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Chemical and Biochemical Engineering Faculty Research & Creative Works
- Keyword
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- Neural Nets (3)
- Robots (3)
- Motion Decision-Making Process (2)
- Neural Networks (2)
- Robotic Arm (2)
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- Trajectory Planning (2)
- Adaptability (1)
- Atomic layer deposition (1)
- Backpropagation (1)
- Coating volume expansion (1)
- Computational Efficiency (1)
- Computational Speed (1)
- Control Action Sequence (1)
- Control Emulation Process (1)
- Coordinates Transformation (1)
- Distributed Control (1)
- Forward Modeling (1)
- Functionality (1)
- Hierarchical Neural Network (1)
- Hierarchical Neurocontroller Architecture (1)
- Hierarchical Systems (1)
- Hybrid Hierarchical/Distributed Organization (1)
- Intelligent Control (1)
- Knowledge Sources (1)
- Learning (Artificial Intelligence) (1)
- Learning Systems (1)
- Li-ion diffusivity (1)
- Li-ion intercalation (1)
- Parallel Architectures (1)
- Path Planning (1)
Articles 1 - 4 of 4
Full-Text Articles in Mechanical Engineering
Impact Of Ultrathin Coating Layer On Lithium-Ion Intercalation Into Particles For Lithium-Ion Batteries, Yufang He, Hiep Pham, Xinhua Liang, Jonghyun Park
Impact Of Ultrathin Coating Layer On Lithium-Ion Intercalation Into Particles For Lithium-Ion Batteries, Yufang He, Hiep Pham, Xinhua Liang, Jonghyun Park
Chemical and Biochemical Engineering Faculty Research & Creative Works
Ultrathin film coatings on battery materials via atomic layer deposition (ALD) have been demonstrated as an efficient technology for battery performance enhancement. However, the fundamental understanding on lithium intercalation into active materials through the interface between the coating and active materials is unclear, which makes it difficult to optimize ALD coating strategies. Further, like most active materials, a coating layer can undergo volume change during the intercalation process, which can produce detrimental structural changes and mechanical failure of the layer. In this work, first-principles calculations are conducted to reveal the behavior of a coating layer on an active material particle …
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level network system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower-level network provides the correct sequence of control actions. A straightforward example illustrates the architecture''s capabilities, including speed, adaptability, and computational efficiency
Planning And Control Of A Robotic Manipulator Using Neural Networks, Xavier J. R. Avula, Heng Ma, Anil Malkani, Jay-Shinn Tsai, Luis C. Rabelo
Planning And Control Of A Robotic Manipulator Using Neural Networks, Xavier J. R. Avula, Heng Ma, Anil Malkani, Jay-Shinn Tsai, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
An architecture which utilizes two artificial neural systems for planning and control of a robotic arm is presented. The first neural network system participates in the trajectory planning and the motion decision-making process. The second neural network system provides the correct sequence of control actions with a high accuracy due to the utilization of an unsupervised/supervised neural network scheme. The utilization of a hybrid hierarchical/distributed organization, supervised/unsupervised learning models, and forward modeling yielded an architecture with capabilities of high level functionality.
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
Two artificial neural network systems are considered in a hierarchical fashion to plan the trajectory and control of a robotic arm. At the higher level of the hierarchy the neural system consists of four networks: a restricted Coulomb energy network to delineate the robot arm workspace; two standard backpropagation (BP) networks for coordinates transformation; and a fourth network which also uses BP and participates in the trajectory planning by cooperating with other knowledge sources. The control emulation process which is developed using a second neural system at a lower hierarchical level provides the correct sequence of control actions. An example …