作者:
Prof. Jian-Xin XuProf. Leonid FridmanDepartment of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566
Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University
China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI
UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University
Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu
Bloom's taxonomy of the cognitive domain and the SOLO taxonomy are being increasingly widely used in the design and assessment of courses, but there are some drawbacks to their use in computerscience. This paper ...
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ISBN:
(纸本)9781450378420
Bloom's taxonomy of the cognitive domain and the SOLO taxonomy are being increasingly widely used in the design and assessment of courses, but there are some drawbacks to their use in computerscience. This paper reviews the literature on educational taxonomies and their use in computerscience education, identifies some of the problems that arise, proposes a new taxonomy and discusses how this can be used in application-oriented courses such as programming.
作者:
M. FeemsterD.M. DawsonA. BehalW. DixonMatthew Feemster received the B.S degree in Electrical Engineering from Clemson University
Clemson South Carolina in December 1994. Upon graduation he remained at Clemson University and received the M.S. degree in Electrical Engineering in 1997. During this time he also served as a research/teaching assistant. His research work focused on the design and implementation of various nonlinear control algorithms with emphasis on the induction motor and mechanical systems with friction present. He is currently working toward his Ph.D. degree in Electrical Engineering at Clemson University. Darren M. Dawson was born in 1962
in Macon Georgia. He received an Associate Degree in Mathematics from Macon Junior College in 1982 and a B.S. Degree in Electrical Engineering from the Georgia Institute of Technology in 1984. He then worked for Westinghouse as a control engineer from 1985 to 1987. In 1987 he returned to the Georgia Institute of Technology where he received the Ph.D. Degree in Electrical Engineering in March 1990. During this time he also served as a research/teaching assistant. In July 1990 he joined the Electrical and Computer Engineering Department and the Center for Advanced Manufacturing (CAM) at Clemson University where he currently holds the position of Professor. Under the CAM director's supervision he currently leads the Robotics and Manufacturing Automation Laboratory which is jointly operated by the Electrical and Mechanical Engineering departments. His main research interests are in the fields of nonlinear based robust adaptive and learning control with application to electro-mechanical systems including robot manipulators motor drives magnetic bearings flexible cables flexible beams and high-speed transport systems. Aman Behal was born in India in 1973. He received his Masters Degree in Electrical Engineering from Indian Institute of Technology
Bombay in 1996. He is currently working towards a Ph.D in Controls and Robotics at Clemson University. His research focuses on the control of no
In this paper, we extend the observer/control strategies previously published in [25] to an n -link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects...
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In this paper, we extend the observer/control strategies previously published in [25] to an n -link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu-Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.
software risk advisory tools have been developed to support Verification and Validation (V&V) processes for NASA flight projects on the Constellation program. The Orthogonal Defect Classification COnstructive QUAL...
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ISBN:
(纸本)1563479079
software risk advisory tools have been developed to support Verification and Validation (V&V) processes for NASA flight projects on the Constellation program. The Orthogonal Defect Classification COnstructive QUALity MOdel (ODC COQUALMO) predicts software defects introduced and removed classifying them with ODC defect types, allowing various tradeoff analyses. We have been exploring methods to design and optimize V&V processes with static and dynamic versions of the quality model by integrating it with different risk minimization techniques. These techniques allow "what-if" experimentation to determine the impact of V&V techniques on specific risks and overall flight risk. V&V techniques are quantified from a value-based perspective when the defect model is integrated with machine learning, strategic optimization and JPL's Defect Detection and Prevention (DDP) risk management method. Results to-date show that the automated methods are practical for flight projects to design higher value V&V processes in shorter time and with fewer resources.
Message Passing is a popular mechanism used to enable inter-process communication in parallel and distributed computing. Many complex scientific and engineering applications that are executed on clusters have been dev...
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The advent of service-oriented Grid computing has resulted in the need for Grid resources such as clusters to enforce user-specific service needs and expectations. Service Level Agreements (SLAs) define conditions whi...
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It is well known that errors introduced early in the development process are commonly the most expensive to correct. The increasingly popular model-driven architecture (MDA) exacerbates this problem by propagating the...
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Increasingly, software should dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing and communication infrastructure, and in the surrounding physical environment. In...
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ISBN:
(纸本)1595933751
Increasingly, software should dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing and communication infrastructure, and in the surrounding physical environment. In order for an adaptive program to be trusted, it is important to have mechanisms to ensure that the program functions correctly during and after adaptations. Adaptive programs are generally more difficult to specify, verify, and validate due to their high complexity. Particularly, when involving multi-threaded adaptations, the program behavior is the result of the collaborative behavior of multiple threads and software components. This paper introduces an approach to create formal models for the behavior of adaptive programs. Our approach separates the adaptation behavior and nonadaptive behavior specifications of adaptive programs, making the models easier to specify and more amenable to automated analysis and visual inspection. We introduce a process to construct adaptation models, automatically generate adaptive programs from the models, and verify and validate the models. We illustrate our approach through the development of an adaptive GSM-oriented audio streaming protocol for a mobile computing application. Copyright 2006 ACM.
Achieving acceptable quality of service in highly dynamic computing environments requires not only adaptation and reconfiguration of individual components of the system, but also collaboration among these components. ...
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With the emergence of aspect-oriented (AO) techniques, crosscutting concerns can be now explicitly modularized and exposed as additional variabilities in program families Hence, the development of highly customizable ...
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ISBN:
(纸本)9781627486606
With the emergence of aspect-oriented (AO) techniques, crosscutting concerns can be now explicitly modularized and exposed as additional variabilities in program families Hence, the development of highly customizable software family architectures requires the explicit handling of crosscutting variabilities through domain engineering and application engineering steps In this context, this paper presents a generative model that addresses the implementation and instantiation of variabilities encountered in AO software family architectures The use of our model allows for an early specification and preparation of AO variabilities, which in turn can be explicitly customized by means of domain engineering activities All the variabilities of the architecture are modeled using feature models In application engineering, developers can request an instance of the AO architecture in a process of two stages: (i) the definition of a feature model instance which specifies the resolution of variabilities for the AO family architecture;and (ii) the definition of a set of crosscutting relationships between features.
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