subject:(Hidden Markov Model)

Texas A&M University

1. Wang, Xiduo. A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.

Degree: 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/156266

► The inherent intention and decision process of lane changes are complex and unobservable. Though the external environment and traffic conditions are changing along the traveling… (more)

Subjects/Keywords: Lane Changing; Hidden Markov Model

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APA (6^th Edition):

Wang, X. (2015). A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/156266

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Xiduo. “A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.” 2015. Thesis, Texas A&M University. Accessed March 24, 2019. http://hdl.handle.net/1969.1/156266.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Xiduo. “A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.” 2015. Web. 24 Mar 2019.

Vancouver:

Wang X. A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. [Internet] [Thesis]. Texas A&M University; 2015. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1969.1/156266.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang X. A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. [Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/156266

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

2. Huang, Yu-Zhi. Detecting Attack Sequence in Cloud Based on Hidden Markov Model.

Degree: Master, Computer Science and Engineering, 2012, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041

► Cloud computing provides business new working paradigm with the benefit of cost reduce and resource sharing. Tasks from different users may be performed on the… (more)

Subjects/Keywords: Cloud Computing; Hidden Markov Model; Attack Plan

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APA (6^th Edition):

Huang, Y. (2012). Detecting Attack Sequence in Cloud Based on Hidden Markov Model. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Huang, Yu-Zhi. “Detecting Attack Sequence in Cloud Based on Hidden Markov Model.” 2012. Thesis, NSYSU. Accessed March 24, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Huang, Yu-Zhi. “Detecting Attack Sequence in Cloud Based on Hidden Markov Model.” 2012. Web. 24 Mar 2019.

Vancouver:

Huang Y. Detecting Attack Sequence in Cloud Based on Hidden Markov Model. [Internet] [Thesis]. NSYSU; 2012. [cited 2019 Mar 24]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang Y. Detecting Attack Sequence in Cloud Based on Hidden Markov Model. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Nut Sornchumni. Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/9846

Subjects/Keywords: Hidden Markov model

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APA (6^th Edition):

Sornchumni, N. (n.d.). Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/9846

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sornchumni, Nut. “Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed March 24, 2019. http://hdl.handle.net/10061/9846.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sornchumni, Nut. “Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ.” Web. 24 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Sornchumni N. Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2019 Mar 24]. Available from: http://hdl.handle.net/10061/9846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Sornchumni N. Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレマルコフモデルニタイスルパラメータスイテイアルゴリズムノサブンプライバシーカニカンスルケンキュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/9846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Penn State University

4. Osotsi, Ame J. Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.

Degree: MS, Statistics, 2016, Penn State University

URL: https://etda.libraries.psu.edu/catalog/vm40xr56k

► Twitter is a popular microblogging platform that displays real-time status updates from over 140 million users a day. The users post about anything, from daily… (more)

Subjects/Keywords: Change Point; Hidden Markov Model; Twitter

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APA (6^th Edition):

Osotsi, A. J. (2016). Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/vm40xr56k

Chicago Manual of Style (16^th Edition):

Osotsi, Ame J. “Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.” 2016. Masters Thesis, Penn State University. Accessed March 24, 2019. https://etda.libraries.psu.edu/catalog/vm40xr56k.

MLA Handbook (7^th Edition):

Osotsi, Ame J. “Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.” 2016. Web. 24 Mar 2019.

Vancouver:

Osotsi AJ. Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. [Internet] [Masters thesis]. Penn State University; 2016. [cited 2019 Mar 24]. Available from: https://etda.libraries.psu.edu/catalog/vm40xr56k.

Council of Science Editors:

Osotsi AJ. Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. [Masters Thesis]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/vm40xr56k

University of North Texas

5. Saudagar, Abdullah Naseer Ahmed. Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors.

Degree: 2011, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc103388/

► In today’s fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or… (more)

Subjects/Keywords: highlights; descriptors; MPEG-7; hidden Markov model

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APA (6^th Edition):

Saudagar, A. N. A. (2011). Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc103388/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Saudagar, Abdullah Naseer Ahmed. “Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors.” 2011. Thesis, University of North Texas. Accessed March 24, 2019. https://digital.library.unt.edu/ark:/67531/metadc103388/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Saudagar, Abdullah Naseer Ahmed. “Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors.” 2011. Web. 24 Mar 2019.

Vancouver:

Saudagar ANA. Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors. [Internet] [Thesis]. University of North Texas; 2011. [cited 2019 Mar 24]. Available from: https://digital.library.unt.edu/ark:/67531/metadc103388/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Saudagar ANA. Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors. [Thesis]. University of North Texas; 2011. Available from: https://digital.library.unt.edu/ark:/67531/metadc103388/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

6. Liu, Lun-Kang. The Analysis and Forecasting of the Bitcoin Return.

Degree: Master, Institute Of Applied Mathematics, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022

► The feasibility of cryptocurrency as an investment tool has been a topic of considerable concern in recent years. Bitcoin is a cryptocurrency that is universally… (more)

Subjects/Keywords: Text mining; Hidden Markov model; Cryptocurrency

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APA (6^th Edition):

Liu, L. (2018). The Analysis and Forecasting of the Bitcoin Return. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Lun-Kang. “The Analysis and Forecasting of the Bitcoin Return.” 2018. Thesis, NSYSU. Accessed March 24, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Lun-Kang. “The Analysis and Forecasting of the Bitcoin Return.” 2018. Web. 24 Mar 2019.

Vancouver:

Liu L. The Analysis and Forecasting of the Bitcoin Return. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Mar 24]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu L. The Analysis and Forecasting of the Bitcoin Return. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

7. Chen, Chao-Yeh. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

URL: http://hdl.handle.net/2152/ETD-UT-2010-12-2301

► Existing methods for image-based location estimation generally attempt to recognize every photo independently, and their resulting reliance on strong visual feature matches makes them most… (more)

Subjects/Keywords: Location estimation; Hidden Markov Model; Object recognition

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APA (6^th Edition):

Chen, C. (2010). Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-12-2301

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Chao-Yeh. “Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.” 2010. Thesis, University of Texas – Austin. Accessed March 24, 2019. http://hdl.handle.net/2152/ETD-UT-2010-12-2301.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Chao-Yeh. “Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.” 2010. Web. 24 Mar 2019.

Vancouver:

Chen C. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2301.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen C. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2301

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

8. -6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.

Degree: Operations Research and Industrial Engineering, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/63715

► Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into… (more)

▼ Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into decisions on what to do with the equipment. Hidden Markov model (HMM) is a useful framework to probabilistically model the condition of complex engineering systems with partial observability of the underlying states. Condition monitoring and prediction of such type of system requires accurate knowledge of HMM that describes the degradation of such a system with data collected from the sensors mounted on it, as well as understanding of the uncertainty of the HMMs identified from the available data. To that end, this thesis proposes a novel HMM estimation scheme based on the principles of Bayes theorem. The newly proposed Bayesian estimation approach for estimating HMM parameters naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation process. In addition, a novel condition monitoring scheme based on uncertain HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor manufacturing facility. Portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation process, while the remainder of the data was used for monitoring of machine condition using the uncertain degradation HMMs yielded by Bayesian estimation. Comparison with a traditional signature-based statistical monitoring method showed that the newly proposed approach effectively utilizes the fact that its parameters are uncertain themselves, leading to orders of magnitude fewer false alarms. This methodology is further extended to address the practical issue that maintenance interventions are usually imperfect. We propose both a novel non-ergodic and non-homogeneous HMM that assumes imperfect maintenances and a novel process monitoring method capable of monitoring the hidden states considering model uncertainty. Significant improvement in both the log-likelihood of estimated HMM parameters and monitoring performance were observed, compared to those obtained using degradation HMMs that always assumed perfect maintenance. Finally, behavior of the posterior distribution of parameters of unidirectional non- ergodic HMMs modeling in this thesis for degradation was theoretically analyzed in terms of their evolution as more data become available in the estimation process. The convergence problem is formulated as a Bernstein-von Mises theorem (BvMT), and under certain regularity conditions, the sequence of posterior distributions is proven to converge to a Gaussian distribution with variance matrix being the inverse of the Fisher information matrix. An example of a unidirectional HMM is presented for which the regularity conditions are verified, and illustrations of expected theoretical results are given using simulation. The understanding of such convergence of posterior distributions enables one to… Advisors/Committee Members: Djurdjanovic, Dragan (advisor), Hasenbein, John (committee member), Bickel, James Eric (committee member), Walker, Stephen G (committee member), Hanasusanto, Grani (committee member).

Subjects/Keywords: Hidden Markov model; Condition-based maintenance

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APA (6^th Edition):

-6024-5216. (2018). Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63715

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Thesis, University of Texas – Austin. Accessed March 24, 2019. http://hdl.handle.net/2152/63715.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Web. 24 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2152/63715.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63715

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

9. Li, Na. Hidden Markov model and financial application.

Degree: Statistics, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/47043

► A Hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with numerous unobserved… (more)

Subjects/Keywords: Hidden Markov model; Financial time series

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, N. (2016). Hidden Markov model and financial application. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47043

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Na. “Hidden Markov model and financial application.” 2016. Thesis, University of Texas – Austin. Accessed March 24, 2019. http://hdl.handle.net/2152/47043.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Na. “Hidden Markov model and financial application.” 2016. Web. 24 Mar 2019.

Vancouver:

Li N. Hidden Markov model and financial application. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2152/47043.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li N. Hidden Markov model and financial application. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/47043

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

10. Liu, Mingming. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.

Degree: PhD, Computer Science, 2015, Virginia Tech

URL: http://hdl.handle.net/10919/73703

► With the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been… (more)

Subjects/Keywords: Genetic variation; Indel; SNP; Hidden Markov Model

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APA (6^th Edition):

Liu, M. (2015). Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73703

Chicago Manual of Style (16^th Edition):

Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed March 24, 2019. http://hdl.handle.net/10919/73703.

MLA Handbook (7^th Edition):

Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Web. 24 Mar 2019.

Vancouver:

Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/10919/73703.

Council of Science Editors:

Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73703

Anna University

11. Jagadeesh Kannan R. Handwritten character recognition using hybrid techniques.

Degree: Information and Communication, 2013, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/9849

►

The primary objective of this research work is to develop a character recognition system, which can further be developed to recognize all printed and handwritten… (more)

▼

The primary objective of this research work is to develop a character recognition system, which can further be developed to recognize all printed and handwritten Tamil characters. Such a system will be extremely useful to digitize innumerable ancient documents available both on paper and palm leaves and are dated few hundred years back. The problem considered is to develop a recognition system for Printed and Handwritten Tamil and Numeral Characters. An overview of the research work is presented below. The first component of this research work focuses on technique to recognize handwritten Tamil characters using a Hidden Markov Model (HMM) approach, for a subset of the Tamil alphabet. The second component of this research work also focuses on technique to recognize handwritten Tamil characters using an Octal Graph approach. The third component introduces a methodology to recognize handwritten characters using Structural Hidden Markov Models (SHMM). The proposed approach is motivated by the need to model complex structures which are encountered in many areas such as speech/handwriting recognition, content-based information retrieval etc. The observations considered are strings that produce the structures. These observations are related in the sense they all contribute to produce a particular structure. The recognition efficiency of the system is 96.5%. The results reported in this component shows that the Structural Hidden Markov Model (SHMM) produces better recognition than the Hidden Markov Model. A character recognition system was successfully developed for subset of Tamil characters and numerals and found to perform reasonably well with sufficient accuracy. With this preliminary study of character recognition systems it is planned to continue the work in future. A more complete OCR will evolve in public domain with this work as the starting point, so that the primary source of motivation will benefit from the efforts.

None

Advisors/Committee Members: Suresh R M.

Subjects/Keywords: Structural Hidden Markov Model; Hidden Markov Model; Character recognition system; Tamil character

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APA (6^th Edition):

R, J. K. (2013). Handwritten character recognition using hybrid techniques. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/9849

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

R, Jagadeesh Kannan. “Handwritten character recognition using hybrid techniques.” 2013. Thesis, Anna University. Accessed March 24, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/9849.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

R, Jagadeesh Kannan. “Handwritten character recognition using hybrid techniques.” 2013. Web. 24 Mar 2019.

Vancouver:

R JK. Handwritten character recognition using hybrid techniques. [Internet] [Thesis]. Anna University; 2013. [cited 2019 Mar 24]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9849.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

R JK. Handwritten character recognition using hybrid techniques. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9849

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

12. Ali Akbar Soltan, Reza. Enhancements in Markovian Dynamics.

Degree: PhD, Mechanical Engineering, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/77345

► Many common statistical techniques for modeling multidimensional dynamic data sets can be seen as variants of one (or multiple) underlying linear/nonlinear model(s). These statistical techniques… (more)

▼ Many common statistical techniques for modeling multidimensional dynamic data sets can be seen as variants of one (or multiple) underlying linear/nonlinear model(s). These statistical techniques fall into two broad categories of supervised and unsupervised learning. The emphasis of this dissertation is on unsupervised learning under multiple generative models. For linear models, this has been achieved by collective observations and derivations made by previous authors during the last few decades. Factor analysis, polynomial chaos expansion, principal component analysis, gaussian mixture clustering, vector quantization, and Kalman filter models can all be unified as some variations of unsupervised learning under a single basic linear generative model. Hidden Markov modeling (HMM), however, is categorized as an unsupervised learning under multiple linear/nonlinear generative models. This dissertation is primarily focused on hidden Markov models (HMMs). On the first half of this dissertation we study enhancements on the theory of hidden Markov modeling. These include three branches: 1) a robust as well as a closed-form parameter estimation solution to the expectation maximization (EM) process of HMMs for the case of elliptically symmetrical densities; 2) a two-step HMM, with a combined state sequence via an extended Viterbi algorithm for smoother state estimation; and 3) a duration-dependent HMM, for estimating the expected residency frequency on each state. Then, the second half of the dissertation studies three novel applications of these methods: 1) the applications of Markov switching models on the Bifurcation Theory in nonlinear dynamics; 2) a Game Theory application of HMM, based on fundamental theory of card counting and an example on the game of Baccarat; and 3) Trust modeling and the estimation of trustworthiness metrics in cyber security systems via Markov switching models. As a result of the duration dependent HMM, we achieved a better estimation for the expected duration of stay on each regime. Then by robust and closed form solution to the EM algorithm we achieved robustness against outliers in the training data set as well as higher computational efficiency in the maximization step of the EM algorithm. By means of the two-step HMM we achieved smoother probability estimation with higher likelihood than the standard HMM. Advisors/Committee Members: Ahmadian, Mehdi (committeechair), Hall, T. Simin (committee member), Southward, Steve C. (committee member), Asl, Farshid M. (committee member), Ball, Joseph A. (committee member), Taheri, Saied (committee member).

Subjects/Keywords: Duration Dependent Hidden Markov; Expectation Maximization; Nonlinear Stochastic Model; Hidden Markov Model; Maximum Likelihood Estimation

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APA (6^th Edition):

Ali Akbar Soltan, R. (2012). Enhancements in Markovian Dynamics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77345

Chicago Manual of Style (16^th Edition):

Ali Akbar Soltan, Reza. “Enhancements in Markovian Dynamics.” 2012. Doctoral Dissertation, Virginia Tech. Accessed March 24, 2019. http://hdl.handle.net/10919/77345.

MLA Handbook (7^th Edition):

Ali Akbar Soltan, Reza. “Enhancements in Markovian Dynamics.” 2012. Web. 24 Mar 2019.

Vancouver:

Ali Akbar Soltan R. Enhancements in Markovian Dynamics. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/10919/77345.

Council of Science Editors:

Ali Akbar Soltan R. Enhancements in Markovian Dynamics. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/77345

13. Treinen, James J. Heuristics For Improved Enterprise Intrusion Detection.

Degree: PhD, Computer Science, 2009, U of Denver

URL: https://digitalcommons.du.edu/etd/657

► One of the greatest challenges facing network operators today is the identification of malicious activity on their networks. The current approach is to deploy… (more)

▼ One of the greatest challenges facing network operators today is the identification of malicious activity on their networks. The current approach is to deploy a set of intrusion detection sensors (IDSs) in various locations throughout the network and on strategic hosts. Unfortunately, the available intrusion detection technologies generate an overwhelming volume of false alarms, making the task of identifying genuine attacks nearly impossible. This problem is very difficult to solve even in networks of nominal size. The task of uncovering attacks in enterprise class networks quickly becomes unmanageable. Research on improving intrusion detection sensors is ongoing, but given the nature of the problem to be solved, progress is slow. Research simultaneously continues in the field of mining the set of alarms produced by IDS sensors. Varying techniques have been proposed to aggregate, correlate, and classify the alarms in ways that make the end result more concise and digestible for human analysis. To date, the majority of these techniques have been successful only in networks of modest size. As a means of extending this research to real world, enterprise scale networks, we propose 5 heuristics supporting a three-pronged approach to the systematic evaluation of large intrusion detection logs. Primarily, we provide a set of algorithms to assist operations personnel in the daunting task of ensuring that no true attack goes unnoticed. Secondly, we provide information that can be used to tune the sensors which are deployed on the network, reducing the overall alarm volume, thus mitigating the monitoring costs both in terms of hardware and labor, and improving overall accuracy. Third, we provide a means of discovering stages of attacks that were overlooked by the analyst, based on logs of known security incidents. Our techniques work by applying a combination of graph algorithms and Markovian stochastic processes to perform probabilistic analysis as to whether an alarm is a true or false positive. Using these techniques it is possible to significantly reduce the total number of alarms and hosts which must be examined manually, while simultaneously discovering attacks that had previously gone unnoticed. The proposed algorithms are also successful at the discovery of new profiles for multi-stage attacks, and can be used in the automatic generation of meta-alarms, or rules to assist the monitoring infrastructure in performing automated analysis. We demonstrate that it is possible to successfully rank hosts which comprise the vertices of an Alarm Graph in a manner such that those hosts which are of highest risk for being involved in attack are immediately highlighted for examination or inclusion on hot lists. We close with an evaluation of 3 sensor profiling algorithms, and show that the order in which alarms are generated is tightly coupled with whether or not they are false positives. We show that by using time based Markovian analysis of the alarms, we are able to identify alarms which have… Advisors/Committee Members: Ramakrishna Thurimella.

Subjects/Keywords: Anomaly Detection; Hidden Markov Model; Intrusion Detection; Markov Chain; Misuse Detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Treinen, J. J. (2009). Heuristics For Improved Enterprise Intrusion Detection. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/657

Chicago Manual of Style (16^th Edition):

Treinen, James J. “Heuristics For Improved Enterprise Intrusion Detection.” 2009. Doctoral Dissertation, U of Denver. Accessed March 24, 2019. https://digitalcommons.du.edu/etd/657.

MLA Handbook (7^th Edition):

Treinen, James J. “Heuristics For Improved Enterprise Intrusion Detection.” 2009. Web. 24 Mar 2019.

Vancouver:

Treinen JJ. Heuristics For Improved Enterprise Intrusion Detection. [Internet] [Doctoral dissertation]. U of Denver; 2009. [cited 2019 Mar 24]. Available from: https://digitalcommons.du.edu/etd/657.

Council of Science Editors:

Treinen JJ. Heuristics For Improved Enterprise Intrusion Detection. [Doctoral Dissertation]. U of Denver; 2009. Available from: https://digitalcommons.du.edu/etd/657

University of Illinois – Chicago

14. Huang, Ziqian. Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.

Degree: 2012, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/9611

► Segmentation refers to the assignment of each consumer to a set of similar consumers. The formation of the sets and the assignments are done simultaneously… (more)

Subjects/Keywords: Segmentation; Finite Mixture Model; Mixture Logit Model; Hidden Markov Model; Quantitative Marketing Model

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APA (6^th Edition):

Huang, Z. (2012). Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Huang, Ziqian. “Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.” 2012. Thesis, University of Illinois – Chicago. Accessed March 24, 2019. http://hdl.handle.net/10027/9611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Huang, Ziqian. “Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.” 2012. Web. 24 Mar 2019.

Vancouver:

Huang Z. Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. [Internet] [Thesis]. University of Illinois – Chicago; 2012. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/10027/9611.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang Z. Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. [Thesis]. University of Illinois – Chicago; 2012. Available from: http://hdl.handle.net/10027/9611

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Nevada – Las Vegas

15. Dasu, Nagendra Abhinav. Implementation of hidden semi-Markov models.

Degree: MSin Computer Science, Computer Science, 2011, University of Nevada – Las Vegas

URL: https://digitalscholarship.unlv.edu/thesesdissertations/997

► One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a… (more)

Subjects/Keywords: Abhinav thesis; Computer simulation; Hidden Markov model; Hidden semi-Markov model; HSMM; Implementation of HSMM; Markov processes; Mathematical models; Semi-Markov; Stochastic processes; Applied Mathematics; Computer Sciences; Theory and Algorithms

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APA (6^th Edition):

Dasu, N. A. (2011). Implementation of hidden semi-Markov models. (Masters Thesis). University of Nevada – Las Vegas. Retrieved from https://digitalscholarship.unlv.edu/thesesdissertations/997

Chicago Manual of Style (16^th Edition):

Dasu, Nagendra Abhinav. “Implementation of hidden semi-Markov models.” 2011. Masters Thesis, University of Nevada – Las Vegas. Accessed March 24, 2019. https://digitalscholarship.unlv.edu/thesesdissertations/997.

MLA Handbook (7^th Edition):

Dasu, Nagendra Abhinav. “Implementation of hidden semi-Markov models.” 2011. Web. 24 Mar 2019.

Vancouver:

Dasu NA. Implementation of hidden semi-Markov models. [Internet] [Masters thesis]. University of Nevada – Las Vegas; 2011. [cited 2019 Mar 24]. Available from: https://digitalscholarship.unlv.edu/thesesdissertations/997.

Council of Science Editors:

Dasu NA. Implementation of hidden semi-Markov models. [Masters Thesis]. University of Nevada – Las Vegas; 2011. Available from: https://digitalscholarship.unlv.edu/thesesdissertations/997

University of Illinois – Urbana-Champaign

16. Khosrowpour, Ardalan. Vision-based workface assessment using depth images for activity analysis of interior construction operations.

Degree: MS, 0106, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/46892

► Workface assessment –the process of determining the overall activity rates of onsite construction workers throughout a day– typically involves manual visual observations which are time-consuming… (more)

▼ Workface assessment –the process of determining the overall activity rates of onsite construction workers throughout a day– typically involves manual visual observations which are time-consuming and labor-intensive. To minimize subjectivity and the time required for conducting detailed assessments, and allowing managers to spend their time on the more important task of assessing and implementing improvements, we propose a new inexpensive vision-based method using RGB-D sensors that is applicable to interior construction operations. This is particularly a challenging task as construction activities have a large range of intra-class variability including varying sequences of body posture and time-spent on each individual activity. On the other hand, the state-of-the-art skeleton extraction algorithms from RGB-D sequences are not robust enough especially when workers interact with tools or self-occlude the camera’s field-of-view. Existing vision-based methods are also rather limited as they can primarily classify “atomic” activities from RGB-D sequences involving one worker conducting a single activity. To address these limitations, our proposed original method involves three main components: 1) an algorithm for detecting, tracking, and extracting body skeleton features from depth images; 2) A discriminative bag-of-poses activity classifier trained using multiple Support Vector Machines for classifying single visual activities from a given body skeleton sequence; and 3) a Hidden Markov model with a Kernel Density Estimation function to represent emission probabilities in form of a statistical distribution of single activity classifiers. For training and testing purposes, we also introduce a new dataset of eleven RGB-D sequences for interior drywall construction operations involving three actual construction workers conducting eight different activities in various interior locations. Our experimental results with an average accuracy of 76% on the testing dataset show the promise of vision-based methods using RGB-D sequences for facilitating the activity analysis workface assessment. Advisors/Committee Members: Golparvar-Fard, Mani (advisor).

Subjects/Keywords: Activity Analysis; Workface Assessment; RGB-D (RedGreenBlue-Depth) cameras; Hidden Markov ModelActivity analysis; Hidden Markov Model

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APA (6^th Edition):

Khosrowpour, A. (2014). Vision-based workface assessment using depth images for activity analysis of interior construction operations. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46892

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Khosrowpour, Ardalan. “Vision-based workface assessment using depth images for activity analysis of interior construction operations.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed March 24, 2019. http://hdl.handle.net/2142/46892.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Khosrowpour, Ardalan. “Vision-based workface assessment using depth images for activity analysis of interior construction operations.” 2014. Web. 24 Mar 2019.

Vancouver:

Khosrowpour A. Vision-based workface assessment using depth images for activity analysis of interior construction operations. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2142/46892.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khosrowpour A. Vision-based workface assessment using depth images for activity analysis of interior construction operations. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46892

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

17. Zhu, Wenhan. Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences.

Degree: PhD, Biology, 2010, Georgia Tech

URL: http://hdl.handle.net/1853/33869

► A metagenome originated from a shotgun sequencing of a microbial community is a heterogeneous mixture of rather short sequences. A vast majority of microbial species… (more)

Subjects/Keywords: Codon usage; Hidden Markov model; Gene prediction; GeneMark; Metagenomics; Gene finding; Markov processes; Genetics

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APA (6^th Edition):

Zhu, W. (2010). Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33869

Chicago Manual of Style (16^th Edition):

Zhu, Wenhan. “Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences.” 2010. Doctoral Dissertation, Georgia Tech. Accessed March 24, 2019. http://hdl.handle.net/1853/33869.

MLA Handbook (7^th Edition):

Zhu, Wenhan. “Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences.” 2010. Web. 24 Mar 2019.

Vancouver:

Zhu W. Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1853/33869.

Council of Science Editors:

Zhu W. Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33869

18. Le Corff, Sylvain. Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.

Degree: Docteur es, Signal et Images, 2012, Paris, ENST

URL: http://www.theses.fr/2012ENST0052

►

Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en… (more)

▼

Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en ligne (sans sauvegarde des observations) au sens du maximum de vraisemblance. Nous proposons une nouvelle méthode basée sur l'algorithme Expectation Maximization appelée Block Online Expectation Maximization (BOEM). Cet algorithme est défini pour des chaînes de Markov cachées à espace d'état et espace d'observations généraux. Dans le cas d'espaces d'états généraux, l'algorithme BOEM requiert l'introduction de méthodes de Monte Carlo séquentielles pour approcher des espérances sous des lois de lissage. La convergence de l'algorithme nécessite alors un contrôle de la norme Lp de l'erreur d'approximation Monte Carlo explicite en le nombre d'observations et de particules. Une seconde partie de cette thèse se consacre à l'obtention de tels contrôles pour plusieurs méthodes de Monte Carlo séquentielles. Nous étudions enfin des applications de l'algorithme BOEM à des problèmes de cartographie et de localisation simultanées. La dernière partie de cette thèse est relative à l'estimation non paramétrique dans les chaînes de Markov cachées. Le problème considéré est abordé dans un cadre précis. Nous supposons que (Xk) est une marche aléatoire dont la loi des incréments est connue à un facteur d'échelle a près. Nous supposons que, pour tout k, Yk est une observation de f(Xk) dans un bruit additif gaussien, où f est une fonction que nous cherchons à estimer. Nous établissons l'identifiabilité du modèle statistique et nous proposons une estimation de f et de a à partir de la vraisemblance par paires des observations.

This document is dedicated to inference problems in hidden Markov models. The first part is devoted to an online maximum likelihood estimation procedure which does not store the observations. We propose a new Expectation Maximization based method called the Block Online Expectation Maximization (BOEM) algorithm. This algorithm solves the online estimation problem for general hidden Markov models. In complex situations, it requires the introduction of Sequential Monte Carlo methods to approximate several expectations under the fixed interval smoothing distributions. The convergence of the algorithm is shown under the assumption that the Lp mean error due to the Monte Carlo approximation can be controlled explicitly in the number of observations and in the number of particles. Therefore, a second part of the document establishes such controls for several Sequential Monte Carlo algorithms. This BOEM algorithm is then used to solve the simultaneous localization and mapping problem in different frameworks. Finally, the last part of this thesis is dedicated to nonparametric estimation in hidden Markov models. It is assumed that the Markov chain (Xk) is a random walk lying in a compact set with increment distribution known up to a scaling factor a. At each time step k, Yk is a noisy observations of f(Xk) where f is an unknown function. We establish the…

Advisors/Committee Members: Moulines, Éric (thesis director), Fort, Gersende (thesis director).

Subjects/Keywords: Modèle de Markov caché; Estimation non-paramétrique; Hidden Markov model; Non-parametric estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Le Corff, S. (2012). Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2012ENST0052

Chicago Manual of Style (16^th Edition):

Le Corff, Sylvain. “Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.” 2012. Doctoral Dissertation, Paris, ENST. Accessed March 24, 2019. http://www.theses.fr/2012ENST0052.

MLA Handbook (7^th Edition):

Le Corff, Sylvain. “Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.” 2012. Web. 24 Mar 2019.

Vancouver:

Le Corff S. Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. [Internet] [Doctoral dissertation]. Paris, ENST; 2012. [cited 2019 Mar 24]. Available from: http://www.theses.fr/2012ENST0052.

Council of Science Editors:

Le Corff S. Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. [Doctoral Dissertation]. Paris, ENST; 2012. Available from: http://www.theses.fr/2012ENST0052

19. Khemiri, Houssemeddine. Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.

Degree: Docteur es, Signal et images, 2013, Paris, ENST

URL: http://www.theses.fr/2013ENST0055

►

La quantité de données audio disponibles, telles que les enregistrements radio, la musique, les podcasts et les publicités est en augmentation constance. Par contre, il… (more)

▼

La quantité de données audio disponibles, telles que les enregistrements radio, la musique, les podcasts et les publicités est en augmentation constance. Par contre, il n'y a pas beaucoup d'outils de classification et d'indexation, qui permettent aux utilisateurs de naviguer et retrouver des documents audio. Dans ces systèmes, les données audio sont traitées différemment en fonction des applications. La diversité de ces techniques d'indexation rend inadéquat le traitement simultané de flux audio où différents types de contenu audio coexistent. Dans cette thèse, nous présentons nos travaux sur l'extension de l'approche ALISP, développé initialement pour la parole, comme une méthode générique pour l'indexation et l'identification audio. La particularité des outils ALISP est qu'aucune transcription textuelle ou annotation manuelle est nécessaire lors de l'étape d'apprentissage. Le principe de cet outil est de transformer les données audio en une séquence de symboles. Ces symboles peuvent être utilisés à des fins d'indexation. La principale contribution de cette thèse est l'exploitation de l'approche ALISP comme une méthode générique pour l'indexation audio. Ce système est composé de trois modules: acquisition et modélisation des unités ALISP d'une manière non supervisée, transcription ALISP des données audio et comparaison des symboles ALISP avec la technique BLAST et la distance de Levenshtein. Les évaluations du système proposé pour les différentes applications sont effectuées avec la base de données YACAST et avec d'autres corpus disponibles publiquement pour différentes tâche de l'indexation audio.

The amount of available audio data, such as broadcast news archives, radio recordings, music and songs collections, podcasts or various internet media is constantly increasing. Therefore many audio indexing techniques are proposed in order to help users to browse audio documents. Nevertheless, these methods are developed for a specific audio content which makes them unsuitable to simultaneously treat audio streams where different types of audio document coexist. In this thesis we report our recent efforts in extending the ALISP approach developed for speech as a generic method for audio indexing, retrieval and recognition. The particularity of ALISP tools is that no textual transcriptions are needed during the learning step. Any input speech data is transformed into a sequence of arbitrary symbols. These symbols can be used for indexing purposes. The main contribution of this thesis is the exploitation of the ALISP approach as a generic method for audio indexing. The proposed system consists of three steps; an unsupervised training to model and acquire the ALISP HMM models, ALISP segmentation of audio data using the ALISP HMM models and a comparison of ALISP symbols using the BLAST algorithm and Levenshtein distance. The evaluations of the proposed systems are done on the YACAST and other publicly available corpora for several tasks of audio indexing.

Advisors/Committee Members: Chollet, Gérard (thesis director), Petrovska-Delacrétaz, Dijana (thesis director).

Subjects/Keywords: Signal audio; Modèle de Markov caché; ALISP segmentation; Audio signal; Hidden Markov model; Segmentation ALISP

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Khemiri, H. (2013). Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0055

Chicago Manual of Style (16^th Edition):

Khemiri, Houssemeddine. “Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.” 2013. Doctoral Dissertation, Paris, ENST. Accessed March 24, 2019. http://www.theses.fr/2013ENST0055.

MLA Handbook (7^th Edition):

Khemiri, Houssemeddine. “Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.” 2013. Web. 24 Mar 2019.

Vancouver:

Khemiri H. Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2019 Mar 24]. Available from: http://www.theses.fr/2013ENST0055.

Council of Science Editors:

Khemiri H. Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0055

University of Waterloo

20. Raffa, Jesse Daniel. Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.

Degree: 2013, University of Waterloo

URL: http://hdl.handle.net/10012/7255

► Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal… (more)

Subjects/Keywords: multivariate longitudinal data; hidden markov model; random effects; Markov chain monte carlo

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APA (6^th Edition):

Raffa, J. D. (2013). Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Raffa, Jesse Daniel. “Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.” 2013. Thesis, University of Waterloo. Accessed March 24, 2019. http://hdl.handle.net/10012/7255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Raffa, Jesse Daniel. “Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.” 2013. Web. 24 Mar 2019.

Vancouver:

Raffa JD. Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/10012/7255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raffa JD. Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

George Mason University

21. Nguyen, Thao Tran Nhu. Hidden Markov model based spectrum sensing for cognitive radio .

Degree: 2013, George Mason University

URL: http://hdl.handle.net/1920/8288

► Cognitive radio is an emerging technology for sensing and opportunistic spectrum access in wireless communication networks. It allows a secondary user to detect under-utilized spectrum… (more)

Subjects/Keywords: Engineering; Baum algorithm; Bivariate Markov chain; Cognitive radio; Hidden Markov model; Spectrum Sensing

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APA (6^th Edition):

Nguyen, T. T. N. (2013). Hidden Markov model based spectrum sensing for cognitive radio . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8288

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nguyen, Thao Tran Nhu. “Hidden Markov model based spectrum sensing for cognitive radio .” 2013. Thesis, George Mason University. Accessed March 24, 2019. http://hdl.handle.net/1920/8288.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nguyen, Thao Tran Nhu. “Hidden Markov model based spectrum sensing for cognitive radio .” 2013. Web. 24 Mar 2019.

Vancouver:

Nguyen TTN. Hidden Markov model based spectrum sensing for cognitive radio . [Internet] [Thesis]. George Mason University; 2013. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1920/8288.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nguyen TTN. Hidden Markov model based spectrum sensing for cognitive radio . [Thesis]. George Mason University; 2013. Available from: http://hdl.handle.net/1920/8288

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

22. Baharian Khoshkhou, Golshid. Stochastic sequential assignment problem.

Degree: PhD, 0127, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/50503

► The stochastic sequential assignment problem (SSAP) studies the allocation of available distinct workers with deterministic values to sequentially-arriving tasks with stochastic parameters so as to… (more)

▼ The stochastic sequential assignment problem (SSAP) studies the allocation of available distinct workers with deterministic values to sequentially-arriving tasks with stochastic parameters so as to maximize the expected total reward obtained from the assignments. The difficulty and challenge in making the assignment decisions is that the assignments are performed in real-time; specifically, pairing a worker with a task is done without knowledge of future task values. This thesis focuses on studying practical variations and extensions of the SSAP, with the goal of eliminating restricting assumptions so that the problem setting converges to that of real-world problems. The existing SSAP literature considers a risk-neutral objective function, seeking an assignment policy to maximize the expected total reward; however, a risk-neutral objective function is not always desirable for the decision-maker since the probability distribution function (pdf) of the total reward might carry a high probability of low values. To take this issue into account, the first part of this dissertation studies the SSAP under a risk-sensitive objective function. Specifically, the assignments are performed so as to minimize the threshold probability, which is the probability of the total reward failing to achieve a specified target (threshold). A target-dependent Markov decision process (MDP) is solved, and sufficient conditions for the existence of a deterministic Markov optimal policy are provided. An approximate algorithm is presented, and convergence of the approximate value function to the optimal value function is established under mild conditions. The second part of this thesis analyzes the limiting behavior of the SSAP as the number of assignments approaches infinity. The optimal assignment policy for the basic SSAP has a threshold structure and involves computing a new set of breakpoints upon the arrival of each task, which is cumbersome for large-scale problems. To address this issue, the second part of this dissertation focuses on obtaining stationary (time-independent) optimal assignment policies that maximize the long-run expected reward per task and are much easier to perform in real-world problems. An exponential convergence rate is established for the convergence of the expected total reward per task to the optimal value as the number of tasks approaches infinity. The limiting behavior of the SSAP is studied in two different settings. The first setting assumes an independent and identically distributed (IID) sequence of arriving tasks with observable distribution functions, while the second problem considers the case where task distributions are unobservable and belong to a pool of feasible distributions. The next part of this dissertation basically brings the first two parts together, studying the limiting behavior of the target-dependent SSAP, where the goal is finding an assignment policy that minimizes the probability of the long-run reward per task failing to achieve a given target value. It is proven that the… Advisors/Committee Members: Jacobson, Sheldon H. (advisor), Chen, Xin (Committee Chair), Jacobson, Sheldon H. (committee member), Kiyavash, Negar (committee member), Shanbhag, Vinayak V. (committee member).

Subjects/Keywords: sequential assignment; Markov decision process; stationary policy; hidden Markov model; threshold criteria; risk measure

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Baharian Khoshkhou, G. (2014). Stochastic sequential assignment problem. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50503

Chicago Manual of Style (16^th Edition):

Baharian Khoshkhou, Golshid. “Stochastic sequential assignment problem.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 24, 2019. http://hdl.handle.net/2142/50503.

MLA Handbook (7^th Edition):

Baharian Khoshkhou, Golshid. “Stochastic sequential assignment problem.” 2014. Web. 24 Mar 2019.

Vancouver:

Baharian Khoshkhou G. Stochastic sequential assignment problem. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/2142/50503.

Council of Science Editors:

Baharian Khoshkhou G. Stochastic sequential assignment problem. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50503

University of New Orleans

23. Baribault, Carl. Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification.

Degree: PhD, Computer Science, 2009, University of New Orleans

URL: https://scholarworks.uno.edu/td/1098

► Using a generalized-clique hidden Markov model (HMM) as the starting point for a eukaryotic gene finder, the objective here is to strengthen the signal information… (more)

▼ Using a generalized-clique hidden Markov model (HMM) as the starting point for a eukaryotic gene finder, the objective here is to strengthen the signal information at the transitions between coding and non-coding (c/nc) regions. This is done by enlarging the primitive hidden states associated with individual base labeling (as exon, intron, or junk) to substrings of primitive hidden states or footprint states. Moreover, the allowed footprint transitions are restricted to those that include either one c/nc transition or none at all. (This effectively imposes a minimum length on exons and the other regions.) These footprint states allow the c/nc transitions to be seen sooner and have their contributions to the gene-structure identification weighted more heavily – yet contributing as such with a natural weighting determined by the HMM model itself according to the training data – rather than via introducing an artificial gain-parameter tuning on major transitions. The selection of the generalized HMM model is interpolated to highest Markov order on emission probabilities, and to highest Markov order (subsequence length) on the footprint states. The former is accomplished via simple count cutoff rules, the latter via an identification of anomalous base statistics near the major transitions using Shannon entropy. Preliminary indications, from applications to the C. elegans genome, are that the sensitivity/specificity (SN/SP) result for both the individual state and full exon predictions are greatly enhanced using the generalized-clique HMM when compared to the standard HMM. Here the standard HMM is represented by the choice of the smallest size of footprint state in the generalized-clique HMM. Even with these improvements, we observe that both extremely long and short exon and intron segments would go undetected without an explicit model of the duration of state. The key contributions of this effort are the full derivation and experimental confirmation of a rudimentary, yet powerful and competitive gene finding method based on a higher order hidden Markov model. With suitable extensions, this method is expected to provide superior gene finding capability – not only in the context of pre-conditioned data sets as in the evaluations cited but also in the wider context of less preconditioned and/or raw genomic data. Advisors/Committee Members: Winters-Hilt, Stephen, Summa, Christopher, Zhu, Dong-Xiao.

Subjects/Keywords: hidden Markov model; HMM; GHMM; gene finding; gene prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Baribault, C. (2009). Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification. (Doctoral Dissertation). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/1098

Chicago Manual of Style (16^th Edition):

Baribault, Carl. “Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification.” 2009. Doctoral Dissertation, University of New Orleans. Accessed March 24, 2019. https://scholarworks.uno.edu/td/1098.

MLA Handbook (7^th Edition):

Baribault, Carl. “Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification.” 2009. Web. 24 Mar 2019.

Vancouver:

Baribault C. Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification. [Internet] [Doctoral dissertation]. University of New Orleans; 2009. [cited 2019 Mar 24]. Available from: https://scholarworks.uno.edu/td/1098.

Council of Science Editors:

Baribault C. Meta State Generalized Hidden Markov Model for Eukaryotic Gene Structure Identification. [Doctoral Dissertation]. University of New Orleans; 2009. Available from: https://scholarworks.uno.edu/td/1098

University of Alberta

24. Hladky, Stephen Michael. Predicting opponent locations in first-person shooter video games.

Degree: MS, Department of Computing Science, 2009, University of Alberta

URL: https://era.library.ualberta.ca/files/5x21tg037

► Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players,… (more)

Subjects/Keywords: opponent modelling; particle filter; hidden semi-Markov model; believability; video games

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APA (6^th Edition):

Hladky, S. M. (2009). Predicting opponent locations in first-person shooter video games. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/5x21tg037

Chicago Manual of Style (16^th Edition):

Hladky, Stephen Michael. “Predicting opponent locations in first-person shooter video games.” 2009. Masters Thesis, University of Alberta. Accessed March 24, 2019. https://era.library.ualberta.ca/files/5x21tg037.

MLA Handbook (7^th Edition):

Hladky, Stephen Michael. “Predicting opponent locations in first-person shooter video games.” 2009. Web. 24 Mar 2019.

Vancouver:

Hladky SM. Predicting opponent locations in first-person shooter video games. [Internet] [Masters thesis]. University of Alberta; 2009. [cited 2019 Mar 24]. Available from: https://era.library.ualberta.ca/files/5x21tg037.

Council of Science Editors:

Hladky SM. Predicting opponent locations in first-person shooter video games. [Masters Thesis]. University of Alberta; 2009. Available from: https://era.library.ualberta.ca/files/5x21tg037

University of Kansas

25. Senf, Alexander J. A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS.

Degree: PhD, Electrical Engineering & Computer Science, 2011, University of Kansas

URL: http://hdl.handle.net/1808/7639

► Microarray technology captures the rate of expression of genes under varying experimental conditions. Genes encode the information necessary to build proteins; proteins used by cellular… (more)

▼ Microarray technology captures the rate of expression of genes under varying experimental conditions. Genes encode the information necessary to build proteins; proteins used by cellular functions exhibit higher rates of expression for the associated genes. If multiple proteins are required for a particular function then their genes show a pattern of coexpression during time periods when the function is active within a cell. Cellular functions are generally complex and require groups of genes to cooperate; these groups of genes are called functional modules. Modular organization of genetic functions has been evident since 1999. Detecting functionally related genes in a genome and detecting all genes belonging to particular functional modules are current research topics in this field. The number of microarray gene expression datasets available in public repositories increases rapidly, and advances in technology have now made it feasible to routinely perform whole-genome studies where the behavior of every gene in a genome is captured. This promises a wealth of biological and medical information, but making the amount of data accessible to researchers requires intelligent and efficient computational algorithms. Researchers working on specific cellular functions would benefit from this data if it was possible to quickly extract information useful to their area of research. This dissertation develops a machine learning algorithm that allows one or multiple microarray data sets to be queried with a set of known and functionally related input genes in order to detect additional genes participating in the same or closely related functions. The focus is on time-series microarray datasets where gene expression values are obtained from the same experiment over a period of time from a series of sequential measurements. A feature selection algorithm selects relevant time steps where the provided input genes exhibit correlated expression behavior. Time steps are the columns in microarray data sets, rows list individual genes. A specific linear Hidden Markov Model (HMM) is then constructed to contain one hidden state for each of the selected experiments and is trained using the expression values of the input genes from the microarray. Given the trained HMM the probability that a sequence of gene expression values was generated by that particular HMM can be calculated. This allows for the assignment of a probability score for each gene in the microarray. High-scoring genes are included in the result set (of genes with functional similarities to the input genes.) P-values can be calculated by repeating this algorithm to train multiple individual HMMs using randomly selected genes as input genes and calculating a Parzen Density Function (PDF) from the probability scores of all HMMs for each gene. A feedback loop uses the result generated from one algorithm run as input set for another iteration of the algorithm. This iterated HMM algorithm allows for the characterization of functional modules from very small input sets and for weak… Advisors/Committee Members: Chen, Xue-wen (advisor), Huan, Jun (cmtemember), Miller, James (cmtemember), Agah, Arvin (cmtemember), Vakser, Ilya (cmtemember).

Subjects/Keywords: Bioinformatics; Computer science; Hidden Markov model; Hmm; Machine learning; Microarray

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APA (6^th Edition):

Senf, A. J. (2011). A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/7639

Chicago Manual of Style (16^th Edition):

Senf, Alexander J. “A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS.” 2011. Doctoral Dissertation, University of Kansas. Accessed March 24, 2019. http://hdl.handle.net/1808/7639.

MLA Handbook (7^th Edition):

Senf, Alexander J. “A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS.” 2011. Web. 24 Mar 2019.

Vancouver:

Senf AJ. A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS. [Internet] [Doctoral dissertation]. University of Kansas; 2011. [cited 2019 Mar 24]. Available from: http://hdl.handle.net/1808/7639.

Council of Science Editors:

Senf AJ. A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS. [Doctoral Dissertation]. University of Kansas; 2011. Available from: http://hdl.handle.net/1808/7639

Univerzitet u Beogradu

26. Kisić, Emilija A. 1984-. Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema.

Degree: Elektrotehnički fakultet, 2016, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:12044/bdef:Content/get

►

Elektrotehnika i računarstvo - Upravljanje sistemima i obrada signala /Electrical and Computer Engineering - System control and signal processing

U današnjoj industriji primena najboljih strategija… (more)

▼

Elektrotehnika i računarstvo - Upravljanje sistemima i obrada signala /Electrical and Computer Engineering - System control and signal processing

U današnjoj industriji primena najboljih strategija odrţavanja je veoma vaţan zadatak, kako bi se obezbedila stabilnost i pouzdanost tehničkih sistema. U literaturi se moţe naći veliki broj radova i knjiga o različitim strategijama odrţavanja i uglavnom se svuda ističe prednost prediktivnog odrţavanja (eng. predictive maintenance) u odnosu na planirano odrţavanje (eng. time-based maintenance). Prediktivno odrţavanje produţava vreme u kojem sistem radi dobro, smanjuje nepotrebno zaustavljanje sistema, redukuje materijalne gubitke i sprečava nastajanje katastrofalnih otkaza. Iako je sa razvojem visoko sofisticiranih tehnologija ova oblast istraţivanja veoma uznapredovala, i dalje ima puno prostora za poboljšanje postojećih tehnika, kao i za razvoj novih. U ovoj tezi predloţena je inovativna tehnika prediktivnog odrţavanja sa ciljem isticanja prednosti prediktivnog odrţavanja u odnosu na planirano odrţavanje. Predloţeni algoritam primenjen je na konkretan problem koji se javlja planiranim odrţavanjem udarnih ploča mlinova koje melju ugalj u podsistemu za mlevenje uglja u termoelektrani “TEKO”, Kostolac, Srbija. Planiranim odrţavanjem predviĎa se zamena udarnih ploča nakon odreĎenog broja radnih sati, meĎutim u zavisnosti od kvaliteta uglja i same udarne ploče ova zamena je nekad potrebna ranije ili kasnije. Posledice ovakvog odrţavanja su veliki materijalni gubici koji nastaju zbog čestog prevremenog zaustavljanja celog podsistema za mlevenje uglja, kao i mogućnost da otkaz nastupi pre zamene. Prvi korak u primeni predloţenog algoritma bio je sakupljanje podataka, odnosno snimanje akustičkih signala. Akustički signali su snimljeni pomoću usmerenog mikrofona, dok je podsistem za mlevenje uglja u funkciji, bez ometanja rada mlina. Sakupljeni podaci zatim su obraĎeni izdvajanjem korisnih parametara akustičkih signala koji su predstavljeni u frekvencijskom domenu pomoću spektrograma (eng. spectrogram). Inovativnost predloţenog algoritma zasniva se na izboru tehnike prognoze otkaza (eng. failure prognostics). Izabrana je tehnika koja je zasnovana na pokretnim podacima (eng. data driven), zato što se ne traţi poznavanje modela, a dostupni su nam podaci nadgledanja stanja (snimljeni akustički signali). Najpre...

Advisors/Committee Members: Đurović, Željko, 1964-.

Subjects/Keywords: Predictive maintenance. T2 control chart. Hidden Markov Model. Acoustic signals

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APA (6^th Edition):

Kisić, E. A. 1. (2016). Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:12044/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kisić, Emilija A 1984-. “Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema.” 2016. Thesis, Univerzitet u Beogradu. Accessed March 24, 2019. https://fedorabg.bg.ac.rs/fedora/get/o:12044/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kisić, Emilija A 1984-. “Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema.” 2016. Web. 24 Mar 2019.

Vancouver:

Kisić EA1. Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2019 Mar 24]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12044/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kisić EA1. Primena T2 kontrolnih dijagrama i skrivenih Markovljevih modela na prediktivno održavanje tehničkih sistema. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12044/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Univerzitet u Beogradu

27. Bujaković, Dimitrije M. 1980-. Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара.

Degree: Elektrotehnički fakultet, 2016, Univerzitet u Beogradu

URL: https://fedorabg.bg.ac.rs/fedora/get/o:12194/bdef:Content/get

Електротехника и рачунарство-Управљање системима и обрада сигнала / Electrical and Computer Engineering-System control and signal processing

Један од најзначајнијих процеса у војним применама је процена ситуације...

Advisors/Committee Members: Đurović, Željko. 1964-.

Subjects/Keywords: Classification; ground surveillance radar; hidden Markov chains; autoregressive process model

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APA (6^th Edition):

Bujaković, D. M. 1. (2016). Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:12194/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bujaković, Dimitrije M 1980-. “Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара.” 2016. Thesis, Univerzitet u Beogradu. Accessed March 24, 2019. https://fedorabg.bg.ac.rs/fedora/get/o:12194/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bujaković, Dimitrije M 1980-. “Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара.” 2016. Web. 24 Mar 2019.

Vancouver:

Bujaković DM1. Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2019 Mar 24]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12194/bdef:Content/get.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bujaković DM1. Експертски систем за праћење и препознавање циљева у окружењу извиђачких радара. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:12194/bdef:Content/get

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cincinnati

28. Jin, Chao. A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift.

Degree: MS, Engineering and Applied Science: Mechanical Engineering, 2015, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

► In the field of prognostics and health management, process monitoring is an essential technique to equip the system with the intelligence of being “aware” of… (more)

Subjects/Keywords: Mechanics; process monitoring; hidden Markov model; fault detection

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APA (6^th Edition):

Jin, C. (2015). A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

Chicago Manual of Style (16^th Edition):

Jin, Chao. “A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift.” 2015. Masters Thesis, University of Cincinnati. Accessed March 24, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.

MLA Handbook (7^th Edition):

Jin, Chao. “A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift.” 2015. Web. 24 Mar 2019.

Vancouver:

Jin C. A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift. [Internet] [Masters thesis]. University of Cincinnati; 2015. [cited 2019 Mar 24]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.

Council of Science Editors:

Jin C. A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift. [Masters Thesis]. University of Cincinnati; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

Penn State University

29. Bardakci, Ibrahim Ekrem. Approximate Realization of Hidden Markov Models.

Degree: MS, Electrical Engineering, 2014, Penn State University

URL: https://etda.libraries.psu.edu/catalog/23697

► The realization problem for hidden Markov models (HMMs) has been studied over years. However, it does not always have solution and requires the exact string… (more)

Subjects/Keywords: Hidden Markov Model; Approximate Realization; Hankel Matrix; Nuclear Norm

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APA (6^th Edition):

Bardakci, I. E. (2014). Approximate Realization of Hidden Markov Models. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/23697

Chicago Manual of Style (16^th Edition):

Bardakci, Ibrahim Ekrem. “Approximate Realization of Hidden Markov Models.” 2014. Masters Thesis, Penn State University. Accessed March 24, 2019. https://etda.libraries.psu.edu/catalog/23697.

MLA Handbook (7^th Edition):

Bardakci, Ibrahim Ekrem. “Approximate Realization of Hidden Markov Models.” 2014. Web. 24 Mar 2019.

Vancouver:

Bardakci IE. Approximate Realization of Hidden Markov Models. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Mar 24]. Available from: https://etda.libraries.psu.edu/catalog/23697.

Council of Science Editors:

Bardakci IE. Approximate Realization of Hidden Markov Models. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/23697

Clemson University

30. Bryfczynski, Samuel. BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input.

Degree: PhD, Computer Science, 2012, Clemson University

URL: https://tigerprints.clemson.edu/all_dissertations/1053

► This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic… (more)

▼ This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic focuses on targeting questions that are free-form in nature yet defined to the point which allows for automatic evaluation and analysis. The system includes a set of modules which provide instructors with tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several modules that recognize, evaluate, provide feedback, and analyze student-drawn structures, including Euclidean graphs, chemistry molecules, computer science graphs, and simple drawings. Our system uses a visual, rule-based authoring system which enables the creation of activities for use within science, technology, engineering, and mathematics classrooms. BeSocratic records each action that students make within the system. Using a set of post-analysis tools, teachers have the ability to examine both individual and group performances. We accomplish this using hidden Markov model-based clustering techniques and visualizations. These visualizations can help teachers quickly identify common strategies and errors for large groups of students. Furthermore, analysis results can be used directly to improve activities through advanced detection of student errors and refined feedback. BeSocratic activities have been created and tested at several universities. We report specific results from several activities, and discuss how BeSocratic's analysis tools are being used with data from other systems. We specifically detail two chemistry activities and one computer science activity: (1) an activity focused on improving mechanism use, (2) an activity which assesses student understanding of Gibbs energy, and (3) an activity which teaches students the fundamentals of splay trees. In addition to analyzing data collected from students within BeSocratic, we share our visualizations and results from analyzing data gathered with another educational system, PhET. Advisors/Committee Members: Pargas, Roy P, Dean , Brian, Sitaraman , Murali.

Subjects/Keywords: Assessment; Hidden Markov Model; Intelligent Tutor; Visualization; Education

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APA (6^th Edition):

Bryfczynski, S. (2012). BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1053

Chicago Manual of Style (16^th Edition):

Bryfczynski, Samuel. “BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input.” 2012. Doctoral Dissertation, Clemson University. Accessed March 24, 2019. https://tigerprints.clemson.edu/all_dissertations/1053.

MLA Handbook (7^th Edition):

Bryfczynski, Samuel. “BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input.” 2012. Web. 24 Mar 2019.

Vancouver:

Bryfczynski S. BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input. [Internet] [Doctoral dissertation]. Clemson University; 2012. [cited 2019 Mar 24]. Available from: https://tigerprints.clemson.edu/all_dissertations/1053.

Council of Science Editors:

Bryfczynski S. BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input. [Doctoral Dissertation]. Clemson University; 2012. Available from: https://tigerprints.clemson.edu/all_dissertations/1053

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