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Articles 1 - 5 of 5
Full-Text Articles in Mechanical Engineering
High-Temperature Characteristics Of 1.3 Μm Ingaasn:Sb/Gaas Multiple-Quantum-Well Lasers Grown By Molecular-Beam Epitaxy, Xiaodong Yang, J. B. Heroux, M. J. Jurkovic, W. I. Wang
High-Temperature Characteristics Of 1.3 Μm Ingaasn:Sb/Gaas Multiple-Quantum-Well Lasers Grown By Molecular-Beam Epitaxy, Xiaodong Yang, J. B. Heroux, M. J. Jurkovic, W. I. Wang
Mechanical and Aerospace Engineering Faculty Research & Creative Works
1.3 μm InGaAsN:Sb/GaAs multiple-quantum-well laser diodes have been grown by solid-source molecular-beam epitaxy using Sb as a surfactant. A low threshold of 1.1 kA/cm² was achieved for broad-area laser diodes under pulsed operation at room temperature. High-temperature device characterization revealed characteristic temperatures (T₀) of 92 and 54 K for operating temperatures below and above 75°C, respectively, as well as a lasing-wavelength temperature dependence of 0.36 nm/°C.
Lifecycle Analysis For Environmentally Conscious Solid Freeform Manufacturing, Yanchun Luo, Ji Zhiming, Ming-Chuan Leu, R. J. Caudill
Lifecycle Analysis For Environmentally Conscious Solid Freeform Manufacturing, Yanchun Luo, Ji Zhiming, Ming-Chuan Leu, R. J. Caudill
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A lifecycle based process model for analyzing the environmental performance of SFM processes and SFM based rapid tooling processes is presented in this paper. The process environmental performance assessment model considers material, energy and disposal scenarios. The material use, process parameters (e.g. scanning speed) and power use can affect the environmental consequence of a process when material resource, energy, human health and environmental damage are taken into account. The presented method is applied to the SLA process and two SLA based rapid tooling processes. The method can be used to compare different rapid prototyping (RP) and RT processes in terms …
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Adaptive critic based neural networks have been found to be powerful tools in solving various optimal control problems. The adaptive critic approach consists of two neural networks which output the control values and the Lagrangian multipliers associated with optimal control. These networks are trained successively and when the outputs of the two networks are mutually consistent and satisfy the differential constraints, the controller network output produces optimal control. In this paper, we analyze the mechanics of convergence of the network solutions. We establish the necessary conditions for the network solutions to converge and show that the converged solution is optimal.
Infinite Time Optimal Neuro Control For Distributed Parameter Systems, S. N. Balakrishnan, Radhakant Padhi
Infinite Time Optimal Neuro Control For Distributed Parameter Systems, S. N. Balakrishnan, Radhakant Padhi
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The conventional dynamic programming methodology for the solution of optimal control, despite having many desirable features, is severely restricted by its computational requirements. However, in recent times, an alternate formulation, known as the adaptive-critic synthesis, has given it a new perspective. In this paper, we have attempted to use the philosophy of adaptive-critic design to the optimal control of distributed parameter systems. An important contribution of this study is the derivation of the necessary conditions of optimality for distributed parameter systems, described in discrete domain, following the principle of approximate dynamic programming. Then the derived necessary conditions of optimality are …
Robust Adaptive Critic Based Neurocontrollers For Systems With Input Uncertainties, S. N. Balakrishnan, Zhongwu Huang
Robust Adaptive Critic Based Neurocontrollers For Systems With Input Uncertainties, S. N. Balakrishnan, Zhongwu Huang
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A two-neural network approach to solving optimal control problems is described in this study. This approach called the adaptive critic method consists of two neural networks: one is called the supervisor or critic, and the other is called an action network or controller. The inputs to both these networks are the current states of the system to be controlled. Each network is trained through an output of the other network and the conditions for optimal control. When their outputs are mutually consistent, the controller network output is optimal. The optimality is limited to the underlying model. Hence, we develop a …