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Operations Research, Systems Engineering and Industrial Engineering Commons™
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- Keyword
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- Adaptive Control (4)
- Learning (Artificial Intelligence) (4)
- Neurocontrollers (4)
- Nonlinear Control Systems (4)
- Observers (4)
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- Closed Loop Systems (3)
- Discrete Time Systems (3)
- Lyapunov Methods (3)
- Feedback (2)
- Internal Combustion Engines (2)
- Neural Networks (NNs) (2)
- A Molten Carbonate (1)
- Action Recognition (1)
- Adaptive Critic (1)
- Adaptive Critic Design (1)
- Adaptive Critic-Based Neural Network Controller (1)
- Anaerobic Digestion (1)
- Artificial Intelligence (1)
- CHHP System (1)
- Closed-Loop Signals (1)
- Closed-Loop Tracking Error (1)
- Computer Software (1)
- Computer Vision (1)
- Control Nonlinearities (1)
- Control System Synthesis (1)
- Cycle-To-Cycle Dispersion (1)
- Cyclic Dispersion (1)
- Deadzone Compensation Scheme (1)
- Deadzone Nonlinearity (1)
- Deep Learning (1)
- Publication Year
- Publication
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- Electrical and Computer Engineering Faculty Research & Creative Works (8)
- UMR-MEC Conference on Energy / UMR-DNR Conference on Energy (5)
- Engineering Management and Systems Engineering Faculty Research & Creative Works (3)
- Mechanical and Aerospace Engineering Faculty Research & Creative Works (3)
- Computer Science Faculty Research & Creative Works (1)
Articles 1 - 20 of 20
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Real-Time Assembly Operation Recognition With Fog Computing And Transfer Learning For Human-Centered Intelligent Manufacturing, Wenjin Tao, Md Al-Amin, Haodong Chen, Ming-Chuan Leu, Zhaozheng Yin, Ruwen Qin
Real-Time Assembly Operation Recognition With Fog Computing And Transfer Learning For Human-Centered Intelligent Manufacturing, Wenjin Tao, Md Al-Amin, Haodong Chen, Ming-Chuan Leu, Zhaozheng Yin, Ruwen Qin
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power close to the data source in order to achieve real-time recognition. For data collection, the operator's activity is captured using visual cameras from different perspectives. For operation recognition, instead of directly building and training a deep learning model from scratch, which needs a huge amount of data, transfer learning …
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Computer Science Faculty Research & Creative Works
Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …
Work Zone Simulator Analysis: Driver Performance And Acceptance Of Missouri Alternate Lane Shift Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Thind, H. Nadathur
Work Zone Simulator Analysis: Driver Performance And Acceptance Of Missouri Alternate Lane Shift Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Thind, H. Nadathur
Engineering Management and Systems Engineering Faculty Research & Creative Works
The objective of this project is to evaluate MoDOT’s alternate lane shift sign configuration for work zones. The single signproposed by MoDOT provides the traveler with enough information to let them know that all lanes are available to shift around thework zone, whereas the MUTCD signs require drivers to see two signs. This research simulation project evaluates the drivers’ laneshifting performance and acceptance of the alternate lane shift sign proposed by MoDOT to be used on work zones as compared tothe MUTCD lane shift signs. Based on the study results, no difference was observed between MUTCD lane shift sign andMoDOT …
Study Of A Molten Carbonate Fuel Cell Combined Heat, Hydrogen And Power System: End-Use Application, Tarek A. Hamad, Abdulhakim Amer A. Agll, Yousif M. Hamad, Sushrut Bapat, Mathew Thomas, Kevin B. Martin, John W. Sheffield
Study Of A Molten Carbonate Fuel Cell Combined Heat, Hydrogen And Power System: End-Use Application, Tarek A. Hamad, Abdulhakim Amer A. Agll, Yousif M. Hamad, Sushrut Bapat, Mathew Thomas, Kevin B. Martin, John W. Sheffield
Engineering Management and Systems Engineering Faculty Research & Creative Works
To address the problem of fossil fuel usage and high greenhouse gas emissions at the Missouri University of Science and Technology campus, using of alternative fuels and renewable energy sources can lower energy consumption and greenhouse gas emissions. Biogas, produced by anaerobic digestion of wastewater, organic waste, agricultural waste, industrial waste, and animal by-products is a potential source of renewable energy. In this work, we have discussed the design of CHHP system for the campus using local resources. An energy flow and resource availability study is performed to identify the type and source of feedstock required to continuously run the …
Residential Energy Performance Metrics, Christopher Wright, Stuart Werner Baur, Katie Grantham, Robert B. Stone, Scott Erwin Grasman
Residential Energy Performance Metrics, Christopher Wright, Stuart Werner Baur, Katie Grantham, Robert B. Stone, Scott Erwin Grasman
Engineering Management and Systems Engineering Faculty Research & Creative Works
Techniques for residential energy monitoring are an emerging field that is currently drawing significant attention. This paper is a description of the current efforts to monitor and compare the performance of three solar powered homes built at Missouri University of Science and Technology. The homes are outfitted with an array of sensors and a data logger system to measure and record electricity production, system energy use, internal home temperature and humidity, hot water production, and exterior ambient conditions the houses are experiencing. Data is being collected to measure the performance of the houses, compare to energy modeling programs, design and …
Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility …
Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary …
Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under …
Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate …
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; …
Composite Structures Using Asphalt Based Roofing Scrap Materials: Eiera -- Final Report, V. J. Flanigan, K. Chandrashekhara, Susan L. Murray
Composite Structures Using Asphalt Based Roofing Scrap Materials: Eiera -- Final Report, V. J. Flanigan, K. Chandrashekhara, Susan L. Murray
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The uses of recycled materials in composites provide the potential for large cost savings and a solution to the ever-growing disposal problem. Shingles contain petroleum based binders and fillers, which used as a valuable resource in composite production. Composites offer inherent advantages over traditional materials in regard to corrosion resistance, design flexibility and extended service life. Use of scrap-roofing shingles as a core material in glass fiber reinforced composite materials offer potential low cost composite products such as sound barrier system, railroad ties and other building materials including blocks. In the present work, processes have been developed for shredding scrap …
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a …
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track …
University Of Missouri--Rolla Wood Energy Research, Yildirim Omurtag, V. J. Flanigan, Nathan E. Welch
University Of Missouri--Rolla Wood Energy Research, Yildirim Omurtag, V. J. Flanigan, Nathan E. Welch
UMR-MEC Conference on Energy / UMR-DNR Conference on Energy
The overall objective of this project is to conduct a research and development program which will lead to the early commercialization of wood gasification technology to process wood residues typical of those found in the Ozark region.
Increased Aluminum Use And Its Impact On The Life-Cycle Energy Cost Of Automobiles, Yildirim Omurtag
Increased Aluminum Use And Its Impact On The Life-Cycle Energy Cost Of Automobiles, Yildirim Omurtag
UMR-MEC Conference on Energy / UMR-DNR Conference on Energy
This paper examines the life-cycle of a passenger automobile including ore refining, manufacture of components, assembly, driving and recycling to provide a general computerized model to be used in evaluating the impact of various material substitution rates and recycling policies on the total system energy consumption. The emphasis is on the use of increased aluminum to replace iron and steel.
A Systems Study Of Our Energy Problems, S. C. Lee
A Systems Study Of Our Energy Problems, S. C. Lee
UMR-MEC Conference on Energy / UMR-DNR Conference on Energy
The diminishing supply of petroleum and natural gas has induced many concerned citizens as well as many interest groups to study energy problems. Based on different sources of information and various degrees of economical consideration, conflicting conclusions have been reached. To help resolve this situation, a systems study was conducted. The available statistics on the supply and demand of energy were examined. The resources of nuclear energy, fossil fuel, and renewable energy sources were analyzed. Considerations were weighted equally on energy content, resource availability, existing technology, and consumer economics as well as on the impact of ecology and sociology. The …
3rd Annual Umr-Mec Conference On Energy -- Entire Proceedings, University Of Missouri--Rolla
3rd Annual Umr-Mec Conference On Energy -- Entire Proceedings, University Of Missouri--Rolla
UMR-MEC Conference on Energy / UMR-DNR Conference on Energy
No abstract provided.
2nd Annual Umr-Mec Conference On Energy -- Entire Proceedings, University Of Missouri--Rolla
2nd Annual Umr-Mec Conference On Energy -- Entire Proceedings, University Of Missouri--Rolla
UMR-MEC Conference on Energy / UMR-DNR Conference on Energy
No abstract provided.