Even though handhelds have been discussed as an ideal means to support "learning in the wild", a seamless integration with classroom technologies is usually missing. This paper discusses a system for student...
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Even though handhelds have been discussed as an ideal means to support "learning in the wild", a seamless integration with classroom technologies is usually missing. This paper discusses a system for students called MCPresenter that facilitates both performing and documenting learning experiences outside the classroom and bringing them into more formal settings for discussion and further development. It supports collaborative learning and applies the same gesture-based interaction principles as software for interactive whiteboards, enabling seamless transition between inside and outside as well as facilitating individual work, peer-to-peer interaction and group discussions.
This paper presents a novel field robot JL-I based on a reconfigurable concept for urban search and rescue applications. The robot consists of three identical modules; each module is an entire robotic system that can ...
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This paper presents a novel field robot JL-I based on a reconfigurable concept for urban search and rescue applications. The robot consists of three identical modules; each module is an entire robotic system that can perform distributed activities. It features three-degrees-of-freedom (DOF) active joints actuated by serial and parallel mechanisms for changing shape and flexible docking mechanism. The docking mechanism enables adjacent modules to connect or disconnect flexibly and automatically. DOF analysis, working space analysis and the kinematics of the 3D active joint between connected modules are studied thoroughly. In the end a series of successful tests confirm the principles and the robot's capabilities.
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.
We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the...
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ISBN:
(纸本)9780780397408
We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the system is resident participation. The GIS layer structure proposed in this paper nicely supports this user participation. The system architecture and the use of the system for tsunami disaster control are discussed. We report ongoing developments in Hachinohe-City, Japan.
Biometric Person Authentication such as face, fingerprint, palmprint and signature depends on the quality of image processing. When it needs to be done under a low-resolution image, the accuracy will be impaired. So h...
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In this paper, we propose a novel classification algorithm, called geometrical probability covering (GPC) algorithm, to improve classification ability. On the basis of geometrical properties of data, the proposed algo...
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While manifold learning algorithms can discover intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space, the discriminant ability of the low-dimensional subspaces obtained by the algorithms...
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Much attention has been paid upon network data flow control in recent years. The main problem in this field is how to design good algorithm or control law for flow rate of network data flow sources and for updated pri...
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The problem of designing recurrent continuous-time and spiking neural networks is NP-Hard. A common practice is to utilize stochastic searches, such as evolutionary algorithms, to automatically construct acceptable ne...
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The problem of designing recurrent continuous-time and spiking neural networks is NP-Hard. A common practice is to utilize stochastic searches, such as evolutionary algorithms, to automatically construct acceptable networks. The outcome of the stochastic search is related to its ability to navigate the search space of neural networks and discover those of high quality. In this paper we investigate the search space associated with designing the above recurrent neural networks in order to differentiate which network should be easier to automatically design via a stochastic search. Our investigation utilizes two popular dynamic systems problems; (1) the Henon map and (2) the inverted pendulum as a benchmark.
A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some pres...
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A cellular neural/nonlinear network (CNN) is any spatial arrangement of mainly locally coupled cells, where each cell is a dynamical system which has an input, an output and a state that evolves according to some prescribed dynamical laws. Since the CNN was first introduced in 1988, research in this field has developed rapidly. The goal of this tutorial is to provide participants with a snapshot of the current state of the art and research trends. It covers the broad multi-disciplinary areas of CNN research, from theoretical aspects to applications. The presenters have experience ranging from theoretical analysis of CNN dynamics, VLSI implementation and system level applications
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