In this paper, we presented a method to improve structural modeling based on conserved domain clusters and structure-anchored alignments. We first constructed a template library of structural clusters for all conserve...
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ISBN:
(纸本)1595934804;9781595934802
In this paper, we presented a method to improve structural modeling based on conserved domain clusters and structure-anchored alignments. We first constructed a template library of structural clusters for all conserved sequence domains. Then, for each cluster, we built the profile using the structure and sequence information. Finally we use the profile and structural alignments as anchors to increase the alignment accuracy between a query and its templates. Our preliminary results show that this method can be used for the partial prediction for a majority of known protein sequences with better qualities. Copyright 2007 ACM.
Agent-based models are widely used for the simulation of systems from several domains (biology, economics, meteorology, etc). In biology agent-based models are very useful for predicting the social behaviour of system...
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This paper presents a novel approach to solve the constrained unit commitment problem using the Selective Self-Adaptive Ant Colony Optimization (SSACO) for improving search performance by automatically adapting ant po...
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ISBN:
(纸本)9789810594237
This paper presents a novel approach to solve the constrained unit commitment problem using the Selective Self-Adaptive Ant Colony Optimization (SSACO) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with the Candidate Path Management Module (CPMM) and the Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. A new concept of the Relativity Pheromone Updating (RPU) is also introduced to provide a reasonable evaluation of the pheromone trail intensity among the agents. The proposed SSACO method not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSACO topology can remarkably speed up the computational time of ant colony optimization, which is favorable for a large-scale UC problem implementation.
作者:
Jimshone LiJason Sheng-Hong TsaiLeang-San ShiehControl System Laboratory
Department of Electrical Engineering National Cheng Kung University Tainan 701 Taiwan R.O.C. Jim-Shone Li was born in Taiwan
R. O.C. on April 20 1967. He received B.S. and M.S. degrees in Electrical Engineering from the Chung-Cheng Institute of Technology Taoyuan Taiwan R.O.C. in 1989 and 1993 respectively. He is currently a Ph.D. student at National Cheng-Kung University Tainan Taiwan R.O.C. His research interests include analysis and design of multidimensional systems and nonlinear system control. Department of Electrical and Computer Engineering
University of Houston Houston TX 77004-4793 U.S.A.
An optimal control method for two-dimensional (2-D) linear systems with variable coefficients and free boundary conditions in Roesser's model is proposed in this paper. Based on Roesser's model, an equivalent ...
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An optimal control method for two-dimensional (2-D) linear systems with variable coefficients and free boundary conditions in Roesser's model is proposed in this paper. Based on Roesser's model, an equivalent general 1-D model of the 2-D system is presented, and the problem of minimizing a 2-D linear quadratic (LQ) cost function is solved for the case where complete state information is available. The solution is obtained by using the proposed dynamic programming in 1-D descriptor form to solve the Riccati equation and then arriving at the optimal control law and minimum cost. The proposed control methodology can be applied to discrete-time models of systems described by partial differential equations and can also be used in the field of signal processing.
Agent-based models are widely used for the simulation of systems from several domains (biology, economics, meteorology, etc). In biology agent-based models are very useful for predicting the social behaviour of system...
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Agent-based models are widely used for the simulation of systems from several domains (biology, economics, meteorology, etc). In biology agent-based models are very useful for predicting the social behaviour of systems; in particular they seem well adapted to model the behaviour of a cell population. In this paper an agent-based model, developed to study normal human keratinocytes (tissue cells), will be investigated. This kind of model exhibits probabilistic behaviour and the validation of simulation results is often done with a qualitative analysis by the experts (biologists). The main objective of this paper is to propose new variables and metrics that allow the comparison and a possible quantitative validation using numerical results from simulations.
作者:
M.H. MoradiM.R. KatebiM.A. JohnsonDepartment of Electronic and Electrical Engineering
University of Strath-clyde Glasgow GI 1QE UK. Dr. Moradi is a Lecturer in the Department of Electronic and Electrical Engineering
University of Bu-Ali Sina in Iran. He obtained the BSc and MSc in 1991 and 1993 from the Sharif University of Technology and Tarbiat Modarres University respectively. Dr. Moradi joined the Bu-Ali Sina University in 1993. In 2002 he obtained his PhD degree from the University of Strath-clyde Glasgow Scotland. His theoretical research interests include predictive PID control advanced classical control generalised predictive control system identification and fault monitoring robust control design computer networks and more recently control through networks. His industrial interests are in the areas of large-scale systems especially power systems and power plant modelling and control supervisory control and process control. Dr. Moradi has published a number of Journal and Conference papers. Dr. Katebi is a senior lecturer in the Department of Electronic and Electrical Engineering
University of Strathclyde. His theoretical research interests are currently focused on Non-linear Control and Filtering for Complex Systems Autonomous Control Design System integration and Fault monitoring and Reconfiguration Plant Monitoring through the Internet Discrete Event Simulation Process Optimisation and Robust Control Design. The industrial research interests are in the area of Hot and Rolling for Steel and Aluminium Power Plant Modelling and Control Marine Control Systems Process Control and Wastewater Treatment Control. Dr Katebi is the author/co-author of four books and more than 150 papers and industrial reports. Professor Johnson's academic education began at Coventry University (1969) and continued under the supervision of Professor Greyham Bryant in the field of Control Systems at Imperial College
London. He obtained the DIC MSc and PhD from Imperial College leaving for industry in 1978. Subsequent experience was in the
This paper is concerned with the design of Multi-Inputs and Multi-Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model-based predictive controllers. A new PID control...
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This paper is concerned with the design of Multi-Inputs and Multi-Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model-based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.
This paper presents an approach to trajectory generation for Unmanned Aerial Vehicles (UAV) by using Mixed Integer Linear Programming (MILP) and a modification of the A{sup}* algorithm to optimize paths in dynamic env...
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This paper presents an approach to trajectory generation for Unmanned Aerial Vehicles (UAV) by using Mixed Integer Linear Programming (MILP) and a modification of the A{sup}* algorithm to optimize paths in dynamic environments, particularly having pop-ups with a known future probability of appearance. Each pop-up leads to one or several possible evasion maneuvers, characterized with a set of values used as decision making parameters in an Integer Linear Programming (ILP) model that optimizes the final route by choosing the most suitable alternative trajectories, according to the imposed constrains such as maximum fuel consumption and spent time. The model of the system in MILP and A{sup}* algorithms is presented, as well as the ILP formulation for decision making. Results and discussions are given to promote future real time implementations.
This paper analyzes effects of magnetic saturation, including cross-saturation and secondary saliencies, on saliency- based sensorless control of interior PM synchronous machines. These effects are mitigated by adapti...
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This paper analyzes effects of magnetic saturation, including cross-saturation and secondary saliencies, on saliency- based sensorless control of interior PM synchronous machines. These effects are mitigated by adaptively decoupling saturation induced-saliencies via a structured neural network. The paper includes identification of the dominant, saturation-induced components of the carrier signal current interfering with the rotor position-dependent component being tracked, characterization of these components, and implementation of a non-linear, adaptive, saturation-induced components structured neural network model to perform their decoupling.
A neural-adaptive control solution is exposed in this paper. The control strategy is based on the linear adaptive neuron, which is called ADALINE. Unlike other neural control solutions, based on perceptrons neurons ch...
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A neural-adaptive control solution is exposed in this paper. The control strategy is based on the linear adaptive neuron, which is called ADALINE. Unlike other neural control solutions, based on perceptrons neurons characterized by a long time learning process and a difficult on-line tuning of weights, this approach uses a fast algorithm, which adapts on-line the neuron's weights. Therefore the non-linear character of control law is induced by the permanent changes of neuron weights, which are variable parameters of controller. A set of study cases is done, with application to the excitation control of a synchronous generator.
This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consum...
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This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment.
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