos indicadores como los movimientos, el caudal de filtración, o la presión intersticial, entre otros. Aunque hoy en día hay muchas presas con pocos datos observados, hay una tendencia clara hacia la instalación de un mayor número de aparatos que registran el comportamiento con mayor frecuencia. Como consecuencia, se tiende a disponer de un volumen creciente de datos que reflejan el comportamiento de la presa, lo cual hace interesante estudiar la capacidad de herramientas desarrolladas en otros campos para extraer información útil a partir de variables observadas. En particular, en el ámbito del Aprendizaje Automático (Machine Learning), se han desarrollado algoritmos muy potentes para entender fenómenos cuyo mecanismo es poco conocido, acerca de los cuales se dispone de grandes volúmenes de datos. En la tesis se ha hecho un análisis de las posibilidades de las técnicas más recientes de aprendizaje automático para su aplicación al análisis estructural de presas basado en los datos de auscultación. Para ello se han tenido en cuenta las características habituales de las series de datos disponibles en las presas, en cuanto a su naturaleza, calidad y cantidad. Se ha realizado una revisión crítica de la bibliografía existente, a partir de la cual se han identificado los aspectos clave a tener en cuenta para implementación de estos algoritmos en la seguridad de presas. Se ha realizado un estudio comparativo de la precisión de un conjunto de algoritmos para la predicción del comportamiento de presas considerando desplazamientos radiales, tangenciales y filtraciones. Para ello se han utilizado datos reales de una presa bóveda. Los resultados sugieren que el algoritmo denominado ``Boosted Regression Trees'' (BRTs) es el más adecuado, por ser más preciso en general, además de flexible y relativamente fácil de implementar. En una etapa posterior, se han identificado las posibilidades de interpretación del citado algoritmo para extraer la forma e intensidad de la asociación entre las variables exteriores y la respuesta de la presa, así como el efecto del tiempo. Las herramientas empleadas se han aplicado al mismo caso piloto, y han permitido identificar el efecto del tiempo con más precisión que el método estadístico tradicional. Finalmente, se ha desarrollado una metodología para la aplicación de modelos de predicción basados en BRTs en la detección de anomalías en tiempo real. Esta metodología se ha implementado en una herramienta informática interactiva que ofrece información sobre el comportamiento de la presa, a través de un conjunto de aparatos seleccionados. Permite comprobar a simple vista si los datos reales de cada uno de estos aparatos se encuentran dentro del rango de funcionamiento normal de la presa.
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Winikoff, Steven M. "Incorporating the simplicity first methodology into a machine learning genetic algorithm". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39118.pdf.
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Brun, Yuriy 1981. "Software fault identification via dynamic analysis and machine learning". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 65-67).
I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
by Yuriy Brun.
M.Eng.
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Emani, Murali Krishna. "Adaptive parallelism mapping in dynamic environments using machine learning". Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10469.
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Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mainstream parallel applications execute in the same system competing for resources. This resource contention may lead to a drastic degradation in a program’s performance. In addition, the execution environment composed of workloads and hardware resources, is dynamic and unpredictable. Efficient matching of program parallelism to machine parallelism under uncertainty is hard. The mapping policies that determine the optimal allocation of work to threads should anticipate these variations. This thesis proposes solutions to the mapping of parallel programs in dynamic environments. It employs predictive modelling techniques to determine the best degree of parallelism. Firstly, this thesis proposes a machine learning-based model to determine the optimal thread number for a target program co-executing with varying workloads. For this purpose, this offline trained model uses static code features and dynamic runtime information as input. Next, this thesis proposes a novel solution to monitor the proposed offline model and adjust its decisions in response to the environment changes. It develops a second predictive model for determining how the future environment should be, if the current thread prediction was optimal. Depending on how close this prediction was to the actual environment, the predicted thread numbers are adjusted. Furthermore, considering the multitude of potential execution scenarios where no single policy is best suited in all cases, this work proposes an approach based on the idea of mixture of experts. It considers a number of offline experts or mapping policies, each specialized for a given scenario, and learns online the best expert that is optimal for the current execution. When evaluated on highly dynamic executions, these solutions are proven to surpass default, state-of-art adaptive and analytic approaches.
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Dahlberg, Love. "Dynamic algorithm selection for machine learning on time series". Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72576.
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We present a software that can dynamically determine what machine learning algorithm is best to use in a certain situation given predefined traits. The produced software uses ideal conditions to exemplify how such a solution could function. The software is designed to train a selection algorithm that can predict the behavior of the specified testing algorithms to derive which among them is the best. The software is used to summarize and evaluate a collection of selection algorithm predictions to determine which testing algorithm was the best during that entire period. The goal of this project is to provide a prediction evaluation software solution can lead towards a realistic implementation.
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Ehramikar, Soheila. "The enhancement of credit card fraud detection systems using machine learning methodology". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0023/MQ50338.pdf.
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Botlani-Esfahani, Mohsen. "Modeling of Dynamic Allostery in Proteins Enabled by Machine Learning". Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6804.
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Regulation of protein activity is essential for normal cell functionality. Many proteins are regulated allosterically, that is, with spatial gaps between stimulation and active sites. Biological stimuli that regulate proteins allosterically include, for example, ions and small molecules, post-translational modifications, and intensive state-variables like temperature and pH. These effectors can not only switch activities on-and-off, but also fine-tune activities. Understanding the underpinnings of allostery, that is, how signals are propagated between distant sites, and how transmitted signals manifest themselves into regulation of protein activity, has been one of the central foci of biology for over 50 years. Today, the importance of such studies goes beyond basic pedagogical interests as bioengineers seek design features to control protein function for myriad purposes, including design of nano-biosensors, drug delivery vehicles, synthetic cells and organic-synthetic interfaces. The current phenomenological view of allostery is that signaling and activity control occur via effector-induced changes in protein conformational ensembles. If the structures of two states of a protein differ from each other significantly, then thermal fluctuations can be neglected and an atomically detailed model of regulation can be constructed in terms of how their minimum-energy structures differ between states. However, when the minimum-energy structures of states differ from each other only marginally and the difference is comparable to thermal fluctuations, then a mechanistic model cannot be constructed solely on the basis of differences in protein structure. Understanding the mechanism of dynamic allostery requires not only assessment of high-dimensional conformational ensembles of the various individual states, including inactive, transition and active states, but also relationships between them. This challenge faces many diverse protein families, including G-protein coupled receptors, immune cell receptors, heat shock proteins, nuclear transcription factors and viral attachment proteins, whose mechanisms, despite numerous studies, remain poorly understood. This dissertation deals with the development of new methods that significantly boost the applicability of molecular simulation techniques to probe dynamic allostery in these proteins. Specifically, it deals with two different methods, one to obtain quantitative estimates for subtle differences between conformational ensembles, and the other to relate conformational ensemble differences to allosteric signal communication. Both methods are enabled by a new application of the mathematical framework of machine learning. These methods are applied to (a) identify specific effects of employed force fields on conformational ensembles, (b) compare multiple ensembles against each other for determination of common signaling pathways induced by different effectors, (c) identify the effects of point mutations on conformational ensemble shifts in proteins, and (d) understand the mechanism of dynamic allostery in a PDZ domain. These diverse applications essentially demonstrate the generality of the developed approaches, and specifically set the foundation for future studies on PDZ domains and viral attachment proteins.
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Zorello, Ligia Maria Moreira. "Dynamic CPU frequency scaling using machine learning for NFV applications". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-30012019-100044/.
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Growth in the Information and Communication Technology sector is increasing the need to improve the quality of service and energy efficiency, as this industry has already surpassed 12% of global energy consumption in 2017. Data centers correspond to a large part of this consumption, accounting for about 15% of energy expenditure on the Information and Communication Technology domain; moreover, the subsystem that generates the most costs for data center operators is that of servers and storage. Many solutions have been proposed to reduce server consumption, such as the use of dynamic voltage and frequency scaling, a technology that enables the adaptation of energy consumption to the workload by modifying the operating voltage and frequency, although they are not optimized for network traffic. In this thesis, a control method was developed using a prediction engine based on the analysis of the ongoing traffic. Machine learning algorithms based on Neural Networks and Support Vector Machines have been used, and it was verified that it is possible to reduce power consumption by up to 12% on servers with Intel Sandy Bridge processor and up to 21 % in servers with Intel Haswell processor when compared to the maximum frequency, which is currently the most used solution in the industry.O crescimento do setor de Tecnologia da Informação e Comunicação está aumentando a necessidade de melhorar a qualidade de serviço e a eficiência energética, pois o setor já ultrapassou a marca de 12% do consumo energético global em 2017. Data centers correspondem a grande parte desse consumo, representando cerca de 15% dos gastos com energia do setor Tecnologia Informação e Comunicação; além disso, o subsistema que gera mais custos para operadores de data centers é o de servidores e armazenamento. Muitas soluções foram propostas a fim de reduzir o consumo de energia com servidores, como o uso de escalonamento dinâmico de tensão e frequência, uma tecnologia que permite adaptar o consumo de energia à carga de trabalho, embora atualmente não sejam otimizadas para o processamento do tráfego de rede. Nessa dissertação, foi desenvolvido um método de controle usando um mecanismo de previsão baseado na análise do tráfego que chega aos servidores. Os algoritmos de aprendizado de máquina baseados em Redes Neurais e em Máquinas de Vetores de Suporte foram utilizados, e foi verificado que é possível reduzir o consumo de energia em até 12% em servidores com processador Intel Sandy Bridge e em até 21% em servidores com processador Intel Haswell quando comparado com a frequência máxima, que é atualmente a solução mais utilizada na indústria.
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Caceres, Carlos Antonio. "Machine Learning Techniques for Gesture Recognition". Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.
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Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications.
In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field.
The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.Master of Science
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Tian, Renran. "Validity and reliability of dynamic virtual interactive design methodology". Master's thesis, Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-09242007-080500.
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Yang, Donghai, i 杨东海. "Dynamic planning and scheduling in manufacturing systems with machine learning approaches". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41757968.
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Arslan, Oktay. "Machine learning and dynamic programming algorithms for motion planning and control". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54317.
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Robot motion planning is one of the central problems in robotics, and has received considerable amount of attention not only from roboticists but also from the control and artificial intelligence (AI) communities. Despite the different types of applications and physical properties of robotic systems, many high-level tasks of autonomous systems can be decomposed into subtasks which require point-to-point navigation while avoiding infeasible regions due to the obstacles in the workspace. This dissertation aims at developing a new class of sampling-based motion planning algorithms that are fast, efficient and asymptotically optimal by employing ideas from Machine Learning (ML) and Dynamic Programming (DP). First, we interpret the robot motion planning problem as a form of a machine learning problem since the underlying search space is not known a priori, and utilize random geometric graphs to compute consistent discretizations of the underlying continuous search space. Then, we integrate existing DP algorithms and ML algorithms to the framework of sampling-based algorithms for better exploitation and exploration, respectively. We introduce a novel sampling-based algorithm, called RRT#, that improves upon the well-known RRT* algorithm by leveraging value and policy iteration methods as new information is collected. The proposed algorithms yield provable guarantees on correctness, completeness and asymptotic optimality. We also develop an adaptive sampling strategy by considering exploration as a classification (or regression) problem, and use online machine learning algorithms to learn the relevant region of a query, i.e., the region that contains the optimal solution, without significant computational overhead. We then extend the application of sampling-based algorithms to a class of stochastic optimal control problems and problems with differential constraints. Specifically, we introduce the Path Integral - RRT algorithm, for solving optimal control of stochastic systems and the CL-RRT# algorithm that uses closed-loop prediction for trajectory generation for differential systems. One of the key benefits of CL-RRT# is that for many systems, given a low-level tracking controller, it is easier to handle differential constraints, so complex steering procedures are not needed, unlike most existing kinodynamic sampling-based algorithms. Implementation results of sampling-based planners for route planning of a full-scale autonomous helicopter under the Autonomous Aerial Cargo/Utility System Program (AACUS) program are provided.
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Xu, Jin. "Machine Learning – Based Dynamic Response Prediction of High – Speed Railway Bridges". Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278538.
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Targeting heavier freights and transporting passengers with higher speeds became the strategic railway development during the past decades significantly increasing interests on railway networks. Among different components of a railway network, bridges constitute a major portion imposing considerable construction and maintenance costs. On the other hand, heavier axle loads and higher trains speeds may cause resonance occurrence on bridges; which consequently limits operational train speed and lines. Therefore, satisfaction of new expectations requires conducting a large number of dynamic assessments/analyses on bridges, especially on existing ones. Evidently, such assessments need detailed information, expert engineers and consuming considerable computational costs. In order to save the computational efforts and decreasing required amount of expertise in preliminary evaluation of dynamic responses, predictive models using artificial neural network (ANN) are proposed in this study. In this regard, a previously developed closed-form solution method (based on solving a series of moving force) was adopted to calculate the dynamic responses (maximum deck deflection and maximum vertical deck acceleration) of randomly generated bridges. Basic variables in generation of random bridges were extracted both from literature and geometrical properties of existing bridges in Sweden. Different ANN architectures including number of inputs and neurons were considered to train the most accurate and computationally cost-effective mode. Then, the most efficient model was selected by comparing their performance using absolute error (ERR), Root Mean Square Error (RMSE) and coefficient of determination (R2). The obtained results revealed that the ANN model can acceptably predict the dynamic responses. The proposed model presents Err of about 11.1% and 9.9% for prediction of maximum acceleration and maximum deflection, respectively. Furthermore, its R2 for maximum acceleration and maximum deflection predictions equal to 0.982 and 0.998, respectively. And its RMSE is 0.309 and 1.51E-04 for predicting the maximum acceleration and maximum deflection prediction, respectively. Finally, sensitivity analyses were conducted to evaluate the importance of each input variable on the outcomes. It was noted that the span length of the bridge and speed of the train are the most influential parameters.
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Gyawali, Sanij. "Dynamic Load Modeling from PSSE-Simulated Disturbance Data using Machine Learning". Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/100591.
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Load models have evolved from simple ZIP model to composite model that incorporates
the transient dynamics of motor loads. This research utilizes the latest trend on Machine
Learning and builds reliable and accurate composite load model. A composite load model
is a combination of static (ZIP) model paralleled with a dynamic model. The dynamic
model, recommended by Western Electricity Coordinating Council (WECC), is an induction
motor representation. In this research, a dual cage induction motor with 20 parameters
pertaining to its dynamic behavior, starting behavior, and per unit calculations is used as
a dynamic model. For machine learning algorithms, a large amount of data is required.
The required PMU field data and the corresponding system models are considered Critical
Energy Infrastructure Information (CEII) and its access is limited. The next best option for
the required amount of data is from a simulating environment like PSSE. The IEEE 118 bus
system is used as a test setup in PSSE and dynamic simulations generate the required data
samples. Each of the samples contains data on Bus Voltage, Bus Current, and Bus Frequency
with corresponding induction motor parameters as target variables. It was determined that
the Artificial Neural Network (ANN) with multivariate input to single parameter output
approach worked best. Recurrent Neural Network (RNN) is also experimented side by side
to see if an additional set of information of timestamps would help the model prediction.
Moreover, a different definition of a dynamic model with a transfer function-based load
is also studied. Here, the dynamic model is defined as a mathematical representation of
the relation between bus voltage, bus frequency, and active/reactive power flowing in the
bus. With this form of load representation, Long-Short Term Memory (LSTM), a variation
of RNN, performed better than the concurrent algorithms like Support Vector Regression
(SVR). The result of this study is a load model consisting of parameters defining the load
at load bus whose predictions are compared against simulated parameters to examine their
validity for use in contingency analysis.Master of Science
Independent system Operators (ISO) and Distribution system operators (DSO) have a responsibility to provide uninterrupted power supply to consumers. That along with the longing to keep operating cost minimum, engineers and planners study the system beforehand and seek to find the optimum capacity for each of the power system elements like generators, transformers, transmission lines, etc. Then they test the overall system using power system models, which are mathematical representation of the real components, to verify the stability and strength of the system. However, the verification is only as good as the system models that are used. As most of the power systems components are controlled by the operators themselves, it is easy to develop a model from their perspective. The load is the only component controlled by consumers. Hence, the necessity of better load models. Several studies have been made on static load modeling and the performance is on par with real behavior. But dynamic loading, which is a load behavior dependent on time, is rather difficult to model. Some attempts on dynamic load modeling can be found already. Physical component-based and mathematical transfer function based dynamic models are quite widely used for the study. These load structures are largely accepted as a good representation of the systems dynamic behavior. With a load structure in hand, the next task is estimating their parameters. In this research, we tested out some new machine learning methods to accurately estimate the parameters. Thousands of simulated data are used to train machine learning models. After training, we validated the models on some other unseen data. This study finally goes on to recommend better methods to load modeling.
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Yang, Donghai. "Dynamic planning and scheduling in manufacturing systems with machine learning approaches". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41757968.
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Renner, Michael Robert. "Machine Learning Simulation: Torso Dynamics of Robotic Biped". Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/34602.
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Military, Medical, Exploratory, and Commercial robots have much to gain from exchanging wheels for legs. However, the equations of motion of dynamic bipedal walker models are highly coupled and non-linear, making the selection of an appropriate control scheme difficult. A temporal difference reinforcement learning method known as Q-learning develops complex control policies through environmental exploration and exploitation. As a proof of concept, Q-learning was applied through simulation to a benchmark single pendulum swing-up/balance task; the value function was first approximated with a look-up table, and then an artificial neural network. We then applied Evolutionary Function Approximation for Reinforcement Learning to effectively control the swing-leg and torso of a 3 degree of freedom active dynamic bipedal walker in simulation. The model began each episode in a stationary vertical configuration. At each time-step the learning agent was rewarded for horizontal hip displacement scaled by torso altitude--which promoted faster walking while maintaining an upright posture--and one of six coupled torque activations were applied through two first-order filters. Over the course of 23 generations, an approximation of the value function was evolved which enabled walking at an average speed of 0.36 m/s. The agent oscillated the torso forward then backward at each step, driving the walker forward for forty-two steps in thirty seconds without falling over. This work represents the foundation for improvements in anthropomorphic bipedal robots, exoskeleton mechanisms to assist in walking, and smart prosthetics.Master of Science
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Jackson, John Taylor. "Improving Swarm Performance by Applying Machine Learning to a New Dynamic Survey". DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1857.
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A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is more indicative of a user’s knowledge on the subject than the previous survey. This study also identifies better metrics for predicting each user’s performance when predicting Major League Baseball game outcomes throughout a given week. This study demonstrates that the new machine learning models and data extraction schemes are approximately 12% more accurate than the currently implemented methods at predicting user performance. Finally, this study shows how predicting a user’s performance based purely on their inputs can increase the average performance of a group by limiting the group to the top predicted performers. This study shows that by limiting the group to the top predicted performers across five different weeks of MLB predictions, the average group performance was increased up to 5.5%, making this a superior method.
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Hammami, Seif Eddine. "Dynamic network resources optimization based on machine learning and cellular data mining". Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0015/document.
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Les traces réelles de réseaux cellulaires représentent une mine d’information utile pour améliorer les performances des réseaux. Des traces comme les CDRs (Call detail records) contiennent des informations horodatées sur toutes les interactions des utilisateurs avec le réseau sont exploitées dans cette thèse. Nous avons proposé des nouvelles approches dans l’étude et l’analyse des problématiques des réseaux de télécommunications, qui sont basé sur les traces réelles et des algorithmes d’apprentissage automatique. En effet, un outil global d’analyse de données, pour la classification automatique des stations de base, la prédiction de la charge de réseau et la gestion de la bande passante est proposé ainsi qu’un outil pour la détection automatique des anomalies de réseau. Ces outils ont été validés par des applications directes, et en utilisant différentes topologies de réseaux comme les réseaux WMN et les réseaux basés sur les drone-cells. Nous avons montré ainsi, qu’en utilisant des outils d’analyse de données avancés, il est possible d’optimiser dynamiquement les réseaux mobiles et améliorer la gestion de la bande passanteReal datasets of mobile network traces contain valuable information about the network resources usage. These traces may be used to enhance and optimize the network performances. A real dataset of CDR (Call Detail Records) traces, that include spatio-temporal information about mobile users’ activities, are analyzed and exploited in this thesis. Given their large size and the fact that these are real-world datasets, information extracted from these datasets have intensively been used in our work to develop new algorithms that aim to revolutionize the infrastructure management mechanisms and optimize the usage of resource. We propose, in this thesis, a framework for network profiles classification, load prediction and dynamic network planning based on machine learning tools. We also propose a framework for network anomaly detection. These frameworks are validated using different network topologies such as wireless mesh networks (WMN) and drone-cell based networks. We show that using advanced data mining techniques, our frameworks are able to help network operators to manage and optimize dynamically their networks
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Sathyan, Anoop. "Intelligent Machine Learning Approaches for Aerospace Applications". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1491558309625214.
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Dalgren, Anton, i Ylva Lundegård. "GreenML : A methodology for fair evaluation of machine learning algorithms with respect to resource consumption". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159837.
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Impressive results can be achieved when stacking deep neural networks hierarchies together. Several machine learning papers claim state-of-the-art results when evaluating their models with different accuracy metrics. However, these models come at a cost, which is rarely taken into consideration. This thesis aims to shed light on the resource consumption of machine learning algorithms, and therefore, five efficiency metrics are proposed. These should be used for evaluating machine learning models, taking accuracy, model size, and time and energy consumption for both training and inference into account. These metrics are intended to allow for a fairer evaluation of machine learning models, not only looking at accuracy. This thesis presents an example of how these metrics can be used by applying them to both text and image classification tasks using the algorithms SVM, MLP, and CNN.
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Magnedal, Holmgren Andreas, i Victor Sellstedt. "Risk Free Credit: Estimating Risk of Debt Delinquency on Credit Cards : Using Machine Learning Methodology". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259747.
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A well functioning economy requires a stable credit market. Computational intelligence methods could provide a method to reduce the amount of uncertainty in the markets. This report examines four different methods for predicting the probability for defaults of credit card clients in Taiwan. The four selected methods were Linear Discriminant Analysis, Support Vector Machines, Artificial Neural Networks and Deep Neural Networks. The models were then evaluated with regards to five different methods: Area Under the Curve for the Receiver Operating Characteristic, accuracy, precision, sensitivity and specificity. The results showed that all models performed better than random with similar results, except for the Support Vector Machine which in our testing configuration incorrectly classified almost all debtors that defaulted on their debt. Although there was no clearly superior model the results showed that the Deep Neural Networks and Linear Discriminant Analysis were the two most promising methods.En välfungerande ekonomi behöver en stabil kreditmarknad. Maskininlärningsmetoder har potential att reducera osäkerheten på marknaden. Rapporten undersöker fyra olika metoder för att beräkna sannolikheten att en låntagare återbetalar sin kreditkortsskuld baserat på kreditkortsdata från Taiwan. Metoderna som valdes var Linear Discriminant Analysis, Support Vector Machines, Arti- ficiella Neurala Nätverk och Djupa Neurala Nätverk. Modellerna utvärderades med avseende på fem olika metoder: Area Under the Curve for the Receiver Operating Characteristic, nogrannhet, precision, sensitivitet och specificitet. Resultaten visade att alla modeller presterade bättre än slump med liknande resultat utom för Support Vector Machines som i vår testkonfiguration felaktigt klassificerade nästintill alla låntagare som inte skulle återbetala. Även om ingen modell var tydligt bättre än de andra visade resultaten att Djupa Neurala Nätverk och Linear Discriminant Analysis är metoderna som visar mest potential.
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Lee, Michael. "Rapid Prediction of Tsunamis and Storm Surges Using Machine Learning". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103154.
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Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities.Doctor of Philosophy
Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.
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Madjar, Nicole, i Filip Lindblom. "Machine Learning implementation for Stress-Detection". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280897.
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This project is about trying to apply machine learning theories on a selection of data points in order to see if an improvement of current methodology within stress detection and measure selecting could be applicable for the company Linkura AB. Linkura AB is a medical technology company based in Linköping and handles among other things stress measuring for different companies employees, as well as health coaching for selecting measures. In this report we experiment with different methods and algorithms under the collective name of Unsupervised Learning, to identify visible patterns and behaviour of data points and further on we analyze it with the quantity of data received. The methods that have been practiced on during the project are “K-means algorithm” and a dynamic hierarchical clustering algorithm. The correlation between the different data points parameters is analyzed to optimize the resource consumption, also experiments with different number of parameters are tested and discussed with an expert in stress coaching. The results stated that both algorithms can create clusters for the risk groups, however, the dynamic clustering method clearly demonstrate the optimal number of clusters that should be used. Having consulted with mentors and health coaches regarding the analysis of the produced clusters, a conclusion that the dynamic hierarchical cluster algorithm gives more accurate clusters to represent risk groups were done. The conclusion of this project is that the machine learning algorithms that have been used, can categorize data points with stress behavioral correlations, which is usable in measure testimonials. Further research should be done with a greater set of data for a more optimal result, where this project can form the basis for the implementations.Detta projekt handlar om att försöka applicera maskininlärningsmodeller på ett urval av datapunkter för att ta reda på huruvida en förbättring av nuvarande praxis inom stressdetektering och åtgärdshantering kan vara applicerbart för företaget Linkura AB. Linkura AB är ett medicintekniskt företag baserat i Linköping och hanterar bland annat stressmätning hos andra företags anställda, samt hälso-coachning för att ta fram åtgärdspunkter för förbättring. I denna rapport experimenterar vi med olika metoder under samlingsnamnet oövervakad maskininlärning för att identifiera synbara mönster och beteenden inom datapunkter, och vidare analyseras detta i förhållande till den mängden data vi fått tillgodosett. De modeller som har använts under projektets gång har varit “K-Means algoritm” samt en dynamisk hierarkisk klustermodell. Korrelationen mellan olika datapunktsparametrar analyseras för att optimera resurshantering, samt experimentering med olika antal parametrar inkluderade i datan testas och diskuteras med expertis inom hälso-coachning. Resultaten påvisade att båda algoritmerna kan generera kluster för riskgrupper, men där den dynamiska modellen tydligt påvisar antalet kluster som ska användas för optimalt resultat. Efter konsultering med mentorer samt expertis inom hälso-coachning så drogs en slutsats om att den dynamiska modellen levererar tydligare riskkluster för att representera riskgrupper för stress. Slutsatsen för projektet blev att maskininlärningsmodeller kan kategorisera datapunkter med stressrelaterade korrelationer, vilket är användbart för åtgärdsbestämmelser. Framtida arbeten bör göras med ett större mängd data för mer optimerade resultat, där detta projekt kan ses som en grund för dessa implementeringar.
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Wenerstrom, Brent K. "Temporal Data Mining in a Dynamic Feature Space". BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/761.
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Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.
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Curtis, Brian J. "Machine Learning and Cellular Automata| Applications in Modeling Dynamic Change in Urban Environments". Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10785215.
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There have been several studies advocating the need for, and the feasibility of, using advanced techniques to support decision makers in urban planning and resource monitoring. One such advanced technique includes a framework that leverages remote sensing and geospatial information systems (GIS) in conjunction with cellular automata (CA) to monitor land use / land change phenomena like urban sprawling. Much research has been conducted using various learning techniques spanning all levels of complexity - from simple logistical regression to advance artificial intelligence methods (e.g., artificial neural networks). In a high percentage of the published research, simulations are performed leveraging only one or two techniques and applied to a case study of a single geographical region. Typically, the findings are favorable and demonstrate the studied methods are superior. This work found no research being conducted to compare the performance of several machine learning techniques across an array of geographical locations. Additionally, current literature was found lacking in investigating the impact various scene parameters (e.g., sprawl, urban growth) had on the simulation results. Therefore, this research set out to understand the sensitivities and correlations associated with the selection of machine learning methods used in CA based models. The results from this research indicate more simplistic algorithms, which are easier to comprehend and implement, have the potential to perform equally as well as compared to more complicated algorithms. Also, it is shown that the quantity of urbanization in the studied area directly impacts the simulation results.
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Tahkola, M. (Mikko). "Developing dynamic machine learning surrogate models of physics-based industrial process simulation models". Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906042313.
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Abstract. Dynamic physics-based models of industrial processes can be computationally heavy which prevents using them in some applications, e.g. in process operator training. Suitability of machine learning in creating surrogate models of a physics-based unit operation models was studied in this research. The main motivation for this was to find out if machine learning model can be accurate enough to replace the corresponding physics-based components in dynamic modelling and simulation software Apros® which is developed by VTT Technical Research Centre of Finland Ltd and Fortum. This study is part of COCOP project, which receive funding from EU, and INTENS project that is Business Finland funded.
The research work was divided into a literature study and an experimental part. In the literature study, the steps of modelling with data-driven methods were studied and artificial neural network architectures suitable for dynamic modelling were investigated. Based on that, four neural network architectures were chosen for the case studies. In the first case study, linear and nonlinear autoregressive models with exogenous inputs (ARX and NARX respectively) were used in modelling dynamic behaviour of a water tank process build in Apros®. In the second case study, also Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were considered and compared with the previously mentioned ARX and NARX models. The workflow from selecting the input and output variables for the machine learning model and generating the datasets in Apros® to implement the machine learning models back to Apros® was defined. Keras is an open source neural network library running on Python that was utilised in the model generation framework which was developed as a part of this study. Keras library is a very popular library that allow fast experimenting. The framework make use of random hyperparameter search and each model is tested on a validation dataset in dynamic manner, i.e. in multi-step-ahead configuration, during the optimisation. The best models based in terms of average normalised root mean squared error (NRMSE) is selected for further testing.
The results of the case studies show that accurate multi-step-ahead models can be built using recurrent artificial neural networks. In the first case study, the linear ARX model achieved slightly better NRMSE value than the nonlinear one, but the accuracy of both models was on a very good level with the average NRMSE being lower than 0.1 %. The generalisation ability of the models was tested using multiple datasets and the models proved to generalise well. In the second case study, there were more difference between the models’ accuracies. This was an expected result as the studied process contains nonlinearities and thus the linear ARX model performed worse in predicting some output variables than the nonlinear ones. On the other hand, ARX model performed better with some other output variables. However, also in the second case study the model NRMSE values were on good level, being 1.94–3.60 % on testing dataset.
Although the workflow to implement machine learning models in Apros® using its Python binding was defined, the actual implementation need more work. Experimenting with Keras neural network models in Apros® was noticed to slow down the simulation even though the model was fast when testing it outside of Apros®. The Python binding in Apros® do not seem to cause overhead to the calculation process which is why further investigating is needed. It is obvious that the machine learning model must be very accurate if it is to be implemented in Apros® because it needs to be able interact with the physics-based model. The actual accuracy requirement that Apros® sets should be also studied to know if and in which direction the framework made for this study needs to be developed.Dynaamisten surrogaattimallien kehittäminen koneoppimismenetelmillä teollisuusprosessien fysiikkapohjaisista simulaatiomalleista. Tiivistelmä. Teollisuusprosessien toimintaa jäljittelevät dynaamiset fysiikkapohjaiset simulaatiomallit voivat laajuudesta tai yksityiskohtien määrästä johtuen olla laskennallisesti raskaita. Tämä voi rajoittaa simulaatiomallin käyttöä esimerkiksi prosessioperaattorien koulutuksessa ja hidastaa simulaattorin avulla tehtävää prosessien optimointia. Tässä tutkimuksessa selvitettiin koneoppimismenetelmillä luotujen mallien soveltuvuutta fysiikkapohjaisten yksikköoperaatiomallien surrogaattimallinnukseen. Fysiikkapohjaiset mallit on luotu teollisuusprosessien dynaamiseen mallinnukseen ja simulointiin kehitetyllä Apros®-ohjelmistolla, jota kehittää Teknologian tutkimuskeskus VTT Oy ja Fortum. Työ on osa COCOP-projektia, joka saa rahoitusta EU:lta, ja INTENS-projektia, jota rahoittaa Business Finland.
Työ on jaettu kirjallisuusselvitykseen ja kahteen kokeelliseen case-tutkimukseen. Kirjallisuusosiossa selvitettiin datapohjaisen mallinnuksen eri vaiheet ja tutkittiin dynaamiseen mallinnukseen soveltuvia neuroverkkorakenteita. Tämän perusteella valittiin neljä neuroverkkoarkkitehtuuria case-tutkimuksiin. Ensimmäisessä case-tutkimuksessa selvitettiin lineaarisen ja epälineaarisen autoregressive model with exogenous inputs (ARX ja NARX) -mallin soveltuvuutta pinnankorkeuden säädöllä varustetun vesisäiliömallin dynaamisen käyttäytymisen mallintamiseen. Toisessa case-tutkimuksessa tarkasteltiin edellä mainittujen mallityyppien lisäksi Long Short-Term Memory (LSTM) ja Gated Recurrent Unit (GRU) -verkkojen soveltuvuutta power-to-gas prosessin metanointireaktorin dynaamiseen mallinnukseen. Työssä selvitettiin surrogaattimallinnuksen vaiheet korvattavien yksikköoperaatiomallien ja siihen liittyvien muuttujien valinnasta datan generointiin ja koneoppimismallien implementointiin Aprosiin. Koneoppimismallien rakentamiseen tehtiin osana työtä Python-sovellus, joka hyödyntää Keras Python-kirjastoa neuroverkkomallien rakennuksessa. Keras on suosittu kirjasto, joka mahdollistaa nopean neuroverkkomallien kehitysprosessin. Työssä tehty sovellus hyödyntää neuroverkkomallien hyperparametrien optimoinnissa satunnaista hakua. Jokaisen optimoinnin aikana luodun mallin tarkkuutta dynaamisessa simuloinnissa mitataan erillistä aineistoa käyttäen. Jokaisen mallityypin paras malli valitaan NRMSE-arvon perusteella seuraaviin testeihin.
Case-tutkimuksen tuloksien perusteella neuroverkoilla voidaan saavuttaa korkea tarkkuus dynaamisessa simuloinnissa. Ensimmäisessä case-tutkimuksessa lineaarinen ARX-malli oli hieman epälineaarista tarkempi, mutta molempien mallityyppien tarkkuus oli hyvä (NRMSE alle 0.1 %). Mallien yleistyskykyä mitattiin simuloimalla usealla aineistolla, joiden perusteella yleistyskyky oli hyvällä tasolla. Toisessa case-tutkimuksessa vastemuuttujien tarkkuuden välillä oli eroja lineaarisen ja epälineaaristen mallityyppien välillä. Tämä oli odotettu tulos, sillä joidenkin mallinnettujen vastemuuttujien käyttäytyminen on epälineaarista ja näin ollen lineaarinen ARX-malli suoriutui niiden mallintamisesta epälineaarisia malleja huonommin. Toisaalta lineaarinen ARX-malli oli tarkempi joidenkin vastemuuttujien mallinnuksessa. Kaiken kaikkiaan mallinnus onnistui hyvin myös toisessa case-tutkimuksessa, koska käytetyillä mallityypeillä saavutettiin 1.94–3.60 % NRMSE-arvo testidatalla simuloitaessa.
Koneoppimismallit saatiin sisällytettyä Apros-malliin käyttäen Python-ominaisuutta, mutta prosessi vaatii lisäselvitystä, jotta mallit saadaan toimimaan yhdessä. Testien perusteella Keras-neuroverkkojen käyttäminen näytti hidastavan simulaatiota, vaikka neuroverkkomalli oli nopea Aprosin ulkopuolella. Aprosin Python-ominaisuus ei myöskään näytä itsessään aiheuttavan hitautta, jonka takia asiaa tulisi selvittää mallien implementoinnin mahdollistamiseksi. Koneoppimismallin tulee olla hyvin tarkka toimiakseen vuorovaikutuksessa fysiikkapohjaisen mallin kanssa. Jatkotutkimuksen ja Python-sovelluksen kehittämisen kannalta on tärkeää selvittää mikä on Aprosin koneoppimismalleille asettama tarkkuusvaatimus.
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Wenerstrom, Brent. "Temporal data mining in a dynamic feature space /". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1317.pdf.
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Bhardwaj, Ananya. "Biomimetic Detection of Dynamic Signatures in Foliage Echoes". Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102299.
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Horseshoe bats (family Rhinolophidae) are among the bat species that dynamically deform
their reception baffles (pinnae) and emission baffles (noseleaves) during signal reception and
emissions, respectively. These dynamics are a focus of prior studies that demonstrated that
these effects could introduce time-variance within emitted and received signals. Recent lab based
experiments with biomimetic hardware have shown that these dynamics can also inject
time-variant signatures into echoes from simple targets. However, complex foliage echoes,
which comprise a large portion of the received echoes and contain useful information for
these bats, have not been studied in prior research. We used a biomimetic sonarhead which
replicated these dynamics, to collect a large dataset of foliage echoes (>55,000). To generate
a neuromorphic representation of echoes that was representative of the neural spikes in bat
brains, we developed an auditory processing model based on Horseshoe bat physiological
data. Then, machine learning classifiers were employed to classify these spike representations
of echoes into distinct groups, based on the presence or absence of dynamics' effects.
Our results showed that classification with up to 80% accuracy was possible, indicating the
presence of these effects in foliage echoes, and their persistence through the auditory processing.
These results suggest that these dynamics' effects might be present in bat brains, and
therefore have the potential to inform behavioral decisions. Our results also indicated that
potential benefits from these effects might be location specific, as our classifier was more
effective in classifying echoes from the same physical location, compared to a dataset with
significant variation in recording locations. This result suggested that advantages of these
effects may be limited to the context of particular surroundings if the bat brain similarly
fails to generalize over variation in locations.Master of Science
Horseshoe bats (family Rhinolophidae) are an echolocating bat species, i.e., they emit sound waves and use the corresponding echoes received from the environment to gather information for navigation. This species of bats demonstrate the behavior of deforming their emitter (noseleaf), and ears (pinna), while emitting or receiving echolocation signals. Horseshoe bats are adept at navigating in the dark through dense foliage. Their impressive navigational abilities are of interest to researchers, as their biology can inspire solutions for autonomous drone navigation in foliage and underwater. Prior research, through numerical studies and experimental reproductions, has found that these deformations can introduce time-dependent changes in the emitted and received signals. Furthermore, recent research using a biomimetic robot has found that echoes received from simple shapes, such as cube and sphere, also contain time-dependent changes. However, prior studies have not used foliage echoes in their analysis, which are more complex, since they include a large number of randomly distributed targets (leaves). Foliage echoes also constitute a large share of echoes from the bats' habitats, hence an understanding of the effects of the dynamic deformations on these foliage echoes is of interest. Since echolocation signals exist within bat brains as neural spikes, it is also important to understand if these dynamic effects can be identified within such signal representations, as that would indicate that these effects are available to the bats' brains. In this study, a biomimetic robot that mimicked the dynamic pinna and noseleaf deformation was used to collect a large dataset (>55,000) of echoes from foliage. A signal processing model that mimicked the auditory processing of these bats and generated simulated spike responses was also developed. Supervised machine learning was used to classify these simulated spike responses into two groups based on the presence or absence of these dynamics' effects. The success of the machine learning classifiers of up to 80% accuracy suggested that the dynamic effects exist within foliage echoes and also spike-based representations. The machine learning classifier was more accurate when classifying echoes from a small confined area, as compared to echoes distributed over a larger area with varying foliage. This result suggests that any potential benefits from these effects might be location-specific if the bat brain similarly fails to generalize over the variation in echoes from different locations.
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Jin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology". University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.
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Lyubchyk, Leonid, Oleksy Galuza i Galina Grinberg. "Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering". Thesis, ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36819.
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The proposed preference learning on clusters method allows to fully realizing the advantages of the kernel-based approach. While the dimension of the model is determined by a pre-selected number of clusters and its complexity do not grow with increasing number of observations. Thus real-time preference function identification algorithm based on training data stream includes successive estimates of cluster parameter as well as average cluster ranks updating and recurrent kernel-based nonparametric estimation of preference model.
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Nguyen, Dang Quang. "Multi-Agent Learning in Highly Dynamic and Uncertain Environments". Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/30020.
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Over the last decades, machine learning research has considerably contributed to important solutions to numerous decision-making problems. Machine learning systems demonstrate enormous potential to generate decisions beyond the performance of humans or hand-crafted engineering systems, by utilising available feedback from environment and the decision history. In spite of significant efforts, challenges remain in developing a generic and consistent learning framework for modelling and optimising decision-making policies, especially applicable to multi-agent environments with frequent agent interactions and uncertain action outcomes. This thesis examines modelling and optimisation of decisions in highly dynamic and uncertain multi-agent environments. Specifically, we develop a general Markov Decision Process (MDP) based learning framework incorporating complex delayed rewards, aimed to optimise adaptive policies in presence of noise and dynamic agent interactions. In developing methods for this optimisation, we address a number of significant challenges: (a) presence of long delays in observing the action outcomes; (b) policy optimisation over complex and/or decentralised behaviours spanning multiple time steps; and (c) low learning efficiency due to the large search-space size. Two domains are selected to examine and resolve these challenges: (i) a large-scale agent-based model of the COVID-19 pandemic and response, with the task of optimising cost-effectiveness of centralised non-pharmaceutical interventions; and (ii) a simulated two-dimensional multi-agent soccer environment (RoboCup Soccer 2D Simulation), with the task of optimising decentralised policies for teams of autonomous soccer agents. Our studies uncover and resolve several interdependencies in modelling and learning action policies for decision-maker(s) in multi-agent environments. Firstly, we develop a general MDP-based framework capable of modelling action decisions at both global level (centralised actions) and local level (decentralised actions), addressing the question of (i) centralised policies versus decentralised policies. Secondly, we propose methods formulating delayed rewards, including short-term (tactical) and long-term (strategic) outcomes, which are applicable for efficient policy optimisation for both centralised and decentralised action decisions, thus addressing the dichotomy of (ii) short-term versus long-term outcomes of action decisions. Finally, we develop heuristics for preserving modular hierarchical decision-making structure, which narrow the search-space size, thus improving learning efficiency and addressing the dilemma of (iii) learning efficiency versus the size of search space.
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Clark, Mark A. "Dynamic Voltage/Frequency Scaling and Power-Gating of Network-on-Chip with Machine Learning". Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1544105215810566.
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Almannaa, Mohammed Hamad. "Optimizing Bike Sharing Systems: Dynamic Prediction Using Machine Learning and Statistical Techniques and Rebalancing". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/100737.
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The large increase in on-road vehicles over the years has resulted in cities facing challenges in providing high-quality transportation services. Traffic jams are a clear sign that cities are overwhelmed, and that current transportation networks and systems cannot accommodate the current demand without a change in policy, infrastructure, transportation modes, and commuter mode choice. In response to this problem, cities in a number of countries have started putting a threshold on the number of vehicles on the road by deploying a partial or complete ban on cars in the city center. For example, in Oslo, leaders have decided to completely ban privately-owned cars from its center by the end of 2019, making it the first European city to totally ban cars in the city center. Instead, public transit and cycling will be supported and encouraged in the banned-car zone, and hundreds of parking spaces in the city will be replaced by bike lanes.
As a government effort to support bicycling and offer alternative transportation modes, bike-sharing systems (BSSs) have been introduced in over 50 countries. BSSs aim to encourage people to travel via bike by distributing bicycles at stations located across an area of service. Residents and visitors can borrow a bike from any station and then return it to any station near their destination. Bicycles are considered an affordable, easy-to-use, and, healthy transportation mode, and BSSs show significant transportation, environmental, and health benefits.
As the use of BSSs have grown, imbalances in the system have become an issue and an obstacle for further growth. Imbalance occurs when bikers cannot drop off or pick-up a bike because the bike station is either full or empty. This problem has been investigated extensively by many researchers and policy makers, and several solutions have been proposed. There are three major ways to address the rebalancing issue: static, dynamic and incentivized. The incentivized approaches make use of the users in the balancing efforts, in which the operating company incentives them to change their destination in favor of keeping the system balanced. The other two approaches: static and dynamic, deal with the movement of bikes between stations either during or at the end of the day to overcome station imbalances. They both assume the location and number of bike stations are fixed and only the bikes can be moved. This is a realistic assumption given that current BSSs have only fixed stations. However, cities are dynamic and their geographical and economic growth affects the distribution of trips and thus constantly changing BSS user behavior. In addition, work-related bike trips cause certain stations to face a high-demand level during weekdays, while these same stations are at a low-demand level on weekends, and thus may be of little use. Moreover, fixed stations fail to accommodate big events such as football games, holidays, or sudden weather changes.
This dissertation proposes a new generation of BSSs in which we assume some of the bike stations can be portable. This approach takes advantage of both types of BSSs: dock-based and dock-less. Towards this goal, a BSS optimization framework was developed at both the tactical and operational level. Specifically, the framework consists of two levels: predicting bike counts at stations using fast, online, and incremental learning approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations.
This dissertation contributes to the field in five ways. First, a multi-objective supervised clustering algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, a dynamic, easy-to-interpret, rapid approach to predict bike counts at stations in a BSS was developed. Third, a univariate inventory model using a Markov chain process that provides an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations, using an agent-based simulation approach as a proof-of-concept was developed. Fifth, mathematical and heuristic approaches were proposed to balance bike stations.Doctor of Philosophy
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Conradsson, Emil, i Vidar Johansson. "A MODEL-INDEPENDENT METHODOLOGY FOR A ROOT CAUSE ANALYSIS SYSTEM : A STUDY INVESTIGATING INTERPRETABLE MACHINE LEARNING METHODS". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160372.
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Today, companies like Volvo GTO experience a vast increase in data and the ability toprocess it. This makes it possible to utilize machine learning models to construct a rootcause analysis system in order to predict, explain and prevent defects. However, thereexists a trade-off between model performance and explanation capability, both of whichare essential to such system.This thesis aims to, with the use of machine learning models, inspect the relationshipbetween sensor data from the painting process and the texture defectorange peel. Theaim is also to evaluate the consistency of different explanation methods.After the data was preprocessed, and new features were engineered, e.g. adjustments,three machine learning models were trained and tested. In order to explain a linearmodel, one can use its coefficients. In the case of a tree-based model, MDI is a commonglobal explanation method. SHAP is a state-of-the-art model-independent method thatcan explain a model globally and locally. These three methods were compared in orderto evaluate the consistency of their explanations. If SHAP would be consistent with theothers on a global level, it can be argued that SHAP can be used locally in an root causeanalysis.The study showed that the coefficients and MDI were consistent with SHAP as theoverall correlation between them were high and because they tended to weight thefeatures in a similar way. From this conclusion, a root cause analysis algorithm wasdeveloped with SHAP as a local explanation method. Finally, it cannot be concludedthat there is a relationship between the sensor data andorange peel, as the adjustments ofthe process were the most impactful features.Idag upplever företag som Volvo GTO en stor ökning av data och en förbättrad förmågaatt bearbeta den. Detta gör det möjligt att, med hjälp av maskininlärningsmodeller,skapa ett rotorsaksanalyssystem för att förutspå, förklara och förebygga defekter. Detfinns dock en balans mellan modellprestanda och förklaringskapacitet, där båda ärväsentliga för ett sådant system.Detta examensarbete har som mål att, med hjälp av maskininlärningsmodeller, under-söka förhållandet mellan sensordata från målningsprocessen och strukturdefektenorangepeel. Målet är även att utvärdera hur konsekventa olika förklaringsmetoder är.Efter att datat förarbetats och nya variabler skapats, t.ex. förändringar som gjorts, trä-nades och testades tre maskinlärningsmodeller. En linjär modell kan tolkas genomdess koefficienter. En vanlig metod för att globalt förklara trädbaserade modeller ärMDI. SHAP är en modern modelloberoende metod som kan förklara modeller bådeglobalt och lokalt. Dessa tre förklaringsmetoder jämfördes sedan för att utvärdera hurkonsekventa de var i sina förklaringar. Om SHAP skulle vara konsekvent med de andrapå en global nivå, kan det argumenteras för att SHAP kan användas lokalt i en rotorsak-analys.Studien visade att koefficienterna och MDI var konsekventa med SHAP då den över-gripande korrelationen mellan dem var hög samt att metoderna tenderade att viktavariablerna på ett liknande sätt. Genom denna slutsats utvecklades en rotorsakanalysal-goritm med SHAP som lokal förklaringsmetod. Slutligen går det inte att dra någonslutsats om att det finns ett samband mellan sensordatat ochorange peel, eftersom förän-dringarna i processen var de mest betydande variablerna.
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Rihani, Mohamad-Al-Fadl. "Management of Dynamic Reconfiguration in a Wireless Digital Communication Context". Thesis, Rennes, INSA, 2018. http://www.theses.fr/2018ISAR0030/document.
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Aujourd'hui, les appareils sans fil disposent généralement de plusieurs technologies d'accès radio (LTE, WiFi,WiMax, ...) pour gérer une grande variété de normes ou de technologies. Ces appareils doivent être suffisamment intelligents et autonomes pour atteindre un niveau de performance donné ou sélectionne automatiquement la meilleure technologie sans fil disponible en fonction de la disponibilité des normes. Du point de vue matériel, les périphériques System on Chip (SoC) intègrent des processeurs et des structures logiques FPGA sur la même puce avec une interconnexion rapide. Cela permet de concevoir des systèmes logiciels / matériels et de mettre en oeuvre de nouvelles techniques et méthodologies qui améliorent considérablement les performances des systèmes de communication. Dans ces dispositifs, la reconfiguration partielle dynamique (DPR) constitue une technique bien connue pour reconfigurer seulement une zone spécifique dans le FPGA tandis que d'autres parties continuent à fonctionner indépendamment. Pour évaluer quand il est avantageux d'effectuer un DPR, des techniques adaptatives ont été proposées. Ils consistent à reconfigurer automatiquement des parties du système en fonction de paramètres spécifiques. Dans cette thèse, un système de communication sans fil intelligent visant à implémenter un émetteur OFDM adaptatif et à effectuer un transfert vertical dans des réseaux hétérogènes est présenté. Une couche physique unifiée pour les réseaux WiFi-WiMax est également proposée. Un algorithme de transfert vertical intelligent (VHA) basé sur les réseaux neuronaux (NN) a été proposé pour sélectionner le meilleur standard sans fil disponible dans un réseau hétérogène. Le système a été implémenté et testé sur un ZedBoard équipé d'un Xilinx Zynq-7000-SoC. La performance du système est décrite et des résultats de simulation sont présentés afin de valider l'architecture proposée. Des mesures de puissance en temps réel ont été appliquées pour calculer l'énergie de surcharge pour l'opération de RP. De plus, des démonstrations ont été effectuées pour tester et valider le système mis en placeToday, wireless devices generally feature multiple radio access technologies (LTE, WiFi, WiMax, ...) to handle a rich variety of standards or technologies. These devices should be intelligent and autonomous enough in order to either reach a given level of performance or automatically select the best available wireless standard. On the hardware side, System on Chip (SoC) devices integrate processors and FPGA logic fabrics on the same chip with fast inter-connection. This allows designing Software/Hardware systems. In these devices, Dynamic Partial Reconfiguration (DPR) constitutes a well-known technique for reconfiguring only a specific area within the FPGA while other parts continue to operate independently. To evaluate when it is advantageous to perform DPR, adaptive techniques have been proposed. They consist in reconfiguring parts of the system automatically according to specific parameters. In this thesis, an intelligent wireless communication system aiming at implementing an adaptive OFDM based transmitter is presented. An unified physical layer for WiFi-WiMax networks is also proposed. An intelligent Vertical Handover Algorithm (VHA) based on Neural Networks (NN) was proposed to select best available wireless standard in heterogeneous network. The system was implemented and tested on a ZedBoard which features a Xilinx Zynq-7000-SoC. The performance of the system is described and simulation results are presented in order to validate the proposed architecture. Real time power measurements have been applied to compute the overhead power for the PR operation. In addition demonstrations have been performed to test and validate the implemented system
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Tamascelli, Nicola. "A Machine Learning Approach to Predict Chattering Alarms". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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The alarm system plays a vital role to grant safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only; during alarm floods, the operator may be overwhelmed by several alarms in a short time span. Crucial alarms are more likely to be missed during these situations. Poor alarm management is one of the main causes of unintended plant shut down, incidents and near misses in the chemical industry. Most of the alarms triggered during a flood episode are nuisance alarms –i.e. alarms that do not communicate new information to the operator, or alarms that do not require an operator action. Chattering alarms –i.e. that repeat three or more times in a minute, and redundant alarms –i.e. duplicated alarms, are common forms of nuisance. Identifying nuisance alarms is a key step to improve the performance of the alarm system. Advanced techniques for alarm rationalization have been developed, proposing methods to quantify chattering, redundancy and correlation between alarms. Although very effective, these techniques produce static results. Machine Learning appears to be an interesting opportunity to retrieve further knowledge and support these techniques. This knowledge can be used to produce more flexible and dynamic models, as well as to predict alarm behaviour during floods. The aim of this study is to develop a machine learning-based algorithm for real-time alarm classification and rationalization, whose results can be used to support the operator decision-making procedure. Specifically, efforts have been directed towards chattering prediction during alarm floods. Advanced techniques for chattering, redundancy and correlation assessment have been performed on a real industrial alarm database. A modified approach has been developed to dynamically assess chattering, and the results have been used to train three different machine learning models, whose performance has been evaluated and discussed.
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Moffett, Jeffrey P. "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games". Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/320.
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We have developed a causal model of how various aspects of a computer game influence how much a player enjoys the experience, as well as how long the player will play. This model is organized into three layers: a generic layer that applies to any game, a refinement layer for a particular game genre, and an instantiation layer for a specific game. Two experiments using different games were performed to validate the model. The model was used to design and implement a system and API for Dynamic Difficulty Adjustment(DDA). This DDA system and API uses machine learning techniques to make changes to a game in real time in the hopes of improving the experience of the user and making them play longer. A final experiment is presented that shows the effectiveness of the designed system.
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Fang, Chunsheng. "Novel Frameworks for Mining Heterogeneous and Dynamic Networks". University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.
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Templeton, Julian. "Designing Robust Trust Establishment Models with a Generalized Architecture and a Cluster-Based Improvement Methodology". Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42556.
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In Multi-Agent Systems consisting of intelligent agents that interact with one another, where the agents are software entities which represent individuals or organizations, it is important for the agents to be equipped with trust evaluation models which allow the agents to evaluate the trustworthiness of other agents when dishonest agents may exist in an environment. Evaluating trust allows agents to find and select reliable interaction partners in an environment. Thus, the cost incurred by an agent for establishing trust in an environment can be compensated if this improved trustworthiness leads to an increased number of profitable transactions. Therefore, it is equally important to design effective trust establishment models which allow an agent to generate trust among other agents in an environment. This thesis focuses on providing improvements to the designs of existing and future trust establishment models.
Robust trust establishment models, such as the Integrated Trust Establishment (ITE) model, may use dynamically updated variables to adjust the predicted importance of a task’s criteria for specific trustors. This thesis proposes a cluster-based approach to update these dynamic variables more accurately to achieve improved trust establishment performance. Rather than sharing these dynamic variables globally, a model can learn to adjust a trustee’s behaviours more accurately to trustor needs by storing the variables locally for each trustor and by updating groups of these variables together by using data from a corresponding group of similar trustors.
This work also presents a generalized trust establishment model architecture to help models be easier to design and be more modular. This architecture introduces a new transaction-level preprocessing module to help improve a model’s performance and defines a trustor-level postprocessing module to encapsulate the designs of existing models. The preprocessing module allows a model to fine-tune the resources that an agent will provide during a transaction before it occurs. A trust establishment model, named the Generalized Trust Establishment Model (GTEM), is designed to showcase the benefits of using the preprocessing module.
Simulated comparisons between a cluster-based version of ITE and ITE indicate that the cluster-based approach helps trustees better meet the expectations of trustors while minimizing the cost of doing so. Comparing GTEM to itself without the preprocessing module and to two existing models in simulated tests exhibits that the preprocessing module improves a trustee’s trustworthiness and better meets trustor desires at a faster rate than without using preprocessing.
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Fent, Thomas. "Using genetics based machine learning to find strategies for product placement in a dynamic market". SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/694/1/document.pdf.
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In this paper we discuss the necessity of models including complex adaptive systems in order to eliminate the shortcomings of neoclassical models based on equilibrium theory. A simulation model containing artificial adaptive agents is used to explore the dynamics of a market of highly replaceable products. A population consisting of two classes of agents is implemented to observe if methods provided by modern computational intelligence can help finding a meaningful strategy for product placement. During several simulation runs it turned out that the agents using CI-methods outperformed their competitors. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Souriau, Rémi. "machine learning for modeling dynamic stochastic systems : application to adaptive control on deep-brain stimulation". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG004.
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Ces dernières années ont été marquées par l'émergence d'un grand nombre de base données dans de nombreux domaines comme la médecine par exemple. La création de ces bases données a ouvert la voie à de nouvelles applications. Les propriétés des données sont parfois complexes (non linéarité, dynamique, grande dimension ou encore absence d'étiquette) et nécessite des modèles d'apprentissage performants. Parmi les modèles d'apprentissage existant, les réseaux de neurones artificiels ont connu un large succès ces dernières décennies. Le succès de ces modèles repose sur la non linéarité des neurones, l'utilisation de variables latentes et leur grande flexibilité leur permettant de s'adapter à de nombreux problèmes. Les machines de Boltzmann présentées dans cette thèse sont une famille de réseaux de neurones non supervisés. Introduite par Hinton dans les années 80, cette famille de modèle a connu un grand intérêt dans le début du 21e siècle et de nouvelles extensions sont proposées régulièrement.Cette thèse est découpée en deux parties. Une partie exploratoire sur la famille des machines de Boltzmann et une partie applicative. L'application étudiée est l'apprentissage non supervisé des signaux électroencéphalogramme intracrânien chez les rats Parkinsonien pour le contrôle des symptômes de la maladie de Parkinson.Les machines de Boltzmann ont donné naissance aux réseaux de diffusion. Il s'agit de modèles non supervisés qui reposent sur l'apprentissage d'une équation différentielle stochastique pour des données dynamiques et stochastiques. Ce réseau fait l'objet d'un développement particulier dans cette thèse et un nouvel algorithme d'apprentissage est proposé. Son utilisation est ensuite testée sur des données jouet ainsi que sur des données réellesThe past recent years have been marked by the emergence of a large amount of database in many fields like health. The creation of many databases paves the way to new applications. Properties of data are sometimes complex (non linearity, dynamic, high dimensions) and require to perform machine learning models. Belong existing machine learning models, artificial neural network got a large success since the last decades. The success of these models lies on the non linearity behavior of neurons, the use of latent units and the flexibility of these models to adapt to many different problems. Boltzmann machines presented in this thesis are a family of generative neural networks. Introduced by Hinton in the 80's, this family have got a large interest at the beginning of the 21st century and new extensions are regularly proposed.This thesis is divided into two parts. A first part exploring Boltzmann machines and their applications. In this thesis the unsupervised learning of intracranial electroencephalogram signals on rats with Parkinson's disease for the control of the symptoms is studied.Boltzmann machines gave birth to Diffusion networks which are also generative model based on the learning of a stochastic differential equation for dynamic and stochastic data. This model is studied again in this thesis and a new training algorithm is proposed. Its use is tested on toy data as well as on real database
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Gray, David Philip Harry. "Software defect prediction using static code metrics : formulating a methodology". Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/11067.
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Software defect prediction is motivated by the huge costs incurred as a result of software failures. In an effort to reduce these costs, researchers have been utilising software metrics to try and build predictive models capable of locating the most defect-prone parts of a system. These areas can then be subject to some form of further analysis, such as a manual code review. It is hoped that such defect predictors will enable software to be produced more cost effectively, and/or be of higher quality. In this dissertation I identify many data quality and methodological issues in previous defect prediction studies. The main data source is the NASA Metrics Data Program Repository. The issues discovered with these well-utilised data sets include many examples of seemingly impossible values, and much redundant data. The redundant, or repeated data points are shown to be the cause of potentially serious data mining problems. Other methodological issues discovered include the violation of basic data mining principles, and the misleading reporting of classifier predictive performance. The issues discovered lead to a new proposed methodology for software defect prediction. The methodology is focused around data analysis, as this appears to have been overlooked in many prior studies. The aim of the methodology is to be able to obtain a realistic estimate of potential real-world predictive performance, and also to have simple performance baselines with which to compare against the actual performance achieved. This is important as quantifying predictive performance appropriately is a difficult task. The findings of this dissertation raise questions about the current defect prediction body of knowledge. So many data-related and/or methodological errors have previously occurred that it may now be time to revisit the fundamental aspects of this research area, to determine what we really know, and how we should proceed.
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AlShammeri, Mohammed. "Dynamic Committees for Handling Concept Drift in Databases (DCCD)". Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23498.
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Concept drift refers to a problem that is caused by a change in the data distribution in data mining. This leads to reduction in the accuracy of the current model that is used to examine the underlying data distribution of the concept to be discovered. A number of techniques have been introduced to address this issue, in a supervised learning (or classification) setting. In a classification setting, the target concept (or class) to be learned is known. One of these techniques is called “Ensemble learning”, which refers to using multiple trained classifiers in order to get better predictions by using some voting scheme. In a traditional ensemble, the underlying base classifiers are all of the same type. Recent research extends the idea of ensemble learning to the idea of using committees, where a committee consists of diverse classifiers. This is the main difference between the regular ensemble classifiers and the committee learning algorithms. Committees are able to use diverse learning methods simultaneously and dynamically take advantage of the most accurate classifiers as the data change. In addition, some committees are able to replace their members when they perform poorly.
This thesis presents two new algorithms that address concept drifts. The first algorithm has been designed to systematically introduce gradual and sudden concept drift scenarios into datasets. In order to save time and avoid memory consumption, the Concept Drift Introducer (CDI) algorithm divides the number of drift scenarios into phases. The main advantage of using phases is that it allows us to produce a highly scalable concept drift detector that evaluates each phase, instead of evaluating each individual drift scenario.
We further designed a novel algorithm to handle concept drift. Our Dynamic Committee for Concept Drift (DCCD) algorithm uses a voted committee of hypotheses that vote on the best base classifier, based on its predictive accuracy. The novelty of DCCD lies in the fact that we employ diverse heterogeneous classifiers in one committee in an attempt to maximize diversity. DCCD detects concept drifts by using the accuracy and by weighing the committee members by adding one point to the most accurate member. The total loss in accuracy for each member is calculated at the end of each point of measurement, or phase. The performance of the committee members are evaluated to decide whether a member needs to be replaced or not. Moreover, DCCD detects the worst member in the committee and then eliminates this member by using a weighting mechanism.
Our experimental evaluation centers on evaluating the performance of DCCD on various datasets of different sizes, with different levels of gradual and sudden concept drift. We further compare our algorithm to another state-of-the-art algorithm, namely the MultiScheme approach. The experiments indicate the effectiveness of our DCCD method under a number of diverse circumstances. The DCCD algorithm generally generates high performance results, especially when the number of concept drifts is large in a dataset. For the size of the datasets used, our results showed that DCCD produced a steady improvement in performance when applied to small datasets. Further, in large and medium datasets, our DCCD method has a comparable, and often slightly higher, performance than the MultiScheme technique. The experimental results also show that the DCCD algorithm limits the loss in accuracy over time, regardless of the size of the dataset.