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You searched for +publisher:"Penn State University" +contributor:("Shashi Phoha, Outside Member"). Showing records 1 – 4 of 4 total matches.

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Penn State University

1. Chattopadhyay, Pritthi. Data-Driven Modeling and Pattern Recognition of Dynamical Systems.

Degree: 2018, Penn State University

 Human-engineered complex systems need to be monitored consistently to ensure their safety and efficiency, which might be affected due to degradation over time or unanticipated… (more)

Subjects/Keywords: data-driven modeling; time series analysis

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

APA (6th Edition):

Chattopadhyay, P. (2018). Data-Driven Modeling and Pattern Recognition of Dynamical Systems. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15396pxc271

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

Chicago Manual of Style (16th Edition):

Chattopadhyay, Pritthi. “Data-Driven Modeling and Pattern Recognition of Dynamical Systems.” 2018. Thesis, Penn State University. Accessed May 06, 2021. https://submit-etda.libraries.psu.edu/catalog/15396pxc271.

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

MLA Handbook (7th Edition):

Chattopadhyay, Pritthi. “Data-Driven Modeling and Pattern Recognition of Dynamical Systems.” 2018. Web. 06 May 2021.

Vancouver:

Chattopadhyay P. Data-Driven Modeling and Pattern Recognition of Dynamical Systems. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 May 06]. Available from: https://submit-etda.libraries.psu.edu/catalog/15396pxc271.

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

Council of Science Editors:

Chattopadhyay P. Data-Driven Modeling and Pattern Recognition of Dynamical Systems. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15396pxc271

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


Penn State University

2. Li, Yue. ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS.

Degree: 2016, Penn State University

 This dissertation represents a framework for adaptive information extraction from complex systems via symbolic time series analysis (STSA). The key idea for STSA is to… (more)

Subjects/Keywords: Symbolic time series analysis; Hidden Markov modeling; Pattern recognition; Finite-state Automaton; Information Fusion; Sensor Networks; Battery SOC estimation; Battery SOH estimation; Recursive Bayes Filter; Image processing

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

APA (6th Edition):

Li, Y. (2016). ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/kd17cs845

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

Chicago Manual of Style (16th Edition):

Li, Yue. “ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS.” 2016. Thesis, Penn State University. Accessed May 06, 2021. https://submit-etda.libraries.psu.edu/catalog/kd17cs845.

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

MLA Handbook (7th Edition):

Li, Yue. “ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS.” 2016. Web. 06 May 2021.

Vancouver:

Li Y. ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 May 06]. Available from: https://submit-etda.libraries.psu.edu/catalog/kd17cs845.

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

Council of Science Editors:

Li Y. ADAPTIVE INFORMATION EXTRACTION FROM COMPLEX SYSTEMS VIA SYMBOLIC TIME SERIES ANALYSIS. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/kd17cs845

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


Penn State University

3. Yao, Wenqing. Data-driven sensor recalibration and fault diagnosis in nuclear power plants.

Degree: 2019, Penn State University

 This dissertation explores techniques for online monitoring of nuclear power plants, especially pressurized water reactor (PWR) plants, which must have the capabilities to examine and… (more)

Subjects/Keywords: sensor recalibration; fault diagnosis; nuclear power plants; online monitoring; autoregressive support vector regression; resistance temperature detector

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

APA (6th Edition):

Yao, W. (2019). Data-driven sensor recalibration and fault diagnosis in nuclear power plants. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15662wvy5022

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

Chicago Manual of Style (16th Edition):

Yao, Wenqing. “Data-driven sensor recalibration and fault diagnosis in nuclear power plants.” 2019. Thesis, Penn State University. Accessed May 06, 2021. https://submit-etda.libraries.psu.edu/catalog/15662wvy5022.

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

MLA Handbook (7th Edition):

Yao, Wenqing. “Data-driven sensor recalibration and fault diagnosis in nuclear power plants.” 2019. Web. 06 May 2021.

Vancouver:

Yao W. Data-driven sensor recalibration and fault diagnosis in nuclear power plants. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 May 06]. Available from: https://submit-etda.libraries.psu.edu/catalog/15662wvy5022.

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

Council of Science Editors:

Yao W. Data-driven sensor recalibration and fault diagnosis in nuclear power plants. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/15662wvy5022

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


Penn State University

4. Mondal, Sudeepta. Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics.

Degree: 2020, Penn State University

 Mechanical systems often involve multi-physics interactions and complex nonlinearities due to which design optimization and diagnostics become challenging. The inherent complexity of the processes, along… (more)

Subjects/Keywords: Machine learning; Combustion; Additive Manufacturing; Multi-Fidelity Modeling; Anomaly Detection

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

APA (6th Edition):

Mondal, S. (2020). Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/18018sbm5423

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

Chicago Manual of Style (16th Edition):

Mondal, Sudeepta. “Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics.” 2020. Thesis, Penn State University. Accessed May 06, 2021. https://submit-etda.libraries.psu.edu/catalog/18018sbm5423.

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

MLA Handbook (7th Edition):

Mondal, Sudeepta. “Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics.” 2020. Web. 06 May 2021.

Vancouver:

Mondal S. Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics. [Internet] [Thesis]. Penn State University; 2020. [cited 2021 May 06]. Available from: https://submit-etda.libraries.psu.edu/catalog/18018sbm5423.

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

Council of Science Editors:

Mondal S. Probabilistic Machine Learning for Advanced Engineering Design Optimization and Diagnostics. [Thesis]. Penn State University; 2020. Available from: https://submit-etda.libraries.psu.edu/catalog/18018sbm5423

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

.