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Статті в журналах з теми "Markov model-based methods"
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.
Повний текст джерелаZang, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаLin, 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.
Повний текст джерелаBouton, 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.
Повний текст джерелаBertolami, 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.
Повний текст джерелаDong, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаWei, 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.
Повний текст джерелаHuan, 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.
Повний текст джерелаДисертації з теми "Markov model-based methods"
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.
Повний текст джерелаFaculdade 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
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.
Повний текст джерелаTang, 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.
Повний текст джерелаDoctor 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.
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/.
Повний текст джерелаMontazeri, Ghahjaverestan Nasim. "Early detection of cardiac arrhythmia based on Bayesian methods from ECG data." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S061/document.
Повний текст джерелаApnea-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
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.
Повний текст джерелаLindahl, 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.
Повний текст джерелаStrengbom, Kristoffer. "Mobile Services Based Traffic Modeling." Thesis, Linköpings universitet, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-116459.
Повний текст джерелаMcGarry, Gregory John. "Model-based mammographic image analysis." Thesis, Queensland University of Technology, 2002.
Знайти повний текст джерелаStarke, 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.
Повний текст джерелаКниги з теми "Markov model-based methods"
Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.
Повний текст джерелаCevelev, Aleksandr. Material management of railway transport. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064961.
Повний текст джерелаQuintana, 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.
Повний текст джерелаVoutilainen, Atro. Part-of-Speech Tagging. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0011.
Повний текст джерелаDixit, Avinash. Relation-Based Governance and Competition. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0015.
Повний текст джерелаLaver, Michael, and Ernest Sergenti. Systematically Interrogating Agent-Based Models. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0004.
Повний текст джерелаKamynin, 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.
Повний текст джерелаЧастини книг з теми "Markov model-based methods"
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.
Повний текст джерелаRotondo, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаAndriushchenko, 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.
Повний текст джерелаCai, 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.
Повний текст джерелаTran, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаLiang, 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.
Повний текст джерелаShi, 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.
Повний текст джерелаТези доповідей конференцій з теми "Markov model-based methods"
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.
Повний текст джерелаZhihao, 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.
Повний текст джерелаOh, 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.
Повний текст джерелаAraújo, 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.
Повний текст джерелаGlaser, 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.
Повний текст джерелаЧерняев, 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.
Повний текст джерелаMorozov, 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.
Повний текст джерелаGiantomassi, 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.
Повний текст джерелаZhou, 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.
Повний текст джерелаFleming, 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.
Повний текст джерелаЗвіти організацій з теми "Markov model-based methods"
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.
Повний текст джерелаСоловйов, В. М., В. В. Соловйова та 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.
Повний текст джерелаTsidylo, 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.
Повний текст джерелаNaddafi, 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.
Повний текст джерелаEngel, 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.
Повний текст джерелаGalili, 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.
Повний текст джерелаYogev, 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.
Повний текст джерелаGur, 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.
Повний текст джерела