Dissertations / Theses on the topic 'Artificial Neural Network-based modeling'
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Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.
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Saptoro, Agus. "An integrated approach to artificial neural network based process modelling." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/2484.
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ANN technology exploded into the world of process modelling and control in the late 1980’s. The technology shows great promise and is seen as a technology that could provide models for most systems without the need to understand the fundamental behaviour or relationships among the process variables. Today, ANN applications have been applied successfully in a number of areas of process modelling and control, with the best-established applications being in the area of inferential measurements or soft sensors.Unfortunately, ‘the free lunch did not have much meat’. Overtime, people focused more on the true capabilities and power of ANN, the ability to model nonlinear relationships in data without having to define the form of the nonlinearity. However, there is often a tendency to merely plug in the data, turn the ANN training software on, and blindly accept the results. This is probably inevitable since, to date, there are no textbooks or scientific journal papers providing an integrated and systematic approach for ANN model development addressing pre-modelling, training and postmodelling stages. Therefore, addressing issues in those three phases of ANN model development is essential to support and to improve further applications of ANN technology in the area of process modelling and control.The model development issues in pre-modelling and training phases were addressed by reviewing current practice and existing techniques. For each issue, a novel method was proposed to improve the performance of ANN models. The new approaches were tested in a variety of benchmarking studies using artificial samples and coal property datasets from power station boilers.The research work in the post-modelling stage analysis which emphasises on taking the lid off black box model, proposes a novel technique to extract knowledge from the models and simultaneously obtain better understanding of the process. Postmodelling phase issues were addressed thoroughly including construction of prediction limit, sensitivity analysis and development of mathematical representation of the trained ANN model.Confidence intervals of the ANN models were analysed to construct the prediction boundary of the model. This analysis provides useful information related to interpolation and extrapolation of the model. It also highlighted how good the ANN models can be used for extrapolation purposes.An effort based on sensitivity analysis of hidden layers is also proposed to understand the behaviours of the ANN models. Using this technique, knowledge and information are retrieved from the developed models. A comparative study of the proposed techniques and the current practice was also presented.The last topic addressed in this thesis is knowledge extraction of ANN models using mathematical analysis of the hidden layers. The proposed analysis is applied in order to open the black box of the ANN models and is implemented to simulated and real historical plant data so that useful information from those data and better understanding of the process are obtained.All in all, efforts have been made in this thesis to minimise the use of abstract mathematical language and in some cases, simplify the language so that ANN modelling theory can be understood by a wider range of audience, especially the new practitioners in ANN based modelling and control. It is hoped that the insight provided in the dissertation will provide an integrated approach to pre-modelling, training and post-modelling stages of ANN models. This ‘new guideline’ of ANN model development is unique and beneficial, providing a systematic framework for the preparation, design, evaluation and implementation of ANN models in process modelling and control in particular and prediction / forecasting tool in general.
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Ajayi, Toluwaleke. "Modeling Discharge and Water Chemistry Using Artificial Neural Network." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1620167556121952.
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Rothrock, Ling. "Modeling skilled decision-making using artificial neural network and genetic-based machine learning techniques." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25084.
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Byrne, Brian James. "An evaluation of artificial neural network modeling for manpower analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273001.
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Thesis (M.S. in Management) Naval Postgraduate School, September 1993.Thesis advisor(s): George W. Thomas ; Timothy P. Hill. "September 1993." Includes bibliographical references. Also available online,
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FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.
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Rappresentare la conoscenza, modellare il ragionamento umano e comprendere i processi di pensiero sono sempre state parti centrali delle attività intellettuali, fin dai primi tentativi dei filosofi greci.
Non è solo un caso che, non appena i computer hanno iniziato a diffondersi, scienziati e matematici straordinari come John McCarthy, Marvin Minsky e Claude Shannon hanno iniziato a creare sistemi Artificialmente Intelligenti con una prospettiva orientata al simbolismo.
Anche se questo è stato un percorso parzialmente forzato a causa delle capacità di calcolo molto limitate dell'epoca, ha segnato l'inizio di quella che oggi è conosciuta come Intelligenza Artificiale Classica (o Simbolica), o essenzialmente, un insieme di tecniche per implementare comportamenti "intelligenti" attraverso formalismi logici e di dimostrazione di teoremi.
Le tecniche di Intelligenza Artificiale Classica sono infatti processi molto diretti e centrati sull'uomo, che trovano il loro punto di forza nella semplice interpretabilità umana e nella riusabilità della conoscenza.
Al contrario, esse soffrono di problemi di computabilità quando sono applicate a compiti del mondo reale, per lo più dovuti all'esplosione combinatoria dello spazio di ricerca (soprattutto quando si ha a che fare con il tempo), e all'indecidibilità.
Tuttavia, le sempre maggiori capacità dell'hardware dei computer hanno aperto nuove possibilità di crescita per altri metodi più orientati alla statistica, come le Reti Neurali.
Anche se la teoria alla base di questi metodi era nota da tempo, è stato solo negli ultimi anni che sono riusciti a raggiungere progressi significativi, e a superare le tecniche classiche di IA su molti fronti.
Al momento, i principali ostacoli di tali tecniche di IA statistica sono rappresentati dall'elevato consumo di energia e dalla mancanza di modi semplici per gli esseri umani di comprendere il processo che ha portato a un particolare risultato.
Riassumendo, le tecniche di IA classica e statistica possono essere viste come due facce della stessa medaglia: se un dominio presenta informazioni strutturate, poca incertezza e processi decisionali chiari, allora l'IA classica potrebbe essere lo strumento giusto, o altrimenti, quando le informazioni sono meno strutturate, hanno più incertezza, ambiguità e non è possibile identificare processi decisionali chiari, allora l'IA statistica dovrebbe essere scelta.
Lo scopo principale di questa tesi è quindi (i) mostrare le capacità e i limiti delle attuali tecniche di Intelligenza Artificiale (Classica e Statistica) sia in ambiti strutturati che non strutturati, e (ii) dimostrare come la modellazione basata su eventi possa affrontare alcune delle loro criticità, fornendo nuove potenziali connessioni e nuove prospettive.Representing knowledge, modeling human reasoning, and understanding thought processes have always been central parts of intellectual activities, since the first attempts by greek philosophers. It is not just by chance that, as soon as computers started to spread, remarkable scientists and mathematicians such as John McCarthy, Marvin Minsky and Claude Shannon started creating Artificially Intelligent systems with a symbolic oriented perspective. Even though this has been a partially forced path due to the very limited computing capabilities at the time, it marked the beginning of what is now known as Classical (or Symbolic) Artificial Intelligence, or essentially, a set of techniques for implementing "intelligent" behaviours by means of logic formalisms and theorem proving. Classical AI techniques are indeed very direct and human-centered processes, which find their strenghts on straightforward human interpretability and knowledge reusability. On the contrary, they suffer of computability problems when applied to real world tasks, mostly due to search space combinatorial explosion (especially when reasoning with time), and undecidability. However, the ever-increasing capabilites of computer hardware opened new possibilities for other more statistical-oriented methods to grow, such as Neural Networks. Even if the theory behind these methods was long known, it was only in recent years that they managed to achieve significant breakthroughs, and to surpass Classical AI techniques on many tasks. At the moment, the main hurdles of such statistical AI techniques are represented by the high energy consumption and the lack of easy ways for humans to understand the process that led to a particular result. Summing up, Classical and Statistical AI techniques can be seen as two faces of the same coin: if a domain presents structured information, little uncertainty, and clear decision processes, then Classical AI might be the right tool, or otherwise, when the information is less structured, has more uncertainty, ambiguity and clear decision processes cannot be identified, then Statistical AI should be chosen. The main purpose of this thesis is thus (i) to show capabilities and limits of current (Classical and Statistical) Artificial Intelligence techniques in both structured and unstructured domains, and (ii) to demostrate how event-based modeling can tackle some of their critical issues, providing new potential connections and novel perspectives.
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FLECK, JULIA LIMA. "ARTIFICIAL NEURAL NETWORK MODELING FOR QUALITY INFERENCE OF A POLYMERIZATION PROCESS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12980@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOO presente trabalho apresenta o desenvolvimento de um modelo neural para a inferência da qualidade do polietileno de baixa densidade (PEBD) a partir dos valores das variáveis de processo do sistema reacional. Para tal, fez- se uso de dados operacionais de uma empresa petroquímica, cujo pré-processamento incluiu a seleção de variáveis, limpeza e normalização dos dados selecionados e preparação dos padrões. A capacidade de inferência do modelo neural desenvolvido neste estudo foi comparada com a de dois modelos fenomenológicos existentes. Para tal, utilizou-se como medida de desempenho o valor do erro médio absoluto percentual dos modelos, tendo como referência valores experimentais do índice de fluidez. Neste contexto, o modelo neural apresentou-se como uma eficiente ferramenta de modelagem da qualidade do sistema reacional de produção do PEBD.
This work comprises the development of a neural network- based model for quality inference of low density polyethylene (LDPE). Plant data corresponding to the process variables of a petrochemical company`s LDPE reactor were used for model development. The data were preprocessed in the following manner: first, the most relevant process variables were selected, then data were conditioned and normalized. The neural network- based model was able to accurately predict the value of the polymer melt index as a function of the process variables. This model`s performance was compared with that of two mechanistic models developed from first principles. The comparison was made through the models` mean absolute percentage error, which was calculated with respect to experimental values of the melt index. The results obtained confirm the neural network model`s ability to infer values of quality-related measurements of the LDPE reactor.
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Li, Tan. "Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87417.
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Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic.PHD
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Bhanot, Nishant. "Artificial Neural Networks based Modeling and Analysis of Semi-Active Damper System." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78295.
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The suspension system is one of the most sensitive systems of a vehicle as it affects the dynamic behavior of the vehicle with even minor changes. These systems are designed to carry out multiple tasks such as isolating the vehicle body from the road/tire vibrations as well as achieving desired ride and handling performance levels in both steady state and limit handling conditions. The damping coefficient of the damper plays a crucial role in determining the overall frequency response of the suspension system. Considerable research has been carried out on semi active damper systems as the damping coefficient can be varied without the system requiring significant external power giving them advantages over both passive and fully active suspension systems.
Dampers behave as non-linear systems at higher frequencies and hence it has been difficult to develop accurate models for its full range of motion. This study aims to develop a velocity sensitive damper model using artificial neural networks and essentially provide a 'black-box' model which encapsulates the non-linear behavior of the damper. A feed-forward neural network was developed by testing a semi active damper on a shock dynamometer at CenTiRe for multiple frequencies and damping ratios. This data was used for supervised training of the network using MATLAB Neural Network Toolbox. The developed NN model was evaluated for its prediction accuracy. Further, the developed damper model was analyzed for feasibility of use for simulations and controls by integrating it in a Simulink based quarter car model and applying the well-known skyhook control strategy. Finally, effects on ride and handling dynamics were evaluated in Carsim by replacing the default damper model with the proposed model. It was established that this damper modeling technique can be used to help evaluate the behavior of the damper on both component as well as vehicle level without needing to develop a complex physics based model. This can be especially beneficial in the earlier stages of vehicle development.Master of Science
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Kvale, David Thomas. "Artificial Neural Network-Based Approaches for Modeling the Radiated Emissions from Printed Circuit Board Structures and Shields." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1280698960.
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Joy, Karen Lynn. "Evaluating input variable effects of an artificial neural network modeling facial attractiveness /." Also available to VCU users online at:, 2005. http://hdl.handle.net/10156/1253.
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Joy, Karen. "Evaluating Input Variable Effects of an Artificial Neural Network Modeling Facial Attractiveness." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd_retro/128.
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Artificial Neural Networks (ANNs) are powerful predictors, however, they essentially function like 'black boxes' because they lack explanatory power. Various algorithms have been developed to examine input influences and interactions thus enhancing understanding of the function being modeled. The study of facial attractiveness is one domain that could potentially benefit from ANN models. The literature shows that the relationship between attractiveness and facial attributes is complex and not yet fully understood. In this project, a feed-forward ANN was trained with backpropagation to 0.86 classification using 8-fold cross validation. The dataset consisted of 88 female facial images, each containing 17 geofacial measurements, a random noise variable, and a rating. Input 'clamping' and the Connection Weight Approach (Olden & Jackson, 2002), were implemented and the results were examined in terms of the facial attractiveness domain. In general, the results suggest that more feminized and asymmetrical features enhance facial attractiveness.
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Perez, Miguel A. "Prediction of Whole-body Lifting Kinematics using Artificial Neural Networks." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/28706.
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Musculoskeletal pain and injury continue to be prevalent sources of disability for thousands of workers in the U.S. every year. Proactive approaches to the reduction of this incidence attempt to prevent the injury by effecting task design so that human capabilities and limitations are driving factors in the task design and analysis process. Knowledge about the posture and kinematics that might be employed by an individual in performing a task is an important element of these proactive approaches to task design and analysis, especially for manual materials handling (i.e., lifting) exertions. In turn, accurate models that predict posture and kinematics can reduce the need for empirical postural and kinematic data in this task development process. Artificial neural networks were used in this investigation to achieve these predictions. As input, these networks received information about lift characteristics (e.g. target location, movement duration) and returned a predicted set of joint angles. Two types of networks were created, one to predict static posture based on target position, the second to predict the time histories of several joint angles (i.e., kinematics) as an object is lifted or lowered. Initial networks were created for sagittally symmetric lifts (two dimensions), but the final set of networks was expanded to make predictions for symmetric and asymmetric lifts in three dimensions. Networks were trained and verified with an empirical set of non-cyclic lifting motions. Notably, the within-subject variability in these motions was similar in magnitude to the associated between-subjects variability. In general, the networks were able to assimilate the data relatively well, especially in predicting kinematics, where root mean square errors were typically smaller than 20 degrees. These errors were similar in magnitude to the levels of within-subject variability observed in the dataset. Network performance also compared favorably to other existing models, typically resulting in smaller prediction errors than these other approaches. In addition, the internal connections of trained networks were examined to infer hypothetical motor control strategies. Results of this examination showed that feedback was an important component in providing kinematic predictions, whereas posture prediction benefited greatly from knowledge about individual anthropometry. Finally, potential improvements to increase prediction accuracy are discussed. Overall, these results support the use of artificial neural network models to predict posture and kinematics for lifting tasks.Ph. D.
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Akkala, Arjun. "Development of Artificial Neural Networks Based Interpolation Techniques for the Modeling and Estimation of Radon Concentrations in Ohio." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279315482.
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Pagliarini, Silvia. "Modeling the neural network responsible for song learning." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0107.
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Pendant la première période de leur vie, les bébés et les jeunes oiseaux présentent des phases de développement vocal comparables : ils écoutent d'abord leurs parents/tuteurs afin de construire une représentation neurale du stimulus auditif perçu, puis ils commencent à produire des sons qui se rapprochent progressivement du chant de leur tuteur. Cette phase d'apprentissage est appelée la phase sensorimotrice et se caractérise par la présence de babillage. Elle se termine lorsque le chant se cristallise, c'est-à-dire lorsqu'il devient semblable à celui produit par les adultes.Il y a des similitudes entre les voies cérébrales responsables de l'apprentissage sensorimoteur chez l'homme et chez les oiseaux. Dans les deux cas, une voie s’occupe de la production vocale et implique des projections directes des zones auditives vers les zones motrices, et une autre voie s’occupe de l’apprentissage vocal, de l'imitation et de la plasticité.Chez les oiseaux, ces circuits cérébraux sont exclusivement dédiés à l'apprentissage du chant, ce qui en fait un modèle idéal pour explorer les mécanismes neuronaux de l’apprentissage vocal par imitation.Cette thèse vise à construire un modèle de l'apprentissage du chant des oiseaux par imitation. De nombreuses études antérieures ont tenté de mettre en œuvre l'apprentissage par imitation dans des modèles informatiques et partagent une structure commune. Ces modèles comprennent des mécanismes d'apprentissage et, éventuellement, des stratégies d'exploration et d'évaluation.Dans ces modèles, une fonction de contrôle moteur permet la production de sons et une réponse sensorielle modélise soit la façon dont le son est perçu, soit la façon dont il façonne la récompense. Les entrées et les sorties de ces fonctions sont dans plusieurs espaces: l'espace moteur (paramètres moteurs), l'espace sensoriel (sons réels), l'espace perceptif (représentation à faible dimension du son) ou l’espace des objectifs (représentation non perceptive du son cible).Le premier modèle proposé est un modèle théorique inverse basé sur un modèle d'apprentissage vocal simplifié où l'espace sensoriel coïncide avec l'espace moteur (c'est-à-dire qu'il n'y a pas de production sonore). Une telle simplification permet d'étudier comment introduire des hypothèses biologiques (par exemple, une réponse non linéaire) dans un modèle d'apprentissage vocal et quels sont les paramètres qui influencent le plus la puissance de calcul du modèle.Afin de disposer d'un modèle complet (capable de percevoir et de produire des sons), nous avions besoin d'une fonction de contrôle moteur capable de reproduire des sons similaires à des données réelles. Nous avons analysé la capacité de WaveGAN (un réseau de génération) à produire des chants de canari réalistes. Dans ce modèle, l'espace d'entrée devient l'espace latent après l'entraînement et permet la représentation d'un ensemble de données à haute dimension dans une variété à plus basse dimension. Nous avons obtenu des chants de canari réalistes en utilisant seulement trois dimensions pour l'espace latent. Des analyses quantitatives et qualitatives démontrent les capacités d'interpolation du modèle, ce qui suggère que le modèle peut être utilisé comme fonction motrice dans un modèle d'apprentissage vocal.La deuxième version du modèle est un modèle d'apprentissage vocal complet avec une boucle action-perception complète (il comprend l'espace moteur, l'espace sensoriel et l'espace perceptif). La production sonore est réalisée par le générateur GAN obtenu précédemment. Un réseau neuronal récurrent classant les syllabes sert de réponse sensorielle perceptive. La correspondance entre l'espace perceptuel et l'espace moteur est apprise par un modèle inverse. Les résultats préliminaires montrent l'impact du taux d'apprentissage lorsque différentes fonctions de réponse sensorielle sont mises en œuvreDuring the first period of their life, babies and juvenile birds show comparable phases of vocal development: first, they listen to their parents/tutors in order to build a neural representation of the experienced auditory stimulus, then they start to produce sound and progressively get closer to reproducing their tutor song. This phase of learning is called the sensorimotor phase and is characterized by the presence of babbling, in babies, and subsong, in birds. It ends when the song crystallizes and becomes similar to the one produced by the adults.It is possible to find analogies between brain pathways responsible for sensorimotor learning in humans and birds: a vocal production pathway involves direct projections from auditory areas to motor neurons, and a vocal learning pathway is responsible for imitation and plasticity. The behavioral studies and the neuroanatomical structure of the vocal control circuit in humans and birds provide the basis for bio-inspired models of vocal learning.In particular, birds have brain circuits exclusively dedicated to song learning, making them an ideal model for exploring the representation of vocal learning by imitation of tutors.This thesis aims to build a vocal learning model underlying song learning in birds. An extensive review of the existing literature is discussed in the thesis: many previous studies have attempted to implement imitative learning in computational models and share a common structure. These learning architectures include the learning mechanisms and, eventually, exploration and evaluation strategies. A motor control function enables sound production and sensory response models either how sound is perceived or how it shapes the reward. The inputs and outputs of these functions lie (1)~in the motor space (motor parameters’ space), (2)~in the sensory space (real sounds) and (3)~either in the perceptual space (a low dimensional representation of the sound) or in the internal representation of goals (a non-perceptual representation of the target sound).The first model proposed in this thesis is a theoretical inverse model based on a simplified vocal learning model where the sensory space coincides with the motor space (i.e., there is no sound production). Such a simplification allows us to investigate how to introduce biological assumptions (e.g. non-linearity response) into a vocal learning model and which parameters influence the computational power of the model the most. The influence of the sharpness of auditory selectivity and the motor dimension are discussed.To have a complete model (which is able to perceive and produce sound), we needed a motor control function capable of reproducing sounds similar to real data (e.g. recordings of adult canaries). We analyzed the capability of WaveGAN (a Generative Adversarial Network) to provide a generator model able to produce realistic canary songs. In this generator model, the input space becomes the latent space after training and allows the representation of a high-dimensional dataset in a lower-dimensional manifold. We obtained realistic canary sounds using only three dimensions for the latent space. Among other results, quantitative and qualitative analyses demonstrate the interpolation abilities of the model, which suggests that the generator model we studied can be used as a motor function in a vocal learning model.The second version of the sensorimotor model is a complete vocal learning model with a full action-perception loop (i.e., it includes motor space, sensory space, and perceptual space). The sound production is performed by the GAN generator previously obtained. A recurrent neural network classifying syllables serves as the perceptual sensory response. Similar to the first model, the mapping between the perceptual space and the motor space is learned via an inverse model. Preliminary results show the influence of the learning rate when different sensory response functions are implemented
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Moral, Hakan. "Modeling Of Activated Sludge Process By Using Artificial Neural Networks." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605733/index.pdf.
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Current activated sludge models are deterministic in character and are constructed by basing on the fundamental biokinetics. However, calibrating these models are extremely time consuming and laborious. An easy-to-calibrate and user friendly computer model, one of the artificial intelligence techniques, Artificial Neural Networks (ANNs) were used in this study. These models can be used not only directly as a substitute for deterministic models but also can be plugged into the system as error predictors.
Three systems were modeled by using ANN models. Initially, a hypothetical wastewater treatment plant constructed in Simulation of Single-Sludge Processes for Carbon Oxidation, Nitrification &Denitrification (SSSP) program, which is an implementation of Activated Sludge Model No 1 (ASM1), was used as the source of input and output data. The other systems were actual treatment plants, Ankara Central Wastewater Treatment Plant, ACWTP and iskenderun Wastewater Treatment Plant (IskWTP). A sensitivity analysis was applied for the hypothetical plant for both of the model simulation results obtained by the SSSP program and the developed ANN model. Sensitivity tests carried out by comparing the responses of the two models indicated parallel sensitivities. In hypothetical WWTP modeling, the highest correlation coefficient obtained with ANN model versus SSSP was about 0.980. By using actual data from IskWTP the best fit obtained by the ANN model yielded R value of 0.795 can be considered very high with such a noisy data. Similarly, ACWTP the R value obtained was 0.688, where accuracy of fit is debatable.
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Cretu, Ana-Maria. "Neural network modeling of three-dimensional objects for virtualized reality applications." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26465.
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This thesis presents a critical comparison between three neural architectures for 3D object representation in terms of purpose, computational cost, complexity, conformance and convenience, ease of manipulation and potential uses in the context of virtualized reality. Starting from a pointcloud that embeds the shape of the object to be modeled, a volumetric representation is obtained using a multilayered feedforward neural network or a surface representation using either the self-organizing map or the neural gas network. The representation provided by the neural networks is simple, compact and accurate. The models can be easily transformed in size, position (affine transformations) and shape (deformation). Some potential uses of the presented architectures in the context of virtualized reality are for the modeling of set operations and object morphing, for the detection of objects collision and for object recognition, object motion estimation and segmentation.
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Khodaverdi, Afaghi Mahtab. "Application of artificial neural network modeling in thermal process calculations of canned foods." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0033/MQ64381.pdf.
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Bajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.
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Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales.Program: Magisterutbildning i informatik
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ABU, OMAR OSAMA YOUSEF. "ARTIFICIAL NEURAL NETWORK MODELING OF FLOW STRESS RESPONSE AS A FUNCTION OF DISLOCATION MICROSTRUCTURES." MSSTATE, 2007. http://sun.library.msstate.edu/ETD-db/theses/available/etd-06222007-112519/.
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An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of a material. This ANN model establishes a relationship between flow stress and dislocation structure content. The density of geometrically necessary dislocations (GNDs) was calculated based on analysis of local lattice curvature evolution. The model includes essential statistical measures extracted from the distributions of dislocation microstructures, including substructure cell size, wall thickness, and GND density as the input variables to the ANN model. The model was able to successfully predict the flow stress of aluminum alloy 6022 as a function of its dislocation structure content. Furthermore, a sensitivity analysis was performed to identify the significance of individual dislocation parameters on the flow stress. The results show that an ANN model can be used to calibrate and predict inelastic material properties that are often cumbersome to model with rigorous dislocation-based plasticity models.
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Kessler, Brian Scott. "Development of an integrated approach combining artificial neural network material based on modeling with finite element analysis of forming processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4164.
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Thesis (Ph. D.)--University of Missouri-Columbia, 2005.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 24, 2006) Vita. Includes bibliographical references.
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Yang, Zhiguo. "MODELING LARGE-SCALE CROSS EFFECT IN CO-PURCHASE INCIDENCE: COMPARING ARTIFICIAL NEURAL NETWORK TECHNIQUES AND MULTIVARIATE PROBIT MODELING." UKnowledge, 2015. http://uknowledge.uky.edu/busadmin_etds/6.
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This dissertation examines cross-category effects in consumer purchases from the big data and analytics perspectives. It uses data from Nielsen Consumer Panel and Scanner databases for its investigations. With big data analytics it becomes possible to examine the cross effects of many product categories on each other. The number of categories whose cross effects are studied is called category scale or just scale in this dissertation. The larger the category scale the higher the number of categories whose cross effects are studied. This dissertation extends research on models of cross effects by (1) examining the performance of MVP model across category scale; (2) customizing artificial neural network (ANN) techniques for large-scale cross effect analysis; (3) examining the performance of ANN across scale; and (4) developing a conceptual model of spending habits as a source of cross effect heterogeneity. The results provide researchers and managers new knowledge about using the two techniques in large category scale settings The computational capabilities required by MVP models grow exponentially with scale and thus are more significantly limited by computational capabilities than are ANN models. In our experiments, for scales 4, 8, 16 and 32, using Nielsen data, MVP models could not be estimated using baskets with 16 and more categories. We attempted to and could calibrate ANN models, on the other hand, for both scales 16 and 32. Surprisingly, the predictive results of ANN models exhibit an inverted U relationship with scale. As an ancillary result we provide a method for determining the existence and extent of non-linear own and cross category effects on likelihood of purchase of a category using ANN models. Besides our empirical studies, we draw on the mental budgeting model and impulsive spending literature, to provide a conceptualization of consumer spending habits as a source of heterogeneity in cross effect context. Finally, after a discussion of conclusions and limitations, the dissertation concludes with a discussion of open questions for future research.
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Fang, Zhufeng. "USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/193439.
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We use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates of soil texture and bulk density data across the site are then used as input into a pedotransfer function to produce estimates of soil hydraulic parameter (saturated and residual water content θs and θr, saturated hydraulic conductivity Ks, van Genuchten parameters αand n) distributions across the site in three dimensions. We compare these estimates with laboratory-measured values of these same hydraulic parameters and find the estimated parameters match the measured well for θs, n and Ks but not well for θr nor α, while some measured extreme values are not captured. Finally the estimated soil hydraulic parameters are put into a numerical simulator to test the reliability of the models. Resultant simulated water contents do not agree well with those observed, indicating inverse calibration is required to improve the modeling performance. The results of this research conform to a previous work by Wang et al. at 2003. Also this research covers the gaps of Wang’s work in sense of generating 3-D heterogeneous fields of soil texture and bulk density by cokriging and providing comparisons between estimated and measured soil hydraulic parameters with new field and laboratory measurements of water retentions datasets.
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Thekkudan, Travis Francis. "Calibration of an Artificial Neural Network for Predicting Development in Montgomery County, Virginia: 1992-2001." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33732.
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This study evaluates the effectiveness of an artificial neural network (ANN) to predict locations of urban change at a countywide level by testing various calibrations of the Land Transformation Model (LTM). It utilizes the Stuttgart Neural Network Simulator (SNNS), a common medium through which ANNs run a back-propagation algorithm, to execute neural net training.
This research explores the dynamics of socioeconomic and biophysical variables (derived from the 1990 Comprehensive Plan) and how they affect model calibration for Montgomery County, Virginia. Using NLCD Retrofit Land Use data for 1992 and 2001 as base layers for urban change, we assess the sensitivity of the model with policy-influenced variables from data layers representing road accessibility, proximity to urban lands, distance from urban expansion areas, slopes, and soils. Aerial imagery from 1991 and 2002 was used to visually assess changes at site-specific locations.
Results show a percent correct metric (PCM) of 32.843% and a Kappa value of 0.319. A relative operating characteristic (ROC) value of 0.660 showed that the model predicted locations of change better than chance (0.50). It performs consistently when compared to PCMs from a logistic regression model, 31.752%, and LTMs run in the absence of each driving variable ranging 27.971% â 33.494%. These figures are similar to results from other land use and land cover change (LUCC) studies sharing comparable landscape characteristics. Prediction maps resulting from LTM forecasts driven by the six variables tested provide a satisfactory means for forecasting change inside of dense urban areas and urban fringes for countywide urban planning.Master of Science
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Mudunuru, Venkateswara Rao. "Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6120.
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Survival analysis today is widely implemented in the fields of medical and biological sciences, social sciences, econometrics, and engineering. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. Events or outcomes are defined by a transition from one discrete state to another at an instantaneous moment in time. In the recent years, research in the area of survival analysis has increased greatly because of its large usage in areas related to bio sciences and the pharmaceutical studies. After identifying the probability density function that best characterizes the tumors and survival times of breast cancer women, one purpose of this research is to compare the efficiency between competing estimators of the survival function. Our study includes evaluation of parametric, semi-parametric and nonparametric analysis of probability survival models.
Artificial Neural Networks (ANNs), recently applied to a number of clinical, business, forecasting, time series prediction, and other applications, are computational systems consisting of artificial neurons called nodes arranged in different layers with interconnecting links. The main interest in neural networks comes from their ability to approximate complex nonlinear functions. Among the available wide range of neural networks, most research is concentrated around feed forward neural networks called Multi-layer perceptrons (MLPs). One of the important components of an artificial neural network (ANN) is the activation function. This work discusses properties of activation functions in multilayer neural networks applied to breast cancer stage classification. There are a number of common activation functions in use with ANNs. The main objective in this work is to compare and analyze the performance of MLPs which has back-propagation algorithm using various activation functions for the neurons of hidden and output layers to evaluate their performance on the stage classification of breast cancer data.
Survival analysis can be considered a classification problem in which the application of machine-learning methods is appropriate. By establishing meaningful intervals of time according to a particular situation, survival analysis can easily be seen as a classification problem. Survival analysis methods deals with waiting time, i.e. time till occurrence of an event. Commonly used method to classify this sort of data is logistic regression. Sometimes, the underlying assumptions of the model are not true. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that affect the appropriateness of the model. Two such strategies, which are used nowadays frequently, are artificial neural network (ANN) and decision trees (DT), which needs a minimal assumption. DT and ANNs are widely used methodological tools based on nonlinear models. They provide a better prediction and classification results than the traditional methodologies such as logistic regression. This study aimed to compare predictions of the ANN, DT and logistic models by breast cancer survival. In this work our goal is to design models using both artificial neural networks and logistic regression that can precisely predict the output (survival) of breast cancer patients. Finally we compare the performances of these models using receiver operating characteristic (ROC) analysis.
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Thai, Shee Meng. "Neural network modelling and control of coal fired boiler plant." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/neural-network-modelling-and-control-of-coal-fired-boiler-plant(b5562ca0-e45e-44d8-aad2-ed2e3e114808).html.
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This thesis presents the development of a Neural Network Based Controller (NNBC) for chain grate stoker fired boilers. The objective of the controller was to increase combustion efficiency and maintain pollutant emissions below future medium term stringent legislation. Artificial Neural Networks (ANNs) were used to estimate future emissions from and control the combustion process. Initial tests at Casella CRE Ltd demonstrated the ability of ANNs to characterise the complex functional relationships which subsisted in the data set, and utilised previously gained knowledge to deliver predictions up to three minutes into the future. This technique was then built into a carefully designed control strategy that fundamentally mimicked the actions of an expert boiler operator, to control an industrial chain grate stoker at HM Prison Garth, Lancashire. Test results demonstrated that the developed novel NNBC was able to control the industrial stoker boiler plant to deliver the load demand whilst keeping the excess air level to a minimum. As a result the NNBC also managed to maintain the pollutant emissions within probable future limits for this size of boiler. This prototype controller would thus offer the industrial coal user with a means to improve the combustion efficiency on chain grate stokers as well as meeting medium term legislation limits on pollutant emissions that could be imposed by the European Commission.
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VACCA, GIANMARCO. "Redundancy Analysis Models with Categorical Endogenous Variables: New Estimation Techniques Based on Vector GLM and Artificial Neural Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158304.
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I modelli ad equazioni strutturali con variabili latenti hanno subito un notevole sviluppo negli ultimi anni. Partendo dai pionieri delle due macro-definizioni di modelli con variabili latenti, Covariance Structure Analysis e Component Analysis, con LISREL e PLS-PM come le tecniche più importanti, diverse estensioni e miglioramenti sono stati proposti. Inoltre, per i modelli di analisi di ridondanza, che fanno parte della Component Analysis, ma hanno solo variabili endogene osservate, sono stati proposti nuovi metodi in letteratura per affrontare più di un gruppo di variabili osservate esogene, con equazioni lineari semplici ed un'ottimizzazione unificata del problema. La critica principale, che è stata affrontata di recente in nuovi filoni di letteratura riguardanti i modelli ad equazioni strutturali, è l'incapacità parziale di questi sistemi di equazioni di modellizzare indicatori categoriali. Sono stati proposti diversi metodi a tale scopo, in PLS-PM e LISREL rispettivamente, che sfruttano metodi di Optimal Scaling o l’algoritmo EM nel processo di ottimizzazione. Per l’analisi di ridondanza, con variabili endogene solo osservate, la possibilità di estendere le procedure di stima a variabili qualitative è notevolmente meno ostacolata da restrizioni del modello, ancor di più nel modello di analisi di ridondanza estesa, con più di un blocco di variabili esogene. Questo lavoro presenta una nuova stima di modelli di analisi di ridondanza estesa in presenza di variabili endogene binarie o categoriali, con due principali tecniche di stima: Iterated Reweighed Least Squares, e Gradient Descent con backpropagation tramite reti neurali. Per questi ultimi, recenti sviluppi nei modelli ad equazioni strutturali con reti neurali saranno esaminati, e la nuova tecnica sarà quindi introdotta.Structural Equation Models with latent variables have considerably developed in recent years. Starting from the pioneers of the two most prominent ways of defining models with latent variables, namely Covariance Structure Analysis and Component Analysis, with LISREL and PLS-PM as the most famous techniques, several extensions and improvements have been put forward. Moreover, for Redundancy Analysis models, which are part of the Component Analysis framework, but have only observed endogenous variables, new methods have been proposed in literature to deal with more than one group of exogenous observed variables, with simple linear equations and a unified optimization problem. One main criticism, that has been dealt with recently in new strands of literature regarding Structural Equation Modeling, is the partial inability of these systems of linear equations to deal with categorical indicators. Several methods have been proposed, in PLS-PM and LISREL respectively, either related to Optimal Scaling, or adapting the EM algorithm to the particular case under examination. In the Redundancy Analysis framework, with only observed endogenous variables, the possibility of extending the estimation procedures to a qualitative setting is considerably less hampered by model restrictions, even more so in the Extended Redundancy Analysis model, with more than one block of exogenous variables. This work will hence present a new estimation of Extended Redundancy Analysis models in presence of binary or categorical endogenous variables, with two main estimation techniques: Iterated Reweighed Least Squares, and Gradient Descent with backpropagation in an Artificial Neural Network architecture. For the latter, recent developments in Structural Equation Models in the neural networks setting will be firstly examined, and the new technique will be subsequently introduced.
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Li, Xin. "Abstractive Representation Modeling for Image Classification." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623250959448677.
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Zhao, Yi. "Combination of Wireless sensor network and artifical neuronal network : a new approach of modeling." Thesis, Toulon, 2013. http://www.theses.fr/2013TOUL0013/document.
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Face à la limitation de la modélisation paramétrique, nous avons proposé dans cette thèse une procédure standard pour combiner les données reçues a partir de Réseaux de capteurs sans fils (WSN) pour modéliser a l'aide de Réseaux de Neurones Artificiels (ANN). Des expériences sur la modélisation thermique ont permis de démontrer que la combinaison de WSN et d'ANN est capable de produire des modèles thermiques précis. Une nouvelle méthode de formation "Multi-Pattern Cross Training" (MPCT) a également été introduite dans ce travail. Cette méthode permet de fusionner les informations provenant de différentes sources de données d'entraînements indépendants (patterns) en un seul modèle ANN. D'autres expériences ont montré que les modèles formés par la méthode MPCT fournissent une meilleure performance de généralisation et que les erreurs de prévision sont réduites. De plus, le modèle de réseau neuronal basé sur la méthode MPCT a montré des avantages importants dans le multi-variable Model Prédictive Control (MPC). Les simulations numériques indiquent que le MPC basé sur le MPCT a surpassé le MPC multi-modèles au niveau de l'efficacité du contrôleA Wireless Sensor Network (WSN) consisting of autonomous sensor nodes can provide a rich stream of sensor data representing physical measurements. A well built Artificial Neural Network (ANN) model needs sufficient training data sources. Facing the limitation of traditional parametric modeling, this paper proposes a standard procedure of combining ANN and WSN sensor data in modeling. Experiments on indoor thermal modeling demonstrated that WSN together with ANN can lead to accurate fine grained indoor thermal models. A new training method "Multi-Pattern Cross Training" (MPCT) is also introduced in this work. This training method makes it possible to merge knowledge from different independent training data sources (patterns) into a single ANN model. Further experiments demonstrated that models trained by MPCT method shew better generalization performance and lower prediction errors in tests using different data sets. Also the MPCT based Neural Network Model has shown advantages in multi-variable Neural Network based Model Predictive Control (NNMPC). Software simulation and application results indicate that MPCT implemented NNMPC outperformed Multiple models based NNMPC in online control efficiency
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Narayanan, Pavanesh. "Sensor-less Control of Shape Memory Alloy Using Artificial Neural Network and Variable Structure Controller." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1416501021.
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Bataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction." Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.
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Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.
This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.
When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.
The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.
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CHANG, DYI-HUEY. "ANALYSIS AND MODELING OF SPACE-TIME ORGANIZATION OF REMOTELY SENSED SOIL MOISTURE." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1011125319.
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Fletcher, Eric Matthew. "FE-ANN based modeling of 3D simple reinforced concrete girders for objective structural health evaluation." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/32497.
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Master of ScienceDepartment of Civil Engineering
Hayder A. Rasheed
The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the issue, economic strains limit the resources available for repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proved to be a cost-effective method for detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of this method suffers due to its reliance on the availability and experience of qualified personnel performing largely qualitative damage evaluations. The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection methods by developing a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate. Simply-supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. Two feedforward ANNs utilizing backpropagation learning algorithms were then trained on the FE model database with beam properties serving as inputs for both neural networks. The outputs for the first network consisted of the nodal stiffness ratios, and the sole output for the second ANN was a health index parameter, computed by normalizing the area under the stiffness ratio profile over the span length of the beam. The ANNs achieved excellent prediction accuracies with coefficients of determination (R²) exceeding 0.99 for both networks. Additional FE models were created to further assess the networks’ prediction capabilities on data not utilized in the training process. The ANNs displayed good prediction accuracies (R² > 0.8) even when predicting damage levels in beams with geometric, material, and cracking parameters dissimilar from those found in the training database. A touch-enabled user interface was developed to allow the ANN models to be utilized for on-site damage evaluations. The results of this study indicate that application of ANNs with FE modeling shows great promise in SHM for damage evaluation.
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Sen, Baris Ali. "Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31757.
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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010.Committee Chair: Menon, Suresh; Committee Member: Lieuwen, Timothy C.; Committee Member: Sankar, Lakshmi; Committee Member: Stoesser, Thorsten; Committee Member: Syed, Saadat; Committee Member: Walker, Mitchell. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Rosquist, Parker Gary. "Modeling Three Dimensional Ground Reaction Force Using Nanocomposite Piezoresponsive Foam Sensors." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6390.
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Three dimensional (3D) ground reaction force (GRF) are an essential component for gait analysis. Current methods for measuring 3D GRF involve using a stationary force plate embedded in the ground, which captures the forces as subjects walk across the platform. This approach has several limitations, a few being: it can only capture a few steps at a time, it is expensive to purchase and maintain, it can't reflect forces caused by natural uneven surfaces, etc. Previous research has attempted to develop wearable force sensors to overcome these problems; however, these endeavors have resulted in devices that are expensive, bulky, and fail to accurately measure forces when compared to static force plates. This thesis presents the implementation and validation of novel nanocomposite piezoresponsive foam (NCPF) sensors for measuring 3D GRF. Four NCPF sensors were embedded in a shoe sole at four locations: heel, arch, ball, and toe. The signals from each sensor were used in a functional data analysis (FDA) to develop a statistical model for estimating 3D GRF. The process of calibrating the sensors to model GRF was validated through a study where 9 subjects (4 females, 5 males) walked on a force-sensing treadmill for two minutes. Two approaches were used to model the GRF response. The first approach was based on functional decomposition of the data. Using a tenfold cross validation process a statistical model was developed for each subject with the ability to predict walking 3D GRF with less than 7% error. The second approach used machine learning to model 3D GRF. Using the same walking data for the statistical models, an artificial neural network (ANN) was used to create subject-specific models that could predict walking 3D GRF with less than 11% error. The predictive capabilities of ANN were tested using a pilot study where a single subject performed a calibration procedure by running at seven different speeds for thirty seconds each on the force-sensing treadmill. This calibration data was used to train a model, which was then used to estimate vertical GRF (VGRF) for three additional running trials at randomly selected speeds from within the calibration range. The ANN model was able to predict VGRF for three running speeds after calibration with less than 4% error. The use of NCPF sensors to estimate 3D GRF was shown to be a viable alternative to static force plates. It is recommended, in future work, that 3D GRF and subsequent sensor data be collected from a large sample of subjects to create a baseline of 3D GRF characteristics for a population that will enable a robust cross-subject model capable of performing real-time ground reaction force analysis across the general population, which will greatly benefit our understanding of human gait.
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Ramalho, José Pinto. "Oxicorte: estudo da transferência de calor e modelamento por redes neurais artificiais de variáveis do processo." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/3/3133/tde-30092008-150619/.
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O oxicorte produz superfícies que variam entre um padrão semelhante à usinagem até outro em que o corte é praticamente sem qualidade. Além das condições de equipamentos e habilidade de operadores, estas possibilidades são conseqüências da correta seleção de parâmetros e variáveis de trabalho. O processo baseia-se numa reação química fortemente exotérmica, que gera parte de calor necessário para sua ocorrência juntamente com o restante do calor proveniente da chama do maçarico. A proporção entre estes valores é fortemente dependente, entre outros fatores, da espessura do material utilizado. Este trabalho mostra como calcular a quantidade de energia gerada no oxicorte, com duas metodologias de diferentes autores, estuda de que maneira fatores como a variação da concentração do oxigênio e a temperatura inicial das chapas cortadas podem variar o balanço térmico e simula, com a utilização de Redes Neurais Artificiais, alguns dos dados necessários para a realização destes cálculos. Para isto foram cortadas chapas de aço carbono ASTM A36 de 12,7 a 50,8 mm, com diferentes concentrações de O2 (99,5% e 99,95%) e diferentes temperaturas de pré-aquecimento das chapas (30 e 230±30ºC). As superfícies cortadas foram caracterizadas, os óxidos produzidos identificados e os resultados foram correlacionados com o uso de tratamento matemático e técnicas de inteligência artificial. Para a realização do trabalho alguns aspectos não existentes em literatura foram superados como o desenvolvimento de uma metodologia para a caracterização dos óxidos de Fe por meio de difração de raios X com o método de Rietveld, a utilização de redes neurais artificiais para estimativa de resultados no processo oxicorte e a comparação entre diferentes redes neurais artificiais, que são também aspectos inéditos apresentados nos sete artigos técnicos publicados no decorrer deste trabalho. Os resultados apresentam: uma metodologia para a análise da eficiência energética do processo, o desenvolvimento de técnicas que, com o emprego de inteligência artificial simulam o comportamento de aspectos do processo, o que por fim possibilita a simulação da análise de sua eficiência energética.Oxygen cutting process produces surfaces that vary from a machine cut finishing to one of virtually no quality at all. Besides equipment conditions and operators\' skills, these possibilities result from the correct selection of work parameters and variables. The process is based on a highly exothermic chemical reaction that generates part of the heat needed for its occurrence, along with the rest of heat resultant from the flame of the blowpipe. The ratio between these values depends highly on the thickness of the material used. This work shows how to calculate the amount of energy generated in the cutting process. Based on two methodologies of different authors, this research studies how factors such as the change in the oxygen concentration and the pre heating temperature of plates can vary the heat balance and simulates, with the use of Artificial Neural Networks, some of the data needed to perform these calculations. ASTM A36 carbon steel plates, from 12.7 to 50.8 mm thick, with different oxygen concentration (99,5% e 99,95%) and preheating temperatures (30 and 230 ±30ºC) were cut. The cut surfaces and the produced oxides were characterized and the results were correlated with the use of mathematical treatment and artificial intelligence techniques. In order to carry out this work some previously inexistent aspects in literature have been developed, such as a Fe oxides characterization methodology with X-ray diffraction and Rietveld method; the use of artificial neural networks to simulate the results in the oxygen cutting process and the comparison between different artificial neural networks, which are unpublished aspects of this work that can be seen in seven technical papers published while this work was in progress. Results show: a methodology for the analysis of the energy efficiency of the process; the development of techniques that, together with artificial intelligence, simulate the results of aspects of the process; which finally allows the simulation analysis of the energy efficiency of the process.
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Sarlak, Nermin. "Evaluation And Modeling Of Streamflow Data: Entropy Method, Autoregressive Models With Asymmetric Innovations And Artificial Neural Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606135/index.pdf.
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In the first part of this study, two entropy methods under different distribution assumptions are examined on a network of stream gauging stations located in Kizilirmak Basin to rank the stations according to their level of importance. The stations are ranked by using two different entropy methods under different distributions. Thus, showing the effect of the distribution type on both entropy methods is aimed.
In the second part of this study, autoregressive models with asymmetric innovations and an artificial neural network model are introduced. Autoregressive models (AR) which have been developed in hydrology are based on several assumptions. The normality assumption for the innovations of AR models is investigated in this study. The main reason of making this assumption in the autoregressive models established is the difficulties faced in finding the model parameters under the distributions other than the normal distributions. From this point of view, introduction of the modified maximum likelihood procedure developed by Tiku et. al. (1996) in estimation of the autoregressive model parameters having non-normally distributed residual series, in the area of hydrology has been aimed. It is also important to consider how the autoregressive model parameters having skewed distributions could be estimated.
Besides these autoregressive models, the artificial neural network (ANN) model was also constructed for annual and monthly hydrologic time series due to its advantages such as no statistical distribution and no linearity assumptions.
The models considered are applied to annual and monthly streamflow data obtained from five streamflow gauging stations in Kizilirmak Basin. It is shown that AR(1) model with Weibull innovations provides best solutions for annual series and AR(1) model with generalized logistic innovations provides best solution for monthly as compared with the results of artificial neural network models.
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Tavares, Guilherme Farias. "Modelagem matemática e sistemas inteligentes para predição do comportamento alimentar de suínos nas fases de crescimento e terminação." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-28072017-082242/.
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A suinocultura é uma atividade de grande importância em termos mundiais e de Brasil. Entretanto, por serem animais homeotérmicos, algumas alterações no ambiente térmico de alojamento podem alterar suas respostas fisiológicas e comportamentais para manutenção da temperatura interna. Portanto, o objetivo dessa pesquisa foi avaliar o comportamento alimentar de suínos, mediante a influência do ambiente térmico, nas fases de crescimento e terminação para diferentes linhagens comerciais e sexo. Além disso, buscou-se o desenvolvimento de modelos matemáticos e sistemas inteligentes para predição do tempo em alimentação (TM, min dia-1) dos suínos. Os dados foram coletados em uma granja experimental de suínos, localizada na cidade de Clay Center, Nebraska, Estados Unidos. O período experimental contemplou duas estações durante o ano 2015/2016 (verão e inverno), totalizando 63 dias (9 semanas) de informações coletadas para cada estação. Os animais alojados foram de três linhagens comerciais distintas: Landrace, Duroc e Yorkshire. Cada baia apresentava composição mista, sendo alojados 40 animais de diferentes linhagens comerciais e sexo. No total, foram confinados 240 animais, sendo 80 animais para cada linhagem comercial entre machos castrados e fêmeas. Foram registrados dados de temperatura do ar (Tar, °C), temperatura do ponto de orvalho (Tpo, °C) e umidade relativa do ar (UR, %) a cada 5 minutos no interior da instalação. Para TM, os dados foram coletados e registrados a cada 20 segundos por meio de um sistema de coleta de dados por rádio frequência. O conforto térmico foi analisado a partir do Índice de Temperatura e Umidade (ITU) e a Entalpia Específica (H, kJ kg-1 de ar seco). Para avaliar a relação entre o ambiente térmico e TM, foi utilizada estatística multivariada por meio de análise de componentes principais (ACP) e agrupamento para obtenção de padrões e seleção de variáveis para entrada nos modelos. O modelo fuzzy e as redes neurais artificias foram desenvolvidos em ambiente MATLAB® R2015a por meio dos toolboxes Fuzzy e Neural Network, com o objetivo de predizer TM, tendo como variáveis de entrada: linhagem comercial, sexo, idade e ITU. De uma maneira geral, as médias de Tar estiveram dentro da zona de termoneutralidade (ZCT) em todo período experimental, sendo que apenas a UR apresentou valores abaixo da UR crítica inferior. Para o ITU, apenas no verão foram encontrados valores acima da ZCT, entretanto, esses valores estiveram abaixo do ITU crítico superior. Diante da análise dos resultados, pôde-se observar em relação ao comportamento alimentar, que a fêmea Landrace apresentou o menor tempo em alimentação com médias de 42,19 min dia-1 e 43,73 min dia-1 para o inverno e verão, respectivamente, seguido do macho castrado de mesma linhagem. Enquanto as demais linhagens apresentaram valores acima de 60 min dia-1. Não foi observado correlação linear significativa entre o ambiente térmico e TM uma vez que os animais estiveram dentro de sua ZCT ao longo de todo período experimental, indicando que o comportamento alimentar foi influenciado principalmente pelos fatores homeostáticos e cognitivos-hedônicos. A estatística multivariada dividiu os animais em 8 grupos. Foi observado que animais de linhagens e sexos distintos se comportaram da mesma maneira, dificultando a modelagem matemática. Entretanto, alguns grupos apresentaram maior quantidade de animais de determinada linhagem e sexo, sendo estes utilizados como \"grupos padrão\" para o desenvolvimento do modelo fuzzy e a rede neural artificial. O modelo fuzzy apresentou R2 de 0,858 quando utilizado os dados do grupo padrão, entretanto, para todos os valores o R2 foi de 0,549. Já a rede neural apresentou um R2 de 0,611 para os dados completos e R2 de 0,914 para o \"grupo padrão\". Portanto, a rede neural artificial mostrou-se como uma ferramenta de maior precisão e acurácia na predição do comportamento alimentar de suínos nas fases de crescimento e terminação.The swine production in an activity of great importance to Brazil and to the world. However, because they maintain a constant body temperature and, alterations in the thermic accommodation environment can directly affect their physiological and behavioral responses for maintaining the internal temperature. Thus, the objective of this study was to access the feeding behavior of growing-finishing pigs of different sirelines and gender and its relationship with climate variables (thermic environment). Furthermore, mathematical models based on classic logic was developed as well as an intelligent system for predicting the total time spent eating (TM, min day -1). The data was collected in an experimental farm located in Clay Center, Nebraska, United States. The experimental period contemplated two seasons (summer and winter), totalizing 63 days (9 weeks) of information collected for each season. The housed animals were from three different commercial sirelines: Landrace, Duroc and Yorkshire. Each pen presented a mix composition, being housed 40 animals of different sirelines and gender. In total, there were 240 housed animals, being 80 animals for each sireline among barrows and gilts. The data registered were air temperature (Tar, °C), dew point temperature (Tpo, °C) and relative humidity of the air (UR, %) every 5 minutes inside the facility. For TM, the data were collected and registered every 20 seconds by a radio frequency data collection system. The thermal comfort was analyzed from the Temperature and Humidity Index (THI) and Specific Enthalpy (H, kJ kg-1 of dry air). In order to evaluate the relationship between the thermic environment and TM, the multivariate statistics through principal component analysis (PCA) and grouping was utilized for obtaining the selection standards of variables to enter in the models. The fuzzy model and the artificial neural networks were developed in a MATLAB® R2015a environment through the Fuzzy and the Neural Network toolboxes with the objective to predict TM, having as entry variables: sireline, gender, age and THI. On the whole, the Tar averages were inside the thermoneutral zone (ZCT), however, these values were below the superior critic THI. In the face of the results analysis, it could be observed in ration to the feeding behavior that the Landrace gilt presented the shortest time eating with averages of 42.19 min day-1 and 43.73 min day-1 for winter and summer respectively followed by the barrow from the same sireline, while the other sirelines presented values above 60 min day-1. It was not observed a significative linear correlation between the thermic environment and TM once the animals were inside their ZCT throughout all the experimentation period, indicating that the feeding behavior was influenced mainly by the homeostatic and cognitivehedonic factors. The multivariate statistics divided the animals in 8 groups, being observed that animals of different sirelines and gender behave the same way throughout the experimentation period, making the mathematical modeling difficult. However, some groups presented a bigger amount of animals of determined sireline and gender, being utilized as \"standard groups\" for the development of the fuzzy model and the artificial neural network. The fuzzy model presented an R2 of 0,858 when utilizing the \"standard group\" data, however, for all the values the R2 was 0.549. In the other hand the neural network presented an R2 of 0.611 for the complete data and an R2 of 0.914 for the \"standard group\". Thus, the artificial neural network appeared to be a tool of a better precision and accuracy when predicting the feeding behavior of pigs on growing-finishing phases.
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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.
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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquoMatlab R 2007b&rdquo
software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
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Grose, Mitchell. "Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788.
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Maragno, Donato. "Optimization with machine learning-based modeling: an application to humanitarian food aid." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21621/.
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In this thesis, we propose a machine learning-based optimization methodology to build (part of) optimization models with a data-driven approach. This approach is useful whenever we have to model one or more relations between the decisions and their impact on the system. This kind of relationship can be challenging to model manually, and so machine learning is used to learn it through the use of data.
We demonstrate the potential of this method through a case study in which a predictive model is used to approximate the palatability scoring function in a typical diet problem formulation. First, the performance of this approach is analyzed by embedding a Linear Regression model and then by embedding a Fully Connected Neural Network.
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Scarborough, David J. (David James). "An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277752/.
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Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.
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Hopkins, Brad Michael. "A Wavelet-Based Rail Surface Defect Prediction and Detection Algorithm." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77351.
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Early detection of rail defects is necessary for preventing derailments and costly damage to the train and railway infrastructure. A rail surface flaw can quickly propagate from a small fracture to a broken rail after only a few train cars have passed over it. Rail defect detection is typically performed by using an instrumented car or a separate railway monitoring vehicle. Rail surface irregularities can be measured using accelerometers mounted to the bogie side frames or wheel axles. Typical signal processing algorithms for detecting defects within a vertical acceleration signal use a simple thresholding routine that considers only the amplitude of the signal. As a result, rail surface defects that produce low amplitude acceleration signatures may not be detected, and special track components that produce high amplitude acceleration signatures may be flagged as defects.
The focus of this research is to develop an intelligent signal processing algorithm capable of detecting and classifying various rail surface irregularities, including defects and special track components. Three algorithms are proposed and validated using data collected from an instrumented freight car. For the first two algorithms, one uses a windowed Fourier Transform while the other uses the Wavelet Transform for feature extraction. Both of these algorithms use an artificial neural network for feature classification. The third algorithm uses the Wavelet Transform to perform a regularity analysis on the signal. The algorithms are validated with the collected data and shown to out-perform the threshold-based algorithm for the same data set.
Proper training of the defect detection algorithm requires a large data set consisting of operating conditions and physical parameters. To generate this training data, a dynamic wheel-rail interaction model was developed that relates defect geometry to the side frame vertical acceleration signature. The model was generated by using combined systems dynamic modeling, and the system was solved with a developed combined lumped and distributed parameter system numerical approximation. The broken rail model was validated with real data collected from an instrumented freight car. The model was then used to train and validate the defect detection methodologies for various train and rail physical parameters and operating conditions.Ph. D.
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Källman, Jonas. "Ship Power Estimation for Marine Vessels Based on System Identification." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79248.
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Large marine vessels carry their loads all over the world. It can be a container ship carrying over 10 000 containers filled with foods, textiles and electronics or a bulk freighter carrying 400 000 tons of coal. Vessels usually have a ballast system that pumps water into ballast tanks to stabilize the vessel. The ballast system can be used to change the vessel’s trim and list angles. Trim and list are the ship equivalents of pitch and roll. By changing the trim angle the water resistance can be reduced and thus also the fuel consumption. Since the vessel is consuming a couple of hundred tons of fuel per day, a small reduction in fuel consumption can save a considerable amount of money, and it is good for the environment. In this thesis, the ship’s power consumption has been estimated using an artificial neural network, which is a mathematical model based on data. The name refers to certain structural similarities with the neural synapse system in animals. The idea with neural networks has been to create brain-like systems. For applications such as learning to interpret sensor data, artificial neural networks are an effective learning method. The goal is to estimate the ship power using a artificial neural network and then use it to calculate the trim angle, to be able to save fuel. The data used in the artificial neural network come from sensor systems mounted on a container ship sailing between Europe and Asia. The sensor data have been thoroughly preprocessed and this includes for example removing the parts when the ship is docked in harbour, data patching and synchronisation and outlier detection based on a Kalman filter. A physical model of a marine craft including wind, wave, hydrodynamic and hydrostatic effects, has also been introduced to help analyse the performance and behaviour of the artificial neural network. The artificial neural network developed in this thesis could successfully estimate the power consumption of the ship. Based on the developed networks it can be seen that the fuel consumption is reduced by trimming the ship by bow, i.e., the ship is angled so the bow is closer to the water line than the stern. The method introduced here could also be applied on other marine vessels, such as bulk freighters or tank ships.
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Utai, Katrin [Verfasser]. "Using real-time image processing and active thermography with artificial neural network modeling for non-destructive mango quality assessment / Katrin Utai." Aachen : Shaker, 2018. http://d-nb.info/118854926X/34.
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Al-Shayji, Khawla Abdul Mohsen. "Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30462.
Full textAbstract:
This dissertation introduces desalination processes in general and multistage flash (MSF) and reverse osmosis (RO) in particular. It presents the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction with statistical techniques to identify the major independent variables to optimize the process performance.
In contrast to several recent studies, this work utilizes actual operating data (not simulated) from a large-scale commercial MSF desalination plant (48 million gallonsper day capacity, MGPD) and RO plant (15 MGPD) located in Kuwait and the Kingdom of Saudi Arabia, respectively. We apply Neural Works Professional II/Plus (NeuralWare, 1993) and SAS (SAS Institute Inc., 1996) software to accomplish this task.
This dissertation demonstrates how to apply modular and equation-solving approaches for steady-state and dynamic simulations of large-scale commercial MSF desalination plants using ASPEN PLUS (Advanced System for Process Engineering PLUS) and SPEEDUP (Simulation Program for Evaluation and Evolutionary Design of Unsteady Processes) marketed by Aspen Technology, Cambridge, MA.
This work illustrates the development of an optimal operating envelope for achieving a stable operation of a commercial MSF desalination plant using the SPEEDUP model. We then discuss model linearization around nominal operating conditions and arrive at pairing schemes for manipulated and controlled variables by interaction analysis. Finally, this dissertation describes our experience in applying a commercial software, DynaPLUS, for combined steady-state and dynamic simulations of a commercial MSF desalination plant.
This dissertation is unique and significant in that it reports the first comprehensive study of predictive modeling, simulation, and optimization of large-scale commercial desalination plants. It is the first detailed and comparative study of commercial desalination plants using both artificial intelligence and computer-aided design techniques. The resulting models are able to reproduce accurately the actual operating data and to predict the optimal operating conditions of commercial desalination plants.Ph. D.
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More, Priyanka Ramesh. "Using Machine Learning to predict water table levels in a wet prairie in Northwest Ohio." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1535334208410497.
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Aragao, Almeida Salvio Jr. "Modeling of Concrete Anchors Supporting Non-Structural Components Subjected toStrong Wind and Adverse Environmental Conditions." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1564764404011142.
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Brown, Michael Kenneth. "Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1350307168.
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Gummadi, Jayaram. "A Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376567646.
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