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Copper Ion Cementation in Presence of a Magnetic Field
Fadali, Olfat A.,Obaid, Mohamed,Mahmoud, Mohamed S.,Farrag, Taha E.,TaeWoo, Kim,Khalil, Khalil Abdelrazek,Barakat, Nasser A. M. VCH VERLAGSGESELLSCHAFT 2015 CHEMICAL ENGINEERING AND TECHNOLOGY Vol.38 No.3
<P><B>Abstract</B></P><P>The effect of an electromagnetic field (EMF) on the rate of copper(II) cementation from copper sulfate solutions on a rotating iron cylinder was investigated. The studied variables were cylinder rotation speed, magnetic field strength, and magnetic field direction. The application of EMF increased the rate of cementation in both parallel and perpendicular direction of the magnetic field where the latter proved to be more effective. The rate of mass transfer under an EMF was found to be more than doubled. The enhancement of copper recovery in presence of the EMF is due to the induced motion of Fe<SUP>+</SUP><I><SUP>n</SUP></I> in the solution which is limited to a certain range of cylinder rotation speed. The power consumption for cementation of copper could be significantly reduced by utilizing EMF.</P>
Neural Robust Control for Perturbed Crane Systems
Cho Hyun-Cheol,Fadali M.Sami,Lee Young-Jin,Lee Kwon-Soon The Korean Society of Mechanical Engineers 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.5
In this paper, we present a new control methodology for perturbed crane systems. Nonlinear crane systems are transformed to linear models by feedback linearization. An inverse dynamic equation is applied to compute the system PD control force. The PD control parameters are selected based on a nominal model and are therefore suboptimal for a perturbed system. To achieve the desired performance despite model perturbations, we construct a neural network auxiliary controller to compensate for modeling errors and disturbances. The overall control input is the sum of the nominal PD control and the neural auxiliary control. The neural network is iteratively trained with a perturbed system until acceptable performance is attained. We apply the proposed control scheme to 2- and 3-degree-of-freedom (D.O.F.) crane systems, with known bounds on the payload mass. The effectiveness of the control approach is numerically demonstrated through computer simulation experiments.
Design of Time-varying Stochastic Process with Dynamic Bayesian Networks
Cho, Hyun-Cheol,Fadali, M.Sami,Lee, Kwon-Soon The Korean Institute of Electrical Engineers 2007 Journal of Electrical Engineering & Technology Vol.2 No.4
We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.
Online Parameter Estimation and Convergence Property of Dynamic Bayesian Networks
Cho, Hyun-Cheol,Fadali, M. Sami,Lee, Kwon-Soon Korean Institute of Intelligent Systems 2007 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.7 No.4
In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.
Internet Traffic Control Using Dynamic Neural Networks
Cho, Hyun-Cheol,Fadali, M. Sami,Lee, Kwon-Soon The Korean Institute of Electrical Engineers 2008 Journal of Electrical Engineering & Technology Vol.3 No.2
Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.
Hyun Cheol Cho,Knowles, J.,Fadali, M.S.,Kwon Soon Lee IEEE 2010 IEEE transactions on control systems technology Vol.18 No.2
<P>Dynamic neural models provide an attractive means of fault detection and isolation in industrial process. One approach is to create a neural model to emulate normal system behavior and additional models to emulate various fault conditions. The neural models are then placed in parallel with the system to be monitored, and fault detection is achieved by comparing the outputs of the neural models with the real system outputs. Neural network training is achieved using simultaneous perturbation stochastic approximation (SPSA). Fault classification is based on a simple threshold test of the residuals formed by subtracting each neural model output from the corresponding output of the real system. We present a new approach based on this well known scheme where a Bayesian network is used to evaluate the residuals. The approach is applied to fault detection in a three-phase induction motor.</P>
Lyapunov-based Fuzzy Queue Scheduling for Internet Routers
Hyun Cheol Cho,M. Sami Fadali,Jin Woo Lee,Young Jin Lee,Kwon Soon Lee 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.3
Quality of Service (QoS) in the Internet depends on queuing and sophisticated scheduling in routers. In this paper, we address the issue of managing traffic flows with different priorities. In our reference model, incoming packets are first classified based on their priority, placed into different queues with different capacities, and then multiplexed onto one router link. The fuzzy nature of the information on Internet traffic makes this problem particularly suited to fuzzy methodologies. We propose a new solution that employs a fuzzy inference system to dynamically and efficiently schedule these priority queues. The fuzzy rules are derived to minimize the selected Lyapunov function. Simulation experiments show that the proposed fuzzy scheduling algorithm outperforms the popular Weighted Round Robin (WRR) queue scheduling mechanism.
Internet Traffic Control Using Dynamic Neural Networks
Hyun Cheol Cho,M. Sami Fadali,Kwon Soon Lee 대한전기학회 2008 Journal of Electrical Engineering & Technology Vol.3 No.2
Active Queue Management (AQM) has been widely used for congestion avoidance in Transmission Control Protocol (TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation (BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection (RED) and Proportional-Integral (PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.