In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are p...In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.展开更多
Research into the moisture transport processes in porous materials is primarily important for theoretical modelling and industrial applications in the design of energy saving buildings and living environments, etc. Ba...Research into the moisture transport processes in porous materials is primarily important for theoretical modelling and industrial applications in the design of energy saving buildings and living environments, etc. Based on experimental investigation, we propose new models which describe one-dimensional transport through one-layered uniform materials and dissimilar two-layered composites. Diffusivity as a function of moisture content is obtained through a Boltzman transformation, master curves, and combined numerical and regression techniques. Transport processes in one and two-layered composites are simulated on the basis of extended unsaturated Darcy’s Law using the finite element method (FEM). Simulation results show significantly different transport patterns of moisture profile when moisture migrates in different directions, and high agreement with experimental moisture profiles. Keywords Porous materials - moisture transport - two-layered composites - modelling and simulation Qingguo Wang graduated from Hebei Normal University, China, in 1985. He received the M.Sc. degree from Beijing Petroleum University in 1988 and the Ph.D. degree from the University of Luton, UK, in 2005. He is currently a Research Associate in the Department of Electrical Engineering and Electronics at the University of Liverpool, UK and an Associate Professor of Shijiazhuang Mechanical Engineering College, China. His research interests include measurement and control, mass and heat transportation, EMC, etc.Kemal Ahmet graduated in physics from the University of Leeds. Following the completion of his masters degree, he completed his Ph.D. at the University of London in the area of nuclear instrumentation in 1992. Until recently, he was a Principal Lecturer at the University of Luton, leading a research group in moisture instrumentation, measurement and monitoring. In 2004 he joined Medtronic, world leader in medical technology, and is currently working in the Neurologic Technologies division as a specialist in powered surgical instrumentation.Young Yue is a Principal Lecturer at the University of Luton, UK. He holds a B.Sc. in mechanical engineering from the Northeastern University, China, and a Ph.D. from Heriot-Watt University, UK. He is a chartered engineer and a member of the Institution of Mechanical Engineers, UK. Dr. Yue has been working in academia for 15 years following his 8 years of industrial experience. His main research interests are CAD/CAM, geometric modelling, virtual reality, and pattern recognition. He has over 70 publications in refereed books, journals and conferences.展开更多
基金This project is partially supported by Science Research Funding from the Education Department of Liaoning Province, China (No.J9906065).
文摘In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.
文摘Research into the moisture transport processes in porous materials is primarily important for theoretical modelling and industrial applications in the design of energy saving buildings and living environments, etc. Based on experimental investigation, we propose new models which describe one-dimensional transport through one-layered uniform materials and dissimilar two-layered composites. Diffusivity as a function of moisture content is obtained through a Boltzman transformation, master curves, and combined numerical and regression techniques. Transport processes in one and two-layered composites are simulated on the basis of extended unsaturated Darcy’s Law using the finite element method (FEM). Simulation results show significantly different transport patterns of moisture profile when moisture migrates in different directions, and high agreement with experimental moisture profiles. Keywords Porous materials - moisture transport - two-layered composites - modelling and simulation Qingguo Wang graduated from Hebei Normal University, China, in 1985. He received the M.Sc. degree from Beijing Petroleum University in 1988 and the Ph.D. degree from the University of Luton, UK, in 2005. He is currently a Research Associate in the Department of Electrical Engineering and Electronics at the University of Liverpool, UK and an Associate Professor of Shijiazhuang Mechanical Engineering College, China. His research interests include measurement and control, mass and heat transportation, EMC, etc.Kemal Ahmet graduated in physics from the University of Leeds. Following the completion of his masters degree, he completed his Ph.D. at the University of London in the area of nuclear instrumentation in 1992. Until recently, he was a Principal Lecturer at the University of Luton, leading a research group in moisture instrumentation, measurement and monitoring. In 2004 he joined Medtronic, world leader in medical technology, and is currently working in the Neurologic Technologies division as a specialist in powered surgical instrumentation.Young Yue is a Principal Lecturer at the University of Luton, UK. He holds a B.Sc. in mechanical engineering from the Northeastern University, China, and a Ph.D. from Heriot-Watt University, UK. He is a chartered engineer and a member of the Institution of Mechanical Engineers, UK. Dr. Yue has been working in academia for 15 years following his 8 years of industrial experience. His main research interests are CAD/CAM, geometric modelling, virtual reality, and pattern recognition. He has over 70 publications in refereed books, journals and conferences.
