Search Results - "Beling, Peter A."

Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models by Adams, Stephen, Beling, Peter A., Cogill, Randy

“…In this paper, a joint feature selection and parameter estimation algorithm is presented for hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs)…”
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Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning by Bowes, Benjamin D., Tavakoli, Arash, Wang, Cheng, Heydarian, Arsalan, Behl, Madhur, Beling, Peter A., Goodall, Jonathan L.

“…Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely…”
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Modeling law search as prediction by Dadgostari, Faraz, Guim, Mauricio, Beling, Peter A., Livermore, Michael A., Rockmore, Daniel N.

“…Law search is fundamental to legal reasoning and its articulation is an important challenge and open problem in the ongoing efforts to investigate legal…”
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Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security by Pradhan, Sayantan, Rajagopala, Abhi D., Meno, Emma, Adams, Stephen, Elks, Carl R., Beling, Peter A., Yadavalli, Vamsi K.

“…The increasingly pervasive problem of counterfeiting affects both individuals and industry. In particular, public health and medical fields face threats to…”
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On the Practical Art of State Definitions for Markov Decision Process Construction by Scherer, William T., Adams, Stephen, Beling, Peter A.

“…Many problems faced by decision makers today involve the management of large scale, complex systems that can be modeled as state-based control problems,…”
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A survey of inverse reinforcement learning by Adams, Stephen, Cody, Tyler, Beling, Peter A.

“…Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse…”
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Deep multi-agent reinforcement learning for multi-level preventive maintenance in manufacturing systems by Su, Jianyu, Huang, Jing, Adams, Stephen, Chang, Qing, Beling, Peter A.

“…Designing preventive maintenance (PM) policies that ensure smooth and efficient production for large-scale manufacturing systems is non-trivial. Recent…”
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A survey of feature selection methods for Gaussian mixture models and hidden Markov models by Adams, Stephen, Beling, Peter A.

“…Feature selection is the process of reducing the number of collected features to a relevant subset of features and is often used to combat the curse of…”
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A Systems Theory of Transfer Learning by Cody, Tyler, Beling, Peter A.

“…Existing frameworks for transfer learning are incomplete from a systems theoretic perspective. They place emphasis on notions of domain and task, and neglect…”
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Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games by Lin, Xiaomin, Adams, Stephen C., Beling, Peter A.

“…This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of…”
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Multiagent Inverse Reinforcement Learning for Two-Person Zero-Sum Games by Lin, Xiaomin, Beling, Peter A., Cogill, Randy

“…The focus of this paper is a Bayesian framework for solving a class of problems termed multiagent inverse reinforcement learning (MIRL). Compared to the…”
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Empirically Measuring Transfer Distance for System Design and Operation by Cody, Tyler, Adams, Stephen, Beling, Peter A.

“…Classical machine learning approachesare sensitive to nonstationarity. Transfer learning can address nonstationarity by sharing knowledge from one system to…”
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Hierarchical fault classification for resource constrained systems by Adams, Stephen, Meekins, Ryan, Beling, Peter A., Farinholt, Kevin, Brown, Nathan, Polter, Sherwood, Dong, Qing

“…•Using hierarchical classification to minimize resource consumption of fault classification algorithms.•Using reinforcement learning to select the classifier…”
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Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems by Choo, Benjamin Y, Adams, Stephen C, Weiss, Brian A, Marvel, Jeremy A, Beling, Peter A

“…The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM…”
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Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor by Choo, Benjamin, Landau, Michael, DeVore, Michael, Beling, Peter A

“…The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat…”
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Task recognition from joint tracking data in an operational manufacturing cell by Rude, Don J., Adams, Stephen, Beling, Peter A.

“…This paper investigates the feasibility of using inexpensive, general-purpose automated methods for recognition of worker activity in manufacturing processes…”
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Simulating Kinect Infrared and Depth Images by Landau, Michael J., Choo, Benjamin Y., Beling, Peter A.

“…With the emergence of the Microsoft Kinect sensor, many developer communities and research groups have found countless uses and have already published a wide…”
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Transfer Distance and Operating Envelopes to Detect Non-Stationarity in Cyber Environments by Adams, Stephen, Cody, Tyler, Roughani, Ramin Salman, Beling, Peter A.

“…While machine learning models have demonstrated the ability to detect cyber attacks, their deployment to operational scenarios is often limited due to the…”
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An ontological metamodel for cyber-physical system safety, security, and resilience coengineering by Bakirtzis, Georgios, Sherburne, Tim, Adams, Stephen, Horowitz, Barry M., Beling, Peter A., Fleming, Cody H.

“…Cyber-physical systems are complex systems that require the integration of diverse software, firmware, and hardware to be practical and useful. This increased…”
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Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation by Saliba, Sami M., Bowes, Benjamin D., Adams, Stephen, Beling, Peter A., Goodall, Jonathan L.

“…Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure…”
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