Academic literature on the topic 'Markov model-based methods'
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Journal articles on the topic "Markov model-based methods"
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Liao, T. Warren, Guogang Hua, J. Qu, and P. J. Blau. "GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS." Machining Science and Technology 10, no. 4 (December 2006): 511–38. http://dx.doi.org/10.1080/10910340600996175.
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Zang, Xiaohui, and Chengming Bai. "Markov Model-Based Learning Aid for Students’ Civics Course." Mobile Information Systems 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/6026875.
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Since the 19th National Congress, students’ ideological education has become more and more one of the national priorities, so the Civics course has become one of the essential compulsory courses for students at all stages of school and university, and the learning methods of Civics course have also become a hot issue of concern to students, which makes the learning process of supplementary learning methods very important. In this paper, a Markov model was developed to calculate the probability transfer matrix and predict the supplementary learning methods used by students. This paper also establishes a Markov model to predict the frequency of students’ online classroom learning at different stages, and it is found that, in the future, more and more students will use the Internet for their Civics course assisted learning; therefore, it is very important to establish a perfect Civics course online assisted learning platform, and this paper also puts forward some suggestions for establishing a Civics course online assisted learning system, which provides some methods for subsequent students’ Civics course learning. This paper also proposes some suggestions for establishing a web-assisted learning system for Civics courses and provides some methods for subsequent student learning in Civics courses.
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Zhang, Huifang, Wangsen Lan, and Desheng Zhang. "Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model." Security and Communication Networks 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/3255006.
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In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.
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Lin, Bingjie, Jie Cheng, Jiahui Wei, and Ang Xia. "A Sensing Method of Network Security Situation Based on Markov Game Model." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 531–36. http://dx.doi.org/10.46300/9106.2022.16.66.
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The sensing of network security situation (NSS) has become a hot issue. This paper first describes the basic principle of Markov model and then the necessary and sufficient conditions for the application of Markov game model. And finally, taking fuzzy comprehensive evaluation model as the theoretical basis, this paper analyzes the application fields of the sensing method of NSS with Markov game model from the aspects of network randomness, non-cooperative and dynamic evolution. Evaluation results show that the sensing method of NSS with Markov game model is best for financial field, followed by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.
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Bouton, Maxime, Jana Tumova, and Mykel J. Kochenderfer. "Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10061–68. http://dx.doi.org/10.1609/aaai.v34i06.6563.
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Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.
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Bertolami, Roman, and Horst Bunke. "Hidden Markov model-based ensemble methods for offline handwritten text line recognition." Pattern Recognition 41, no. 11 (November 2008): 3452–60. http://dx.doi.org/10.1016/j.patcog.2008.04.003.
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Dong, Lei, Wei-min Li, Ching-Hsin Wang, and Kuo-Ping Lin. "Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 5 (August 3, 2019): 646–61. http://dx.doi.org/10.1177/0959651819866281.
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In this study, we developed a fault classification model that combines a coupled hidden Markov model based on multi-channel information fusion with a minimum intra-class distance algorithm. This model relies on statistical features in the current time domain, which are the easiest features to extract for clustering. First, an algorithm is used to select and sequence the statistical features with the minimum intra-class distance in order to form feature vectors, which in turn enhance inter-class discrimination and feature reduction. Following reduction, the coupled hidden Markov model is used to perform classification. The coupled hidden Markov model was shown to reflect the coupling relationships between and among channels. We evaluated the efficacy of the proposed scheme by applying it to the diagnosis of faults in a gyro motor in three groups of experiments. Our results were compared with those obtained using a single-chain hidden Markov model and other intelligent fault diagnosis methods. The proposed scheme outperformed the other methods in terms of correct diagnosis rate, fluctuations in correct diagnosis rate, and excellent robustness against the effects of interference.
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Zhang, Wei, Zhaoxiang Qin, and Jun Tang. "Economic Benefit Analysis of Medical Tourism Industry Based on Markov Model." Journal of Mathematics 2022 (March 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/6401796.
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To study the impact of the medical Tourism Industry on regional economic performance, a Markov prediction method was proposed. An improved Markov chain combination forecasting method was established by analyzing the economy of healthcare tourism industry through Markov chain forecasting method and various processing methods for economic results of different years. The research results show that healthcare tourism industry service is a new and highly potential tourism product service. It can generate significant economic and social benefits. The value and market size of healthcare tourism industry is analyzed and studied by using Markov model to explore the complementary roles of Medical and Tourism, which helps to predict the development of market size and benefits. The model results are also analyzed and calculated. The benefits and scale of the development of the healthcare tourism industry are evaluated by combining the actual data situation and development conditions in each year.
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Wei, An Shi. "The Construction of Piano Teaching Innovation Model Based on Full-depth Learning." International Journal of Emerging Technologies in Learning (iJET) 13, no. 03 (March 5, 2018): 32. http://dx.doi.org/10.3991/ijet.v13i03.8369.
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This paper presents a new method of building piano teaching innovation model based on full depth learning. The model includes the following main steps: (1) The normal behavior samples of piano teaching are obtained by the method of spectral clustering based on dynamic time homing (DTW), and the hidden Markov model; (2) to further train the hidden Markov model parameters in a large sample by means of iterative learning; (3) to use the maximum a posteriori (MAP) adaptive method to estimate the Hidden Markov Model (HMM) of the piano teaching behavior in a supervised manner; (4) The behavioral hidden Markov topology model is established for model estimation. The main features of this method are: it can automatically select the kinds and samples of the normal behavior patterns of piano teaching to establish an innovative model of piano teaching; the problem of under-learning of Hidden Markov Model (HMM) can be avoided in the case of fewer samples. The experimental results show that this model is more reliable than other methods.
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Huan, Hongyan, and Qing-mei Tan. "The forecast of cultivate land quantity based on Grey-Markov model." Grey Systems: Theory and Application 5, no. 1 (February 2, 2015): 127–36. http://dx.doi.org/10.1108/gs-03-2012-0016.
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Purpose – The purpose of this paper is to employ the Grey-Markov Chain Model for the scale prediction of cultivated land and took an empirical research with the case of Jiangsu province. Design/methodology/approach – Along with China’s industrialization and urbanization accelerated, a large number of cultivated land converse into construction land. The change of utilization of cultivated land concerns national food security and sustainable development of economy and society. Due to the fact that the different investigation methods of arable land usually cause a uncertain. The Grey-Markov model combines the Grey GM(1,1) and Markov chain, with two advantages of dealing with poor information and long-term and volatile series. A numeric example of scale prediction of cultivated land in Jiangsu province is also computed in the third part of the paper. Findings – The results show that the Grey-Markov Chain Model has a higher prediction accuracy compared with GM (1,1), which is a reliable guarantee for the change of cultivated land resources. Practical implications – The forecast of cultivated land can provide useful information for the general land use planning. Originality/value – The paper confirmed the feasibility of the Grey-Markov model in scale prediction of cultivated land.
Dissertations / Theses on the topic "Markov model-based methods"
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GOMES, Adriano José Oliveira. "Systematic model-based safety assessment via probabilistic model checking." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2651.
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Previous issue date: 2010Faculdade de Amparo à Ciência e Tecnologia do Estado de Pernambuco
A análise da segurança (Safety Assessment) é um processo bem conhecido que serve para garantir que as restrições de segurança de um sistema crítico sejam cumpridas. Dentro dele, a análise de segurança quantitativa lida com essas restrições em um contexto numérico (probabilístico). Os métodos de análise de segurança, como a tradicional Fault Tree Analysis (FTA), são utilizados no processo de avaliação da segurança quantitativo, seguindo as diretrizes de certificação (por exemplo, a ARP4761 Guia de Práticas Recomendadas da Aviação). No entanto, este método é geralmente custoso e requer muito tempo e esforço para validar um sistema como um todo, uma vez que para uma aeronave chegam a ser construídas, em média, 10.000 árvores de falha e também porque dependem fortemente das habilidades humanas para lidar com suas limitações temporais que restringem o âmbito e o nível de detalhe que a análise e os resultados podem alcançar. Por outro lado, as autoridades certificadoras também permitem a utilização da análise de Markov, que, embora seus modelos sejam mais poderosos que as árvores de falha, a indústria raramente adota esta análise porque seus modelos são mais complexos e difíceis de lidar. Diante disto, FTA tem sido amplamente utilizada neste processo, principalmente porque é conceitualmente mais simples e fácil de entender. À medida que a complexidade e o time-to-market dos sistemas aumentam, o interesse em abordar as questões de segurança durante as fases iniciais do projeto, ao invés de nas fases intermediárias/finais, tornou comum a adoção de projetos, ferramentas e técnicas baseados em modelos. Simulink é o exemplo padrão atualmente utilizado na indústria aeronáutica. Entretanto, mesmo neste cenário, as soluções atuais seguem o que os engenheiros já utilizavam anteriormente. Por outro lado, métodos formais que são linguagens, ferramentas e métodos baseados em lógica e matemática discreta e não seguem as abordagens da engenharia tradicional, podem proporcionar soluções inovadoras de baixo custo para engenheiros. Esta dissertação define uma estratégia para a avaliação quantitativa de segurança baseada na análise de Markov. Porém, em vez de lidar com modelos de Markov diretamente, usamos a linguagem formal Prism (uma especificação em Prism é semanticamente interpretada como um modelo de Markov). Além disto, esta especificação em Prism é extraída de forma sistemática a partir de um modelo de alto nível (diagramas Simulink anotados com lógicas de falha do sistema), através da aplicação de regras de tradução. A verificação sob o aspecto quantitativo dos requisitos de segurança do sistema é realizada utilizando o verificador de modelos de Prism, no qual os requisitos de segurança tornam-se fórmulas probabilísticas em lógica temporal. O objetivo imediato do nosso trabalho é evitar o esforço de se criar várias árvores de falhas até ser constatado que um requisito de segurança foi violado. Prism não constrói árvores de falha para chegar neste resultado. Ele simplesmente verifica de uma só vez se um requisito de segurança é satisfeito ou não no modelo inteiro. Finalmente, nossa estratégia é ilustrada com um sistema simples (um projeto-piloto), mas representativo, projetado pela Embraer
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Ngan, Choi-chik, and 顔才績. "A hidden Markov model approach to force-based contact recognition for intelligent robotic assembly." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243496.
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Tang, Man. "Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104039.
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The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data, allowing the identification of biomarkers in early disease diagnosis and driving the transformation of most disciplines in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. This dissertation focuses on modeling ``omics'' data in various NGS applications with a primary goal of developing novel statistical methods to identify sequence variants, find transcription factor (TF) binding patterns, and decode the relationship between TF and gene expression levels. Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in NGS applications. Existing methods for calling these variants often make simplified assumption of positional independence and fail to leverage the dependence of genotypes at nearby loci induced by linkage disequilibrium. We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short read data. Simulation experiments show that, under various sequencing depths, vi-HMM outperforms existing methods in terms of sensitivity and F1 score. When applied to the human whole genome sequencing data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs. One important NGS application is chromatin immunoprecipitation followed by sequencing (ChIP-seq), which characterizes protein-DNA relations through genome-wide mapping of TF binding sites. Multiple TFs, binding to DNA sequences, often show complex binding patterns, which indicate how TFs with similar functionalities work together to regulate the expression of target genes. To help uncover the transcriptional regulation mechanism, we propose a novel nonparametric Bayesian method to detect the clustering pattern of multiple-TF bindings from ChIP-seq datasets. Simulation study demonstrates that our method performs best with regard to precision, recall, and F1 score, in comparison to traditional methods. We also apply the method on real data and observe several TF clusters that have been recognized previously in mouse embryonic stem cells. Recent advances in ChIP-seq and RNA sequencing (RNA-Seq) technologies provides more reliable and accurate characterization of TF binding sites and gene expression measurements, which serves as a basis to study the regulatory functions of TFs on gene expression. We propose a log Gaussian cox process with wavelet-based functional model to quantify the relationship between TF binding site locations and gene expression levels. Through the simulation study, we demonstrate that our method performs well, especially with large sample size and small variance. It also shows a remarkable ability to distinguish real local feature in the function estimates.Doctor of Philosophy
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data and bring out innovations in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. In this dissertation, we mainly focus on three problems closely related to NGS and its applications: (1) how to improve variant calling accuracy, (2) how to model transcription factor (TF) binding patterns, and (3) how to quantify of the contribution of TF binding on gene expression. We develop novel statistical methods to identify sequence variants, find TF binding patterns, and explore the relationship between TF binding and gene expressions. We expect our findings will be helpful in promoting a better understanding of disease causality and facilitating the design of personalized treatments.
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Pagliarani, Andrea. "New markov chain based methods for single and cross-domain sentiment classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8445/.
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Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.
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Montazeri, Ghahjaverestan Nasim. "Early detection of cardiac arrhythmia based on Bayesian methods from ECG data." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S061/document.
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L'apnée est une complication fréquente chez les nouveaux-nés prématurés. L'un des problèmes les plus fréquents est l'épisode d'apnée bradycardie dont la répétition influence de manière négative le développement de l'enfant. C'est pourquoi les enfants prématurés sont surveillés en continu par un système de monitoring. Depuis la mise en place de ce système, l'espérance de vie et le pronostic de vie des prématurés ont été considérablement améliorés et ainsi la mortalité réduite. En effet, les avancées technologiques en électronique, informatique et télécommunications ont conduit à l'élaboration de systèmes multivoies de monitoring néonatal de plus en plus performants. L'un des principaux signaux exploités dans ces systèmes est l'électrocardiogramme (ECG). Toutefois, même si l'analyse de l'ECG a évolué au fil des années, l'ensemble des informations qu'il fournit n'est pas encore totalement exploité dans les processus de décision, notamment en monitoring en Unité de Soins Intensifs en Néonatalogie (USIN). L'objectif principal de cette thèse est d'améliorer la prise en compte des dynamiques multi-dimensionnelles en proposant de nouvelles approches basées sur un formalisme bayésien, pour la détection précoce des apnées bradycardies chez le nouveau-né prématuré. Aussi, dans cette thèse, nous proposons deux approches bayésiennes, basées sur les caractéristiques de signaux biologiques en vue de la détection précoce de l'apnée bradycardie des nouveaux-nés prématurés. Tout d'abord avec l'approche de Markov caché, nous proposons deux extensions du Modèle de Markov Caché (MMC) classique. La première, qui s'appelle Modèle de Markov Caché Couplé (MMCC), créé une chaîne de Markov à chaque dimension de l'observation et établit un couplage entre les chaînes. La seconde, qui s'appelle Modèle Semi-Markov Caché Couplé (MSMCC), combine les caractéristiques du modèle de MSMC avec le mécanisme de couplage entre canaux. Pour les deux nouveaux modèles (MMCC et MSMCC), les algorithmes récursifs basées sur la version classique de Forward-Backward sont introduits pour résoudre les problèmes d'apprentissage et d'inférence dans le cas couplé. En plus des modèles de Markov, nous proposons deux approches passées sur les filtres de Kalman pour la détection d'apnée. La première utilise les modifications de la morphologie du complexe QRS et est inspirée du modèle générateur de McSharry, déjà utilisé en couplant avec un filtre de Kalman étendu dans le but de détecter des changements subtils de l'ECG, échantillon par échantillon. La deuxième utilise deux modèles AR (l'un pour le processus normal et l'autre pour le processus de bradycardie). Les modèles AR sont appliqués sur la série RR, alors que le filtre de Kalman suit l'évolution des paramètres du modèle AR et fournit une mesure de probabilité des deux processus concurrentsApnea-bradycardia episodes (breathing pauses associated with a significant fall in heart rate) are the most common disease in preterm infants. Consequences associated with apnea-bradycardia episodes involve a compromise in oxygenation and tissue perfusion, a poor neuromotor prognosis at childhood and a predisposing factor to sudden-death syndrome in preterm newborns. It is therefore important that these episodes are recognized (early detected or predicted if possible), to start an appropriate treatment and to prevent the associated risks. In this thesis, we propose two Bayesian Network (BN) approaches (Markovian and Switching Kalman Filter) for the early detection of apnea bradycardia events on preterm infants, using different features extracted from electrocardiographic (ECG) recordings. Concerning the Markovian approach, we propose new frameworks for two generalizations of the classical Hidden Markov Model (HMM). The first framework, Coupled Hidden Markov Model (CHMM), is accomplished by assigning a Markov chain (channel) to each dimension of observation and establishing a coupling among channels. The second framework, Coupled Hidden semi Markov Model (CHMM), combines the characteristics of Hidden semi Markov Model (HSMM) with the above-mentioned coupling concept. For each framework, we present appropriate recursions in order to use modified Forward-Backward (FB) algorithms to solve the learning and inference problems. The proposed learning algorithm is based on Maximum Likelihood (ML) criteria. Moreover, we propose two new switching Kalman Filter (SKF) based algorithms, called wave-based and R-based, to present an index for bradycardia detection from ECG. The wave-based algorithm is established based on McSarry's dynamical model for ECG beat generation which is used in an Extended Kalman filter algorithm in order to detect subtle changes in ECG sample by sample. We also propose a new SKF algorithm to model normal beats and those with bradycardia by two different AR processes
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Yu, Junyi. "A Layered Two-Step Hidden Markov Model Positioning Method for Underground Mine Environment Based on Wi-Fi Signals." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-26328.
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The safety of miners is of interest to all countries. In the event of a coal mine disaster, how to locate the miners remains the biggest and most urgent issue. The aim of this study is to propose a precise positioning method for underground mine environments to a low cost and with acceptable accuracy. During the research work, in-depth learning and analysis of current geolocation methods for indoor areas have been carried out: advantages, disadvantages and the level of suitability of each method for mine environment have been presented. A layered two-step Hidden Markov Model has been proposed to simulate human walking in underground mine environments and an improved Viterbi algorithm suitable for the model has been implemented. The result of the positioning accuracy is quite satisfying compared to other positioning methods in the same category. A small modification to the proposed model has been illustrated in the future work which makes it more suitable for different situations rather than that limited by assumptions. The proposed positioning method can be claimed to be quite suitable for underground mine environments to a low cost and with acceptable accuracy.
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Lindahl, John, and Douglas Persson. "Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43498.
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Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.
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Strengbom, Kristoffer. "Mobile Services Based Traffic Modeling." Thesis, Linköpings universitet, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-116459.
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Traditionally, communication systems have been dominated by voice applications. Today with the emergence of smartphones, focus has shifted towards packet switched networks. The Internet provides a wide variety of services such as video streaming, web browsing, e-mail etc, and IP trac models are needed in all stages of product development, from early research to system tests. In this thesis, we propose a multi-level model of IP traffic where the user behavior and the actual IP traffic generated from different services are considered as being two independent random processes. The model is based on observations of IP packet header logs from live networks. In this way models can be updated to reflect the ever changing service and end user equipment usage. Thus, the work can be divided into two parts. The first part is concerned with modeling the traffic from different services. A subscriber is interested in enjoying the services provided on the Internet and traffic modeling should reflect the characteristics of these services. An underlying assumption is that different services generate their own characteristic pattern of data. The FFT is used to analyze the packet traces. We show that the traces contains strong periodicities and that some services are more or less deterministic. For some services this strong frequency content is due to the characteristics of cellular network and for other it is actually a programmed behavior of the service. The periodicities indicate that there are strong correlations between individual packets or bursts of packets. The second part is concerned with the user behavior, i.e. how the users access the different services in time. We propose a model based on a Markov renewal process and estimate the model parameters. In order to evaluate the model we compare it to two simpler models. We use model selection, using the model's ability to predict future observations as selection criterion. We show that the proposed Markov renewal model is the best of the three models in this sense. The model selection framework can be used to evaluate future models.
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McGarry, Gregory John. "Model-based mammographic image analysis." Thesis, Queensland University of Technology, 2002.
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Starke, Martin, Benjamin Beck, Denis Ritz, Frank Will, and Jürgen Weber. "Frequency based efficiency evaluation - from pattern recognition via backwards simulation to purposeful drive design." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71072.
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The efficiency of hydraulic drive systems in mobile machines is influenced by several factors, like the operators’ guidance, weather conditions, material respectively loading properties and primarily the working cycle. This leads to varying operation points, which have to be performed by the drive system. Regarding efficiency analysis, the usage of standardized working cycles gained through measurements or synthetically generated is state of the art. Thereby, only a small extract of the real usage profile is taken into account. This contribution deals with process pattern recognition (PPR) and frequency based efficiency evaluation to gain more precise information and conclusion for the drive design of mobile machines. By the example of an 18 t mobile excavator, the recognition system using Hidden – Markov - Models (HMM) and the efficiency evaluation process by means of backwards simulation of measured operation points will be described.
Books on the topic "Markov model-based methods"
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Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.
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The monograph discusses the theory, algorithms and practical methods of implementing digital processing and recognition of speech signals. The basics of mathematical analysis of digital signals necessary for speech processing are presented. The acoustic theory of speech formation with the construction of a general discrete model is briefly described. The main characteristic features of speech signals, as well as methods of their isolation are considered. Hidden Markov models and the architecture of traditional recognition systems based on them are described in detail. Weighted finite converters used to increase the efficiency and speed up the process of decoding acoustic signals are considered. The main architectures of artificial neural networks and examples of integrated (end-to-end) speech recognition systems based on them are presented.
It is intended for students, postgraduates, researchers and specialists dealing with speech signal processing, pattern recognition and artificial intelligence.
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Cevelev, Aleksandr. Material management of railway transport. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064961.
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In the monograph reviewed the development of the inventory management of railway transport in the new economic environment of market economy.
According to the results of theoretical research, innovative and production potential of the supply system of railway transport the main directions and methods of transformation of the restructuring process under the corporate changes of JSC "RZD", positioned value system of the logistics of railway transportation, and developed a classification model used logistical resources.
Evaluation of activity of structural divisions of Russian Railways supply is proposed to be viewed through an integrated and comprehensive approach to the development of systems of balanced indicators of supply and prompt handling of material resources, the implementation of which allows to distribute the strategic objectives of the company "Russian Railways" activities in the system of logistics of the Railways and also to involve in economic circulation of excessive and unused inventories of material and technical resources and efficiently reallocate them among enterprises at the site of the railway.
Recommendations for the implementation of the developed algorithms and models are long term in nature and are based on the concept of logistics management and improve the business processes of the logistics system.
Will be useful for managers and specialists of directorates of logistics of Russian Railways supply, undergraduates and graduate students interested in the economy of railway transport.
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Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.
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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.
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Voutilainen, Atro. Part-of-Speech Tagging. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0011.
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This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on n-grams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using methods from machine tagging, taggers based on handwritten global rules, and hybrid taggers, which combine the advantages of handwritten and automatically generated taggers. This article focuses on handwritten tagging rules. Well-tagged training corpora are a valuable resource for testing and improving language model. The text corpus reminds the grammarian about any oversight while designing a rule.
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Dixit, Avinash. Relation-Based Governance and Competition. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0015.
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If formal institutions of contract governance are absent or ineffective, traders try to substitute relational governance based on norms and sanctions. However, these alternatives need good information and communication concerning members’ actions; that works well only in relatively small communities. If there are fixed costs, the market has too few firms for perfect competition. The optimum must be a second best, balancing the effectiveness of contract governance and dead-weight loss of monopoly. This chapter explores this idea using a spatial model with monopolistic competition. It is found that relational governance constrains the size of firms and can cause inefficiently excessive entry, beyond the excess that already occurs in a spatial model without governance problems. Effects of alternative methods of improving governance to ameliorate this inefficiency are explored.
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Laver, Michael, and Ernest Sergenti. Systematically Interrogating Agent-Based Models. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0004.
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This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.
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Kamynin, Vladimir. Management by long-term development of a large company. LCC MAKS Press, 2021. http://dx.doi.org/10.29003/m2451.978-5-317-06688-8.
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The new methodological approach to study of market leadership which is based on long-term company development is developed in the monograph. The proposed methodological solution takes into account the manifestation of a complex of factors which are characteristic of world benchmark companies, as well as factors which are necessary to update the company's life cycle and determine the company's competitiveness in the conditions of knowledge economy. A system of indicators which are indicating the company's ability to occupy leading market positions and achieve other goals of various content has been proposed. Methods of revolutionary and evolutionary development are described. The importance of combining these methods during formulation the concept of company development is reflected. The approach of the world's best companies to setting corporate goals, forming a vision has been systematized, the corporate vision model has been proposed. The distinctive special aspects of the cultures of companies which are demonstrate the long-term sustainability are considered. The analysis of the important components of corporate culture, which are influence on the conditions for the development of the company was done. The total covered period by the research is 17 years. For students, graduate students, teachers of economic universities, heads of enterprises and entrepreneurs, as well as for those who are interested in business development strategy.
Book chapters on the topic "Markov model-based methods"
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Le Guen, Héléne, Frederique Vallée, and Anthony Faucogney. "Model-Based TestingAutomatic Generation of Test Cases Using the Markov Chain Model." In Industrial Use of Formal Methods, 29–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118561829.ch2.
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Rotondo, Damiano. "Fault Tolerant Control of Markov Jump Systems Using an Asynchronous Virtual Actuator." In Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis, 309–19. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27540-1_27.
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Zhang, Wei. "Fault Diagnosis Method Based on Hidden Markov Model." In Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines, 279–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49254-3_10.
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Liu, Zhi-Qiang, Jinhai Cai, and Richard Buse. "Hidden Markov Model-Based Method for Recognizing Handwritten Digits." In Handwriting Recognition, 61–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44850-1_3.
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Andriushchenko, Roman, Alexander Bork, Milan Češka, Sebastian Junges, Joost-Pieter Katoen, and Filip Macák. "Search and Explore: Symbiotic Policy Synthesis in POMDPs." In Computer Aided Verification, 113–35. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37709-9_6.
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AbstractThis paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller—that solely decides based on the observations seen so far—to achieve a total expected reward objective. As finding optimal controllers is undecidable, we concentrate on synthesising good finite-state controllers (FSCs). We do so by tightly integrating two modern, orthogonal methods for POMDP controller synthesis: a belief-based and an inductive approach. The former method obtains an FSC from a finite fragment of the so-called belief MDP, an MDP that keeps track of the probabilities of equally observable POMDP states. The latter is an inductive search technique over a set of FSCs, e.g., controllers with a fixed memory size. The key result of this paper is a symbiotic anytime algorithm that tightly integrates both approaches such that each profits from the controllers constructed by the other. Experimental results indicate a substantial improvement in the value of the controllers while significantly reducing the synthesis time and memory footprint.
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Cai, Xingquan, Yufeng Gao, Mengxuan Li, and Kyungeun Cho. "Infrared Human Posture Recognition Method Based on Hidden Markov Model." In Lecture Notes in Electrical Engineering, 501–7. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1536-6_65.
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Tran, H., and S. Setunge. "Deterioration Modeling of Concrete Bridges and Potential Nanotechnology Application." In Lecture Notes in Civil Engineering, 399–408. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_41.
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AbstractManagement of aging concrete bridges with limited resources can be a challenge for state authorities. Deterioration modeling of concrete bridges at the component level is essential to optimize maintenance actions and ensure the safety and serviceability of the bridge network. In this study we examined the Level 2 visual inspection data of a concrete bridge’s components collected over 4–5 inspection cycles with the objective of predicting deterioration of components and the bridge’s life cycle. With the increasing application of nanotechnology to increase the mechanical properties and durability of concrete material for bridge structures, the deterioration of nano-based concrete could be significantly different from conventional concrete. A range of deterioration prediction methods, including deterministic models and stochastic models, were examined to understand the validity of the different methods in predicting the deterioration of bridge components made of conventional and nano-based materials. A case study with a demonstration on a concrete open girder was investigated with regard to linear regression models and the stochastic Markov deterioration model. The outcomes can be used to support future study on the performance of conventional and nano-based concrete materials and their lifecycles in the asset management of bridges.
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Zhang, Weifeng, Zhen Pan, and Ziyuan Wang. "Prediction Method of Code Review Time Based on Hidden Markov Model." In Web Information Systems and Applications, 168–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60029-7_15.
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Liang, Zehai, and Dewang Chen. "An Evaluation Method for Traffic Data Quality Based on Markov Model." In Proceedings of the Fifth International Forum on Decision Sciences, 63–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7817-0_7.
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Shi, Chaochen, and Jiangshan Yu. "A Hidden Markov Model-Based Method for Virtual Machine Anomaly Detection." In Provable Security, 372–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31919-9_24.
Full textConference papers on the topic "Markov model-based methods"
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Nouza, J. "Feature selection methods for hidden Markov model-based speech recognition." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546749.
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Zhihao, Zhang, LI Jian, ZHANG Zhen-Yuan, HUANG Qi, and Qu Hedi. "Non-Intrusive Load Identification Methods Based on LI-Norm and Hidden Markov Chain Model." In 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018. http://dx.doi.org/10.1109/imcec.2018.8469417.
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Oh, KyoJoong, Young-Seob Jeong, Sung-Suk Kim, and Ho-Jin Choi. "Gesture recognition application with Parametric Hidden Markov Model for activity-based personalized service in APRiME." In 2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2011). IEEE, 2011. http://dx.doi.org/10.1109/cogsima.2011.5753443.
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Araújo, Felipe Rocha de, Denis Lima Rosário, Kassio Machado, Eduardo Coelho Cerqueira, and Leandro Villas. "TEMMUS: A Mobility Predictor based on Temporal Markov Model with User Similarity." In XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc.2019.7389.
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Location-Based Social Networks (LBSN) data contains spatial, temporal, and social features of user activity, providing valuable information that is currently available on large-scale and low-cost fashion via traditional data collection methods. In this way, LBSN data enables to predict user mobility based on spatial, temporal, and social features, which can be used in several areas, such as device-to-device (D2D) communication, caching, and others. In addition, a Temporal Markov Chain (TMC) is a stochastic model used to model randomly changing systems, such as mobility prediction based on the spatiotemporal factor such as location and day of the week. In this paper, we introduce the Temporal Markov Model with User Similarity (TEMMUS) mobility prediction model. TEMMUS considers a TMC of variable order based on the day of the week (weekday or weekend) and the user similarity to predict the user's future location. The results highlight a higher accuracy of TEMMUS compared to three state-of-the-art Markov Model predictors.
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Glaser, Philipp, Michael Schick, Kosmas Petridis, and Vincent Heuveline. "COMPARISON BETWEEN A POLYNOMIAL CHAOS SURROGATE MODEL AND MARKOV CHAIN MONTE CARLO FOR INVERSE UNCERTAINTY QUANTIFICATION BASED ON AN ELECTRIC DRIVE TEST BENCH." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2452.10011.
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Черняев, Сергей, Sergey Chernyaev, Олег Лукашенко, and Oleg Lukashenko. "Comparative Analysis of Methods for Segmentation of FMRI Images Based on Markov Random Fields." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-143-147.
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The problem of segmentation of three-dimensional fMRI images based on the Bayesian approach is considered, where Markov Random Field is used as the prior distribution, and von Mises-Fisher distribution is used as the observation model. The main problem when applying this approach in practice is an estimation of the model parameters. In this paper, we review algorithms HMRF-MCEM, HMRF-EM and GrabCut, which implement this statistical model and estimate parameters without the usage of the labeled training data. The methods HMRF-EM and GrabCut were introduced in conjunction with other statistical models, but after a small modification, they can be used with the von Mises-Fisher distribution. A comparative study was carried out by performing experiments on both synthetic, generated from the statistical model, and real fMRI data.
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Morozov, Andrey, Mihai A. Diaconeasa, and Mikael Steurer. "A Hybrid Methodology for Model-Based Probabilistic Resilience Evaluation of Dynamic Systems." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23789.
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Abstract Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numerical resilience analysis of such dynamic systems requires more flexible methods. These methods shall enable the analysis of the systems with sophisticated software parts and dynamic feedback loops. A suitable candidate is the Dual-graph Error Propagation Model (DEPM) that can capture nontrivial failure scenarios and dynamic fault-tolerance mechanisms. The DEPM exploits the method for the automatic generation of Markov chain models and the application of probabilistic model checking techniques. Moreover, the DEPM enables the analysis of highly-customizable system resilience metrics, e.g., “the probability of system recovery to a particular state after a specified system failure during a defined time interval.” In this paper, we show how DEPM-based resilience analysis can be integrated with the general PRA methodology for resilience evaluations. The proposed methodology is demonstrated on a safety-critical autonomous UAV system.
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Giantomassi, Andrea, Francesco Ferracuti, Alessandro Benini, Gianluca Ippoliti, Sauro Longhi, and Antonio Petrucci. "Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48174.
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Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.
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Zhou, Yunyi, Zhixuan Chu, Yijia Ruan, Ge Jin, Yuchen Huang, and Sheng Li. "pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/521.
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Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE over all member models and competitive ensemble methods.
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Fleming, Karl N., and Bengt O. Y. Lydell. "Use of Markov Piping Reliability Models to Evaluate Time Dependent Frequencies of Loss of Coolant Accidents." In 12th International Conference on Nuclear Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/icone12-49172.
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Markov model theory has been applied to develop a method to evaluate the influence of alternate strategies for in-service inspection and leak detection on the frequency of leaks and ruptures in nuclear power plant piping systems [1–4]. This approach to quantification of pipe rupture frequency was originally based on a Bayes’ uncertainty analysis approach to derive piping system failure rates from a combination of service experience data and some simple reliability models [5–7]. More recently the Markov model approach has been used in conjunction with probabilistic fracture mechanics methods in the study of flow accelerated corrosion [8]. One interesting property of the Markov model is its capability to evaluate time dependent rupture frequencies via the model hazard rate. In this paper this time dependent modeling capability is used to investigate the age related and time dependent frequencies of loss of coolant accident (LOCA) initiating event frequencies. A case is presented that plant age dependent LOCA frequencies should be used in lieu of other metrics commonly used in probabilistic risk assessments and in risk informed inservice inspection evaluations. Such more commonly used metrics include the assumed constant failure rate method and the lifetime average rupture probability. Both of these methods are shown to provide optimistic estimates of LOCA frequencies for plants in the latter part of their design lifetimes, which most operating plants are approaching.
Reports on the topic "Markov model-based methods"
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Oliveira, Lucas Gabriel Martins de. Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data. Inter-American Development Bank, July 2023. http://dx.doi.org/10.18235/0005004.
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The objective of this paper is to develop a basic framework for the implementation of a GDP nowcasting strategy using Brazilian data. Our goal is to identify a scalable strategy that allows us to project the Brazilian GDP in real time at any point during the current quarter. In the paper we detail the survey of classical techniques and also of techniques usually known by market practitioners as "machine learning methods". We survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods. Additionally, we perform backtesting exercises, estimate several candidate models for GDP nowcasting. Finally, we evaluate the forecasting power of all models against a naive model and a market expectations model. We demonstrate that a combination of machine learning models based on the distance of forecasts to the average market expectations defeats the fully informed market expectations, while the same is not possible for selected classical nowcasting models.
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Соловйов, В. М., В. В. Соловйова, and Д. М. Чабаненко. Динаміка параметрів α-стійкого процесу Леві для розподілів прибутковостей фінансових часових рядів. ФО-П Ткачук О. В., 2014. http://dx.doi.org/10.31812/0564/1336.
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Modem market economy of any country cannot successfully behave without the existence of the effective financial market. In the conditions of growing financial market, it is necessary to use modern risk-management methods, which take non-gaussian distributions into consideration. It is known, that financial and economic time series return’s distributions demonstrate so-called «heavy tails», which interrupts the modeling o f these processes with classical statistical methods. One o f the models, that is able to describe processes with «heavy tails», are the а -stable Levi processes. They can slightly simulate the dynamics of the asset prices, because it consists o f two components: the Brownian motion component and jump component. In the current work the usage of model parameters estimation procedure is proposed, which is based on the characteristic functions and is applied for the moving window for the purpose of financial-economic system’ s state monitoring.
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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.
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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Naddafi, Rahmat, Göran Sundblad, Alfred Sandström, Lachlan Fetterplace, Jerker Vinterstare, Martin Ogonowski, and Nataliia Kulatska. Developing management goals and associated assessment methods for Sweden’s nationally managed fish stocks : a project synthesis. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.31cfjep2i0.
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This report summarizes and synthesizes results from the Swedish Agency of Marine and Water Management (SwAM, or HaV) funded project “Förvaltningsmål för nationella arter (Management goals for nationally managed species)”. The objectives of the project have been to promote the development of management goals and associated status assessment methods and indicators, as well as reference points, for some nationally managed fish stocks both in coastal as well as freshwater areas. The report focusses largely on species and stocks that can be defined as data-poor. Such stocks are characterised by marked limitations in data availability and/or resources allocated to detailed analytical stock projections. Data-poor stocks also often lack carefully formulated management goals and associated methods and indicators for assessing stock status. In this report, we provide an overview of potential assessment methods and indicators and try to synthesise how they work and what the strengths and weaknesses are by applying them to selected data poor stocks such as pikeperch, pike, whitefish, and vendace. We also discuss how they relate to different potential management goals and provide recommendations for their application. We grouped the indicators and assessment methods by the three categories that are now used in the yearly status assessment framework provided by SLU Aqua (Resursöversikten/Fiskbarometern) – i) mortality, ii) abundance/biomass and iii) size/age structure. The results are also described for these three main categories of assessment indicators. Included is also a status report from a size- and age-based population dynamics model (Stock Synthesis 3) that is being developed for pikeperch in Lake Hjälmaren. An important experience from the project is that to improve the assessment methods for Swedish national fish stocks, it is important that managers develop both general as well as more detailed quantitative goals for the individual stocks. This should ideally be conducted in various forms of collaboration with the main stakeholders and scientists involved with assessment as participatory processes foster legitimacy. Carefully articulated management goals, which are possible to translate into quantitative targets, will facilitate the development of various approaches and methods to monitor stock statuses. Given the strong and complex interactions of fish and their environments it is also important to consider other pressures than fisheries when developing indicators and assessment methods. Our synthesis highlights a number of areas where the assessment of data-poor stocks can be improved: 1. Apply precautionary principles for data-limited stocks, particularly ones that are known to be vulnerable to exploitation. 2. Tailor approaches to how fisheries are managed in Sweden. Swedish nationally managed fish stocks are not managed by quotas (with one exception, vendace in the Bothnian Bay) and do not aim for maximum sustainable yield. Instead, the coastal and inland fisheries are managed by regulating the effort in the small-scale commercial fisheries (number of fishers/licenses and amount of gear). Regulation of recreational and subsistence fisheries effort, in terms of licenses or number of fishers) is not applied, nor possible since the fisheries is lacking obligatory notification and reporting systems. All national fisheries, however, are regulated by various technical measures (closed areas, size-limits, bag-limits, gear restrictions etc). Thus, goals and assessment methods that result in harvest limits or quota recommendations expressed in e.g. biomass/numbers are difficult to use as basis for management. Instead, there is a need for alternative management goals and associated assessment methods. 3. Use best practice methods and indicators and adapt as scientific knowledge is developed. Data-limited methods are developing rapidly, and new methods/approaches are proposed in the scientific literature every year. It is thus important to be updated on the most recent developments. 4. Clearly describe limitations/assumptions of methods used. It is important to be aware of and critically evaluate the assumptions underlying the analyses, and to carefully communicate uncertainty together with the stock status assessment. 5. Be particularly careful with low sample numbers. Many indicators and methods can be applied also on small sample sizes, however, the accuracy and precision of the estimates risk being low in such cases. 6. Accept that there is no "gold standard" for fisheries assessment. Each case study is unique and needs to be balanced against data availability, local needs and other important factors. This also means that analysts need to be careful when using generic reference levels or “borrowing” data from other stocks. 7. If possible, use several different methods/indicators. Although several indicators aim to measure similar aspects of the stock, small methodological differences can support the overall interpretation of individual indicator values. It is particularly important to incorporate many aspects and indicators (size/age/abundance/mortality) in order to produce a balanced assessment. 8. Develop means of communication. Indicators and goals should be easy to understand. However, interpretation of results from multi-indicator frameworks can be challenging. There is thus a need for finding ways of communication that can convey complicated results in a simple-to-understand manner. 9. For details on additional improvements, we refer the reader to the sub-header “recommendations for the future” found under each chapter. The implementation of Stock Synthesis for pikeperch in Lake Hjälmaren showed that it is possible to develop a more ambitious and detailed stock assessment model for a relatively data-poor stock. The model results partly support earlier interpretations of the development of the stock and the importance of the changes in regulations in 2001 (increased minimum size, increased mesh size and reduced mortality of undersized pikeperch). Before the model can be implemented and used for practical management, a number of actions for improvement are needed, which are highlighted in the relevant chapter. The most important next step is establishing management goals and reference levels for this stock. We recommend that such a dialogue is initiated by managers. The fisheries management goals should consider both biomass, fisheries mortality and size-based targets. To conclude, we stress the importance of improving all ongoing aspects related to the assessments of data-poor Swedish stocks. Strong local stocks and sustainable fisheries are vital for a variety of fisheries-related businesses and practices, particularly in rural areas, providing economical and societal value. Fishes also have important roles in aquatic food-webs and it is important that ecological values are managed wisely in order to reach targets for water quality, ecosystem structure and diversity. Given the strong and complex interactions of fish and their environments it is also important to consider other pressures than fisheries when developing indicators and assessment methods.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Full textAbstract:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
Full textAbstract:
The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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Yogev, David, Ricardo Rosenbusch, Sharon Levisohn, and Eitan Rapoport. Molecular Pathogenesis of Mycoplasma bovis and Mycoplasma agalactiae and its Application in Diagnosis and Control. United States Department of Agriculture, April 2000. http://dx.doi.org/10.32747/2000.7573073.bard.
Full textAbstract:
Mycoplasma bovis and M. agalactiae are two phylogenetically related mycoplasmas which cause economically significant diseases in their respective bovine or small ruminant hosts. These organisms cause persistent asymptomatic infections that can result in severe outbreaks upon introduction of carrier animals into susceptible herds. Little is known about the mechanisms underlying mycoplasma-host interaction, variation in virulence, or of the factors enabling avoidance of the host immune system. In recent years it has become apparent that the ability of pathogenic microorganisms to rapidly alter surface antigenic structures and to fine tune their antigenicity, a phenomena called antigenic variation, is one of the most effective strategies used to escape immune destruction and to establish chronic infections. Our discovery of a novel genetic system, mediating antigenic variation in M. bovis (vsp) as well as in M. agalactiae (avg) served as a starting point for our proposal which included the following objectives: (i) Molecular and functional characterization of the variable surface lipoproteins (Vsp) system of M. bovis and comparison with the Vsp-counterpart in M. agalactiae (ii) Determination of the role of Vsp proteins in the survival of M. bovis when confronted by host defense factors, (iii) Assessment of Vsp-based genetic and antigenic typing of M. bovis and M. agalactiae for epidemiology of infection and (iv) Improvement of diagnostic tests for M. bovis and M. agalactiae based on the vsp-and vsp-analogous systems. We have carried out an extensive molecular characterization of the vsp system and unravelled the precise molecular mechanism responsible for the generation of surface antigenic variation in M. bovis. Our data clearly demonstrated that the two pathogenic mycoplasma species possess large gene families encoding variable lipoprotein antigens that apparently play an important role in immune evasion and in pathogen-host interaction during infection. Phase variable production of these antigens was found to be mediated by a novel molecular mechanism utilizing double site-specific DNA inversions via an intermediate vsp configuration. Studies in model systems indicate that phase variation of VspA is relevant in interaction between M. bovis and macrophages or monocytes, a crucial stage in pathogenesis. Using an ELISA test with captured VspA as an antigen, phase variation was shown to occur in vivo and under field conditions. Genomic rearrangements in the avg gene family of M. agalactiae were shown to occur in vivo and may well have a role in evasion of host defences and establishment of chronic infection. An epidemiological study indicated that patterns of vsp-related antigenic variation diverge rapidly in an M. bovis infected herd. Marked divergence was also found with avg-based genomic typing of M. agalactiae in chronically infected sheep. However, avg-genomic fingerprints were found to be relatively homogeneous in different animals during acute stages of an outbreak of Contagious Agalactiae, and differ between unrelated outbreaks. These data support the concept of vsp-based genomic typing but indicate the necessity for further refinement of the methodology. The molecular knowledge on these surface antigens and their encoding genes provides the basis for generating specific recombinant tools and serological methods for serodiagnosis and epidemiological purposes. Utilization of these methods in the field may allow differentiating acutely infected herds from chronic herds and disease-free herds. In addition the highly immunogenic nature of these lipoproteins may facilitate the design of protective vaccine against mycoplasma infections.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Full textAbstract:
Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.