Design and analysis of the Dual-Torus Network

In this paper, we propose a new topology called theDual Torus Network (DTN) which is constructed by adding interleaved edges to a torus. The DTN has many advantages over meshes and tori such as better extendibility, smaller diameter, higher bisection width, and robust link connectivity. The most important property of the DTN is that it can be partitioned into sub-tori of different sizes. This is not possible for mesh and torus-based systems. The DTN is investigated with respect to allocation, embedding, and fault-tolerant embedding. It is shown that the sub-torus allocation problem in the DTN reduces to the sub-mesh allocation problem in the torus. With respect to embedding, it is shown that a topology that can be embedded into a mesh with dilation δ can also be embedded into the DTN with less dilation. In fault-tolerant embedding, a fault-tolerant embedding method based on rotation, column insertion, and column skip is proposed. This method can embed any rectangular grid into its optimal square DTN when the number of faulty nodes is fewer than the number of unused nodes. In conclusion, the DTN is a scalable topology well-suited for massively parallel computation. Sang-Ho Chae, M.S.: He received the B.S. in the Computer Science and Engineering from the Pohang University of Science and Technology (POSTECH) in 1994, and the M.E. in 1996. Since 1996, he works as an Associate Research Engineer in the Central R&D Center of the SK Telecom Co. Ltd. He took part in developing SK Telecom Short Message Server whose subscribers are now over 3.5 million and Advanced Paging System in which he designed and implemented high availability concepts. His research interests are the Fault Tolerance, Parallel Processing, and Parallel Topolgies. Jong Kim, Ph.D.: He received the B.S. degree in Electronic Engineering from Hanyang University, Seoul, Korea, in 1981, the M.S. degree in Computer Science from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1983, and the Ph.D. degree in Computer Engineering from Pennsylvania State University, U.S.A., in 1991. He is currently an Associate Professor in the Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea. Prior to this appointment, he was a research fellow in the Real-Time Computing Laboratory of the Department of Electrical Engineering and Computer Science at the University of Michigan from 1991 to 1992. From 1983 to 1986, he was a System Engineer in the Korea Securities Computer Corporation, Seoul, Korea. His major areas of interest are Fault-Tolerant Computing, Performance Evaluation, and Parallel and Distributed Computing. Sung Je Hong, Ph.D.: He received the B.S. degree in Electronics Engineering from Seoul National University, Korea, in 1973, the M.S. degree in Computer Science from Iowa State University, Ames, U.S.A., in 1979, and the Ph.D. degree in Computer Science from the University of Illinois, Urbana, U.S.A., in 1983. He is currently a Professor in the Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea. From 1983 to 1989, he was a staff member of Corporate Research and Development, General Electric Company, Schenectady, NY, U.S.A. From 1975 to 1976, he was with Oriental Computer Engineering, Korea, as a Logic Design Engineer. His current research interest includes VLSI Design, CAD Algorithms, Testing, and Parallel Processing. Sunggu Lee, Ph.D.: He received the B.S.E.E. degree with highest distinction from the University of Kansas, Lawrence, in 1985 and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1987 and 1990, respectively. He is currently an Associate Professor in the Department of Electronic and Electrical Engineering at the Pohang University of Science and Technology (POSTECH), Pohang, Korea. Prior to this appointment, he was an Associate Professor in the Department of Electrical Engineering at the University of Delaware in Newark, Delaware, U.S.A. From June 1997 to July 1998, he spent one year as a Visiting Scientist at the IBM T. J. Watson Research Center. His research interests are in Parallel, Distributed, and Fault-Tolerant Computing. Currently, his main research focus is on the high-level and low-level aspects of Inter-Processor Communications for Parallel Computers.     

Nonlinear adaptive decentralized stabilization control for multimachine power systems

This paper presents a decentralized adaptive backstepping controller to dampen oscillations and improve the transient stability to parametric uncertainties in multimachine power systems. The proposed design on the i ^th synchronous generator uses only local information and operates without the need for remote signals from the other generators. The design of the nonlinear controller is based on a modified fourth-order nonlinear model of a synchronous generator, and the automatic voltage regulator model is considered so as to decrease the steady state voltage error. The construction of both the control law and the associated Lyapunov function is systematically designed within the design methodology. A 3-machine power system is used to demonstrate the effectiveness of the proposed controller over two other controllers, namely a conventional damping controller (power system stabilizer) and one designed using the feedback linearization techniques. Recommended by Editorial Board member Gang Tao under the direction of Editor Jae Weon Choi. This work was supported by the Korea Electrical Engineering and Science Research Institute, which is funded by Ministry of Commerce, Industry and Energy. Shan-Ying Li received the B.S. degrees in Computer Science and M.S. degree in Electrical Engineering from Northeast DianLi University, China, in 1997 and 2002, respectively. She obtained the Ph.D. degree in Electrical Engineering from Seoul National University, Korea, in 2008. She is a Post Doctor in North China Electric Power Research Institute, North China Grid Co., Ltd., China. Her research interests are in the areas of advanced control and stability applications on power systems. Sang-Seung Lee received the M.S.E.E. and Ph.D. degrees in Electrical Engineering at Seoul National University. Currently, he is with Power System Research Division of KESRI, Seoul National University, Korea. His interest areas are nonlinear/adaptive control theory, North-East Asia power system interconnection, distributed/small generation, distributed transmission/distribution load flow algorithm, regional/local energy system, PSS (power system stabilizer), and RCM (Reliability Centered Maintenance). Yong Tae Yoon was born in Korea on April 20, 1971. He received the B.S. degree, M.Eng. and Ph.D. degrees from M.I.T., USA in 1995, 1997, and 2001, respectively. Currently, he is an Assistant Professor in the School of Electrical Engineering and Computer Science at Seoul National University, Korea. His special field of interest includes electric power network economics, power system reliability, and the incentive regulation of independent transmission companies. Jong-Keun Park received the B.S. degree in Electrical Engineering from Seoul National University, Seoul, Korea in 1973 and the M.S. and Ph.D. degrees in Electrical Engineering from The University of Tokyo, Japan in 1979 and 1982, respectively. He is currently a Professor of School of Electrical Engineering, Seoul National University. In 1992, he attended as a Visiting Professor at Technology and Policy Program and Laboratory for Electromagnetic and Electronic Systems, Massachusetts Institute of Technology. He is a Senior Member of the IEEE, a Fellow of the IEE, and a Member of Japan Institute of Electrical Engineers (JIEE).     

Evolutionary fusion of a multi-classifier system for efficient face recognition

In this paper an evolutionary classifier fusion method inspired by biological evolution is presented to optimize the performance of a face recognition system. Initially, different illumination environments are modeled as multiple contexts using unsupervised learning and then the optimized classifier ensemble is searched for each context using a Genetic Algorithm (GA). For each context, multiple optimized classifiers are searched; each of which are referred to as a context based classifier. An evolutionary framework comprised of a combination of these classifiers is then applied to optimize face recognition as a whole. Evolutionary classifier fusion is compared with the simple adaptive system. Experiments are carried out using the Inha database and FERET database. Experimental results show that the proposed evolutionary classifier fusion method gives superior performance over other methods without using evolutionary fusion. Recommended by Guest Editor Daniel Howard. This work was supported by INHA UNIVERSITY Research Grant. Zhan Yu received the B.E. degree in Software Engineering from Xiamen University, China, in 2008. He is currently a master student in Intelligent Technology Lab, Computer and Information Department, Inha University, Korea. He has research interests in image processing, pattern recognition, computer vision, machine learning and statistical inference and computating. Mi Young Nam received the B.Sc. and M.Sc. degrees in Computer Science from the University of Silla Busan, Korea in 1995 and 2001 respectively and the Ph.D. degree in Computer Science & Engineering from the University of Inha, Korea in 2006. Currently, She is Post-Doctor course in Intelligent Technology Laboratory, Inha University, Korea. She’s research interest includes biometrics, pattern recognition, computer vision, image processing. Suman Sedai received the M.S. degree in Software Engineering from Inha University, China, in 2008. He is currently a Doctoral course in Western Australia University, Australia. He has research interests in image processing, pattern recognition, computer vision, machine learning. Phill Kyu Rhee received the B.S. degree in Electrical Engineering from the Seoul University, Seoul, Korea, the M.S. degree in Computer Science from the East Texas State University, Commerce, TX, and the Ph.D. degree in Computer Science from the University of Louisiana, Lafayette, LA, in 1982, 1986, and 1990 respectively. During 1982–1985 he was working in the System Engineering Research Institute, Seoul, Korea as a research scientist. In 1991 he joined the Electronic and Telecommunication Research Institute, Seoul, Korea, as a Senior Research Staff. Since 1992, he has been an Associate Professor in the Department of Computer Science and Engineering of the Inha University, Incheon, Korea and since 2001, he is a Professor in the same department and university. His current research interests are pattern recognition, machine intelligence, and parallel computer architecture. dr. rhee is a Member of the IEEE Computer Society and KISS (Korea Information Science Society).     

MFR (Multipurpose Field Robot) for installing construction materials

The objective of the study was to propose a MFR (Multipurpose Field Robot) in hazardous operation environments. This system combines a basic system composed of a multi-DOF (Degree Of Freedom) manipulator and a mobile platform with an additional module for construction, national defense and emergency-rescue. According to an additional module type combined with a basic system, it can be used in a various fields. In this study, we describe a prototype of construction robot which helps a human operator handle easily construction materials in case of using the cooperation system on construction site. This study introduces an additional module for construction and a robot control algorithm for a HRC (Human-Robot Cooperation). In addition, it proposes a novel construction method to install construction materials with robot on construction site. Seung Yeol Lee received the B.S. degree from the Department of Mechanical Engineering, Myungji University, Seoul, Korea in 2002, and the M.S. degree from the Department of Mechatronics Engineering, Hanyang University, Seoul, Korea in 2005. He is a Ph.D. degree candidate from the Department of Mechanical Engineering, Hanyang University, Seoul, Korea. From 2003, He is currently a visiting researcher in the Research Institute of Technology, Construction Group at the Samsung Corporation, Korea conducting the design and implementation of construction robot and automation system for construction project. His research interests include design, control, and application of construction robots, field robotic systems and ergonomic design of robotic systems. He is a member of the Korea Society of Mechanical Engineers, Architectural Institute of Korea, and Ergonomics Society of Korea. Yong Seok Lee received the B.S. degree from the Department of Precision Mechanical Engineering, Kunsan national University, Kunsan, Korea in 2002, and the M.S. degree from the Department of Precision Mechanical Engineering, Hanyang University, Seoul, Korea in 2005. Currently, he is the Post Master in Hanyang University, Korea. His major interests include design and kinematic/dynamic analysis on multi-purpose field robots and service robots. He is a member of the Architectural Institute of Korea. Bum Seok Park received the B.S. degree from the Department of Mechanical Engineering, Hanyang University, Ansan, Kyung-gi Do, Korea in 1993, and the M.S. degree from the Department of Mechatronics Engineering, Hanyang University, Seoul, Korea in 1998. He is a Ph.D. from the Department of Mechatronics System Engineering, Hanyang University, Seoul, Korea From 2006. He is currently the post-doctor in Hanyang University, Korea. His major interests include embedded robot control system on multi-purpose field robot and service robot. He is a member of the Korea Society of Mechanical Engineers, Korean Society of Machine Tool Engineers. Sang Heon Lee graduated with the B.S. degree in Precision Mechanical Engineering from Hanyang University, Seoul, Korea in 1992. He received the M.S. degree in Precision Engineering from KAIST, Taejon, Korea in 1994 and the Ph.D. degree in Mechanical Engineering from KAIST in 2001. Currently, he is a senior researcher in Samsung Corporation, Korea. His major interests include the kinematic/dynamic analysis on multi-body system, application of field robots, and automation in construction. ChangSoo Han received the B.S. degree from the Department of Mechanical Engineering, Seoul National University Technology, Seoul, Korea in 1983, and the M.S. and Ph.D. degrees from the Department of Mechanical Engineering, University of Texas at Austin, in 1985 and 1989, respectively. From May 1988 to September 1989, he was a Research Assistant, Robotics Lab in Mechanical Engineering about manufacturing of the high resolution micro manipulator module. In March 1990, he joined Hanyang University, Ansan, Kyungki-Do, Korea as a Professor, Department of Mechanical Engineering. From March 1993 to February 1995, he was a Vice President, The Research Institute of Engineering & Technology of the Hanyang University. From August 1996 to July 1997, he was a Visiting Professor, Univ. of California at Berkeley. From September 1997 to February 1999, he was a Director, Hanyang Business Incubator. In August 2000, he joined a Branch President, The Korean Society of Mechanical Engineers. In January 2002, he joined a Committee Member, The Korean Society of Mechanical Engineers. From January 2001 to December 2001, he was an International Cooperation Director, The Institute of Control, Automation and Systems, Korea. His research interests include design, control, and application of robot, automation systems, and advanced vehicle.     

An LMI approach to robust reduced-order H ∞ filter design for polytopic uncertain systems

This paper proposes a method for robust reduced-order H _∞ filter design for polytopic uncertain systems, using linear matrix inequalities (LMIs). Sufficient LMI conditions for both robust full- and reduced-order H _∞ filter design are derived. Convex optimization problems are formulated and solved to obtain optimal H _∞ filters by using the resulting LMI conditions. The resulting conditions do not involve any non-convex rank constraints, and thus the proposed method for H _∞ filter design guarantees global optimum solutions. Numerical examples are presented to show the effectiveness of the proposed method. Recommended by Editorial Board member Huanshui Zhang under the direction of Editor Young Il Lee. This work was supported by the Brain Korea 21 Project and the Basic Research Program of the Korea Science and Engineering Foundation under grant R01-2006-000-11373-0. Hyoun-Chul Choi received the B.S., M.S., and Ph.D. degrees in Control and Instrumentation Engineering from Ajou University, Suwon, Korea, in 1995, 1997, and 2006, respectively. He was a Visiting Researcher at Griffith University, Brisbane, Australia, from 2001 to 2002, and a Postdoctoral researcher at Ajou University, Suwon, Korea, from 2006 to 2007. Since 2008, he has been with ASRI, School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea, where he is currently a Postdoctoral Researcher. His research interests include LMI-based control, optimal and robust control, network-based control, and mechatronics. Dongkyoung Chwa received the B.S. and M.S. degrees from the Department of Control and Instrumentation Engineering in 1995 and 1997, respectively, and the Ph.D. degree from the School of Electrical and Computer Engineering in 2001, all from Seoul National University, Seoul, Korea. From 2001 to 2003, he was a Postdoctoral Researcher with Seoul National University. In 2003, he was a Visiting Research Fellow at The University of New South Wales, Australian Defence Force Academy, and was the Honorary Visiting Academic at the University of Melbourne, Melbourne, Australia. In 2004, he was a BK21 Assistant Professor with Seoul National University. Since 2005, he has been an Assistant Professor with the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea. His research interests are nonlinear, robust, and adaptive control theories and their applications to the robotics, underactuated systems including wheeled mobile robots, underactuated ships, cranes, and guidance and control of flight systems. Suk-Kyo Hong received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Seoul National University, Seoul, Korea, in 1971, 1973, and 1981, respectively. His major graduate research works were centered on speed control of induction motors. He was an Exchange Professor at Rensselaer Polytechnic Institute, Troy, NY, from 1982 to 1983, and at the Institut National de Recherche en Informatique et en Automatique, France, from 1988 to 1989. He has been with the faculty of the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea, since 1976, and was a Visiting Professor at Griffith University, Australia, in 2001 and 2002. His current research interests include robust robot control, microprocessor applications, factory automation, and computer integrated manufacturing.     

Oscillation amplitude-controlled resonant accelerometer design using a reference tracking automatic gain control

In this paper, it is presented a novel approach for the self-sustained resonant accelerometer design, which takes advantages of an automatic gain control in achieving stabilized oscillation dynamics. Through the proposed system modeling and loop transformation, the feedback controller is designed to maintain uniform oscillation amplitude under dynamic input accelerations. The fabrication process for the mechanical structure is illustrated in brief. Computer simulation and experimental results show the feasibility of the proposed accelerometer design, which is applicable to a control grade inertial sense system. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Hyun Seok Yang. This work was supported by the BK21 Project ST·IT Fusion Engineering program in Konkuk University, 2008. This work was supported by the Korea Foundation for International Cooperation of Science & Technology(KICOS) through a grant provided by the Korean Ministry of Education, Science & Technology(MEST) in 2008 (No. K20601000001). Authors also thank to Dr. B.-L. Lee for the help in structure manufacturing. Sangkyung Sung is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the M.S and Ph.D. degrees in Electrical Engineering from Seoul National University in 1998 and 2003, respectively. His research interests include inertial sensors, avionic system hardware, navigation filter, and intelligent vehicle systems. Chang-Joo Kim is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the Ph.D. degree in Aeronautical Engineering from Seoul National University in 1991. His research interests include nonlinear optimal control, helicopter flight mechanics, and helicopter system design. Young Jae Lee is a Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the Ph.D. degree in Aerospace Engineering from the University of Texas at Austin in 1990. His research interests include integrity monitoring of GNSS signal, GBAS, RTK, attitude determination, orbit determination, and GNSS related engineering problems. Jungkeun Park is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University. Dr. Park received the Ph.D. in Electrical Engineering and Computer Science from the Seoul National University in 2004. His current research interests include embedded real-time systems design, real-time operating systems, distributed embedded real-time systems and multimedia systems. Joon Goo Park is an Assistant Professor of the Department of Electronic Engineering at Gyung Book National University, Korea. He received the Ph.D. degree in School of Electrical Engineering from Seoul National University in 2001. His research interests include mobile navigation and adaptive control.     

A Power-Aware Branch Predictor by Accessing the BTB Selectively

Microarchitects should consider power consumption, together with accuracy, when designing a branch predictor, especially in embedded processors. This paper proposes a power-aware branch predictor, which is based on the gshare predictor, by accessing the BTB （Branch Target Buffer） selectively. To enable the selective access to the BTB, the PHT （Pattern History Table） in the proposed branch predictor is accessed one cycle earlier than the traditional PHT if the program is executed sequentially without branch instructions. As a side effect, two predictions from the PHT are obtained through one access to the PHT, resulting in more power savings. In the proposed branch predictor, if the previous instruction was not a branch and the prediction from the PHT is untaken, the BTB is not accessed to reduce power consumption. If the previous instruction was a branch, the BTB is always accessed, regardless of the prediction from the PHT, to prevent the additional delay/accuracy decrease. The proposed branch predictor reduces the power consumption with little hardware overhead, not incurring additional delay and never harming prediction accuracy. The simulation results show that the proposed branch predictor reduces the power consumption by 29-47%.     

Swing-up control for an inverted pendulum with restricted cart rail length

In this paper, we propose a new swing-up strategy for cart inverted pendulums with restricted rail length. The proposed swing-up strategy is derived from a new Lyapunov function. The Lyapunov function is defined as the sum of the square of the pendulum energy and the weighted square of the cart’s velocity. The resulting swing-up strategy is represented in a compact form and has two design parameters. By adjusting these design parameters, we can affect the swing-up strategy such that the restriction on the rail length is satisfied. We also provide a state-dependent transformation to obtain voltage input to a DC motor required to generate the cart’s acceleration obtained from the proposed swing-up strategy. Finally, we illustrate the performance of the proposed swing-up law through simulation and experiments. It is shown that there is quite good correspondence between theory and experiments. Recommended by Editorial Board member Duk-Sun Shim under the direction of Editor Jae Weon Choi. This work was supported by an Inha Research Grant. Ji-Hyuk Yang received the M.Sc. degree in Electrical Engineering from Inha University, Inchon, Korea, in 2008. He is currently pursuing a Ph.D. degree in Electrical Engineering at Inha University, Inchon, Korea. His primary research interest lies in the development of rapid control prototyping environment. Su-Yong Shim received the B.Sc. degree in Electrical Engineering from Inha University, Inchon, Korea, in 2008. He is currently pursuing his M.Sc. degree in Electrical Engineering at Inha University, Inchon, Korea. His research interests are mechatronics and embedded systems. Jung-Hun Seo received the B.Sc. degree in Electrical Engineering from Inha University, Inchon, Korea, in 2008. He is currently pursuing his M.Sc. degree in Electrical Engineering at Inha University, Inchon, Korea. His research interests are mechatronics, embedded systems, and control applications. Young Sam Lee received the B.S. and M.S. degrees in Electrical Engineering from Inha University, Inchon, Korea in 1997 and 1999, respectively. He received the Ph.D. at the School of Electrical Engineering and Computer Science from Seoul National University, Seoul, Korea, in 2003. His research interests include time delay systems, receding horizon control, signal processing, and embedded systems. He is currently with the School of Electrical Engineering, Inha University, Incheon, Korea.     

Central pattern generator parameter search for a biped walking robot using nonparametric estimation based Particle Swarm Optimization

A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment. Recommended by Editorial Board member Euntai Kim under the direction of Editor Jae-Bok Song. Jeong-Jung Kim received the B.S. degree in Electronics and Information Engineering from Chonbuk National University in 2006 and the M.S. degree in Robotics from Korea Advanced Institute of Science and Technology in 2008. He is currently working toward a Ph.D. at the Korea Advanced Institute of Science and Technology. His research interests include biologically inspired robotics and machine learning. Jun-Woo Lee received the B.S. degree in Electronics, Electrical and Communication Engineering from Pusan National University in 2007. He is currently working toward an M.S. in the Korea Advanced Institute of Science and Technology. His research interests include swarm intelligence and machine learning. Ju-Jang Lee was born in Seoul, Korea, in 1948. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in 1973 and 1977, respectively, and the Ph.D. degree in Electrical Engineering from the University of Wisconsin, in 1984. From 1977 to 1978, he was a Research Engineer at the Korean Electric Research and Testing Institute, Seoul. From 1978 to 1979, he was a Design and Processing Engineer at G. T. E. Automatic Electric Company, Waukesha, WI. For a brief period in 1983, he was the Project Engineer for the Research and Development Department of the Wisconsin Electric Power Company, Milwaukee. He joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, in 1984, where he is currently a Professor. In 1987, he was a Visiting Professor at the Robotics Laboratory of the Imperial College Science and Technology, London, U.K. From 1991 to 1992, he was a Visiting Scientist at the Robotics Department of Carnegie Mellon University, Pittsburgh, PA. His research interests are in the areas of intelligent control of mobile robots, service robotics for the disabled, space robotics, evolutionary computation, variable structure control, chaotic control systems, electronic control units for automobiles, and power system stabilizers. Dr. Lee is a member of the IEEE Robotics and Automation Society, the IEEE Evolutionary Computation Society, the IEEE Industrial Electronics Society, IEEK, KITE, and KISS. He is also a former President of ICROS in Korea and a Counselor of SICE in Japan. He is a Fellow of SICE and ICROS. He is an Associate Editor of IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics.     

Automatic model-based 3D object recognition by combining feature matching with tracking

We propose a vision-based robust automatic 3D object recognition, which provides object identification and 3D pose information by combining feature matching with tracking. For object identification, we propose a robust visual feature and a probabilistic voting scheme. An initial object pose is estimated using correlations between the model image and the 3D CAD model, which are predefined, and the homography, byproduct of the identification. In tracking, a Lie group formalism is used for robust and fast motion computation. Experimental results show that object recognition by the proposed method improves the recognition range considerably. Sungho Kim received the B.S. degree in Electrical Engineering from Korea University, Korea in 2000 and the M.S. degree in Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology, Korea in 2002. He is currently pursuing his Ph.D. at the latter institution, concentrating on 3D object recognition and tracking. In So Kweon received the Ph.D. degree in robotics from Carnegie Mellon University, Pittsburgh, PA, in 1990. Since 1992, he has been a Professor of Electrical Engineering at KAIST. His current research interests include human visual perception, object recognition, real-time tracking, vision-based mobile robot localization, volumetric 3D reconstruction, and camera calibration. He is a member of the IEEE, and Korea Robotics Society (KRS).     

Development of cleaning robot system for live-line suspension insulator strings

A new cleaning robot system for suspension insulator strings was developed to prevent a power failure, which can have severe effects on the national industry and economy. Compared with existing cleaning robots using jets of water or water/air, this robot mechanism is superior in insulation as it uses a porcelain-clamping method, and is more useful in mountainous or salt damage areas by adopting a dry cleaning method without water. In addition, in order to increase its cleaning efficiency and to prevent arc generation under live-line conditions, a set of mechanized brush bristles and a voltage-balancing contactor are devised, respectively. Moreover, a manual device for its installation and removal is presented. We confirmed its effectiveness through experiments. Recommended by Editorial Board member Hyoukryeol Choi under the direction of Editor Jae-Bok Song. This work was supported by Electric Power Industry R&D Project performed by Ministry of Commerce, Industry and Energy in Korea. Joon-Young Park received the B.S. degree in Electrical Engineering in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1997 and 2004, respectively. He is now a Senior Researcher at the Strategic Technology Laboratory in Korea Electric Power Research Institute (KEPRI). His research interests include the robust control of nonlinear systems, the optimum kinematic design of robot manipulators as well as robot systems for the electric power industry. Byung-Hak Cho received the B.S. degree in Electrical Engineering from Hanyang University, Seoul, Korea, in 1982, and the M.S. and Ph.D. degrees in Nuclear Engineering from KAIST, Daejeon, Korea, in 1986 and 1996, respectively. He is now a Chief Researcher at the Strategic Tech-nology Laboratory in KEPRI, Daejeon, Korea. His research interests include robot systems for the electric power industry. Seung-Hyun Byun received the B.S. degree in Electrical Engineering from Yonsei University, Seoul, Korea, in 1992, and the M.S. degree in Electrical Engineering from KAIST, Daejeon, Korea, in 1994. He is now a Senior Researcher at the Power Generation Laboratory in KEPRI, Daejeon, Korea. His research interests include control system design, signal processing and artificial intelligence. Jae-Kyung Lee received the B.S. degree in Electrical Engineering from Kyungpook National University, Daegu, Korea, in 2002, and M.S. the degree in Electrical Engineering from KAIST, Daejeon, Korea, in 2004. He is now a Researcher at the Strategic Technology Laboratory in KEPRI, Daejeon, Korea. His research interests include the development of high-performance robot control and hazardous robot systems.     

A noise robust gait representation: Motion energy image

Gait-based human identification aims to discriminate individuals by the way they walk. A unique advantage of gait as a biometric is that it requires no subject contact and is easily acquired at a distance, which stands in contrast to other biometric techniques involving face, fingerprints, iris, etc. This paper proposes a new gait representation called motion energy image (MEI). Compared with other gait features, MEI is more robust against noise that can be included in binary gait silhouette images due to various factors. The effectiveness of the proposed method for gait recognition is demonstrated using experiments performed on the NLPR database. Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Jae-Bok Song. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University. Grant Number: R11-2002-105-09002-0 (2009). Heesung Lee received the B.S. and M.S. degrees in Electrical and Electronic Engineering, from Yonsei University, Seoul, Korea, in 2003 and 2005, respectively. He is currently a Ph.D. candidate of Dept. of Electrical and Electronic Engineering at Yonsei University. His current research interests include computational intelligence, pattern recognition, biometrics, and neural network. Sungjun Hong received the B.S. degrees in Electrical and Electronic Engineering and Computer Science, from Yonsei University, Seoul, Korea, in 2005. He is a graduate student of the combined master’s and doctoral degree programs at Yonsei University. He has studied machine learning, biometrics and optimization Imran Fareed Nizami received the B.S. degree from University of Engg. & Tech. Taxila, Pakistan and the M.S. degree in the Electrical and Electronic Engineering from Yonsei University, Seoul, Korea. He is currently a senior lecturer in Bahria University, Islamabad, Pakistan. His research interests include biometrics, gait recognition, Bayesian and neural networks. Euntai Kim received the B.S. (with top honors), M.S. and Ph.D. degrees in Electronic Engineering from Yonsei University, Seoul, Korea, in 1992, 1994, and 1999, respectively. From 1999 to 2002, he was a Full-time Lecturer with the Department of Control and Instrumentation Engineering at Hankyong National University, Gyeonggi-do, Korea. Since 2002, he has been with the School of Electrical and Electronic Engineering at Yonsei University, where he is currently an associate professor. He was a Visiting Scholar with the University of Alberta, Edmonton, Canada, and the Berkeley Initiative in Soft Computing (BISC), UC Berkeley, USA, in 2003 and 2008, respectively. His current research interests include computational intelligence and machine learning and their application to intelligent service robots, unmanned vehicles, home networks, biometrics, and evolvable hardware.     

An enhanced force and contact position sensor for micro-manipulations

In micro-manipulations, force sensing devices play an important role in the control and the assembly of micro-objects. To protect these micro-objects from damage, we must have the ability to detect the value of the minute amount of interactive force (about a few μN) upon contact between the tip and the object. To detect this micro-force, we need an optimized design of force sensor to increase the strain values at the positions we place sensing components. Stress concentration can effectively amplify the strain values measured by the force sensors. This paper investigates the effect that the notches have on increasing the strain values at the positions we attach the sensing elements. In addition, the optimal design with a flexible structure improves the sensitivity of the sensor. An algorithm that can calculate both contact force and contact position on the sensor tip is also mentioned. Besides, an optimal location of strain gauges will ensure the accuracy and stability of the measurement. Finally, analysis and experiment are done to verify the proposed idea. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This research was supported by the Ministry of Knowledge Economy and Korean Industrial Technology Foundation through the Human Resource Training Project for Strategic Technology. Tri Cong Phung received the B.S. degree in Mechanical Engineering from the HCM University of Technology, Vietnam in 2004 and the M.S. degree in Mechanical Engineering from Sungkyunkwan University in 2007. He is currently working toward a Ph.D. degree in Intelligent Robotics and Mechatronic System Laboratory (IRMS Lab), Mechanical Engineering from Sungkyunkwan University. His research interests include dexterous manipulation and touch sensors. Seung Hwa Ha received the B.S. degree in Korean University of Technology and Education, Korea in 2004. He received the M.S. degree in Mechanical Engineering from Sungkyunkwan University in 2008. He is currently working in Samsung Electronic Co. Ltd. His research interests are about strain gauge and high precision control. Yong Seok Ihn received the B.S. degree in School of Mechanical Engineering from the Sungkyunkwan University, Korea in 2006. He received the M.S. degree in Mechanical Engineering from the Sungkyunkwan University, in 2008. He is currently working toward a Ph. D. degree in the Computer Aided Modeling & Simulation Laboratory (CAMAS Lab), School of Mechanical Engineering at the Sungkyunkwan University in Korea. His research interests are precision mechatronics, dynamic system modeling, and control. Byung June Choi received the B.S. degree in School of Mechanical Engineering from the Sungkyunkwan University, Korea in 2002. He received the M.S. degree in Mechanical Engineer-ing from the Sungkyunkwan University, in 2005. He is currently working toward a Ph.D. degree in the Intelligent Robotics and Mechatronic System Laboratory (IRMS Lab), School of Mechanical Engineering at the Sungkyunkwan University in Korea. His research interests are mechanisms design, multi-robot system control, cooperation, path planning and task allocation algorithm. Sang Moo Lee was born in Seoul, Korea and educated in Seoul. He received the Ph.D. degree from the Seoul National University in Korea, in 1999. He is currently a Principal Researcher of Division for Applied Robot Technology at Korean Institute of Industrial Technology. His research interests include high-precision robot control, motion field network, and location system in outdoor environment for robots. Ja Choon Koo is an Associate Professor of School of Mechanical Engineering in Sungkyunkwan University in Korea. His major researches are in the field of design, analysis, and control of dynamics systems, especially micro precision mechatronic systems and energy transducers. He was an Advisory Engineer for IBM, San Jose, California, USA and a Staff Engineer for SISA, San Jose, CA, USA. He received the Ph.D. and M.S. degrees from the University of Texas at Austin and the B.S. from Hanyang University, Seoul, Korea. Hyouk Ryeol Choi received the B.S. degree from Seoul National University, Seoul, Korea, in 1984, the M.S. degree from Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea, in 1986, and the Ph.D. degree from Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 1994, all in Mechanical Engineering. From 1986 to 1989, he was an Associate Engineer at LG Electronics Central Research Laboratory, Seoul. From 1993 to 1995, he was at Kyoto University, Kyoto, Japan, as a Grantee of scholarship from the Japanese Educational Ministry. From 2000 to 2001, he visited Advanced Institute of Industrial Science Technology (AIST), Tsukuba, Japan, as a Japan Society for the Promotion of Sciences (JSPS) Fellow. Since 1995, he has been with Sungkyunkwan University, Suwon, Korea, where he is currently a Professor in the School of Mechanical Engineering. He is an Associate Editor of the Journal of Intelligent Service Robotics and International Journal of Control, Automation and Systems (IJCAS), and IEEE Transactions on Robotics. His current research interests include dexterous mechanism, field application of robots, and artificial muscle actuators.     

Two fusion predictors for discrete-time linear systems with different types of observations

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. Then, the accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI model with multisensor environment. Recommended by Editorial Board member Lucy Y. Pao under the direction of Editor Young Il Lee. This work was partially supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST), No. R01-2007-000-20227-0 and the Center for Distributed Sensor Network at GIST. Ha-Ryong Song received the B.S. degree in Control and Instrumentation Engineering from the Chosun University, Korea, in 2006, the M.S. degree in School of Information and Mechatronics from the Gwangju Institute of Science and Technology, Korea, in 2007. He is currently a Ph.D. candidate in Gwangju Institute of Science and Technology. His research interests include estimation, target tracking systems, data fusion, nonlinear filtering. Moon-Gu Jeon received the B.S. degree in architectural engineering from the Korea University, Korea in 1988. He then received both the M.S. and Ph.D. degrees in computer science and scientific computation from the University of Minnesota in 1999 and 2001, respectively. Currently, he is an Associate Professor at the School of Information and Mechatronics of the Gwangju Institute of Science and Technology (GIST). His current research interests are in machine learning and pattern recognition and evolutionary computation. Tae-Sun Choi received the B.S. degree in Electrical Engineering from the Seoul National University, Seoul, Korea, in 1976, the M.S. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1979, and the Ph.D. degree in Electrical Engineering from the State University of New York at Stony Brook, in 1993. He is currently a Professor in the School of Information and Mechatronics at Gwangju Institute of Science and Technology, Korea. His research interests include image processing, machine/robot vision, and visual communications. Vladimir Shin received the B.Sc. and M.Sc. degrees in Applied Mathematics from Moscow State Aviation Institute, in 1977 and 1979, respectively. In 1985 he received the Ph.D. degree in Mathematics at the Institute of Control Science, Russian Academy of Sciences, Moscow. He is currently an Associate Professor at Gwangju Institute of Science and Technology, South Korea. His research interests include estimation, filtering, tracking, data fusion, stochastic control, identification, and other multidimensional data processing methods.     

A study of the model and algorithms for handling location-dependent continuous queries

Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example, mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly, which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints, considering various types of queries. Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in 1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model checking. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications, mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data replication and placement issues in network-centric systems. Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems, especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and high-assurance systems engineering. Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools, the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford University Press High Integrity Systems Journal. I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted to high-assurance systems.     

Imitation learning of humanoid locomotion using the direction of landing foot

Since it is quite difficult to create motions for humanoid robots having a fairly large number of degrees of freedom, it would be very convenient indeed if robots could observe and imitate what they want to create. To this end, this paper discusses how humanoid robots can learn through imitation taking into consideration the fact that demonstrator and imitator robots may have different kinematics and dynamics. As part of a wider interest in humanoid motion generation in general, this work mainly investigates how imitator robots adapt a reference locomotion gait copied from a demonstrator robot. Specifically, the self-adjusting adaptor is proposed, where the perceived locomotion pattern is modified to keep the direction of the lower leg contacting the ground identical between the demonstrator and the imitator, and to sustain dynamic stability by controlling the position of the center of mass. The validity of the proposed scheme is verified through simulations on OpenHRP and real experiments. Recommended by Editorial Board member Hyoukryeol Choi under the direction of Editor Jae-Bok Song. This work was conducted as a program for the “Fostering Talent in Emergent Research Fields” in Special Coordination Funds for the Promotion of Science and Technology by the Ministry of Education, Culture, Sports, Science and Technology of Japan. This work was also supported in part by MIC and IITA of Korea through IT Leading R&D Support Project. [2009-S028-01, Development of Cooperative Network-based Humanoids Technology] Woosung Yang received his B.S. and M.S. degrees in Mechanical Engineering from Sogang University, Seoul, Korea in 2001 and 2003, and his Ph.D. degree in the School of Information Science from Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan in 2007, respectively. Since 2007, he has been a Post-doctoral Researcher in Center for Cognitive Robotics, Korea Institute of Science and Technology. His research interests include intelligent control theory, biologically inspired control and system, humanoids, and actuator controls for small form factor precision devices. Nak Young Chong received his B.S., M.S., and Ph.D. in Mechanical Engineering from Hanyang University, Seoul, Korea in 1987, 1989, and 1994, respectively. He was senior researcher at Daewoo Heavy Industries Ltd. (1994–98), visiting researcher at MEL in Tsukuba, Japan (1995–96), and postdoctoral researcher at KIST (1998). From 1998–2007, he was on the research staff of AIST in Tsukuba, Japan. In 2003, he joined the faculty of JAIST as Associate Professor of Information Science. Dr. Chong served as Co-chair of the IEEE RAS Technical Committee on Networked Robots (2004–06), and the Fujitsu Scientific Systems Robotics WG (2004–06) and Robot Information Processing WG (2006–08), respectively. He visited Northwestern University (2001) and Georgia Tech (2008–09). He is currently serving as Associate Editor of the IEEE Transactions on Robotics and the International Journal of Assistive Robotics and Systems. He is the Korea Robotics Society director of international cooperation, and a member of IEEE, RSJ, and SICE.     

Dynamic Voltage Scaling for Digital Control System Implementation

For real-time computer-controlled systems, control performances of tasks as well as energy consumption of overall system must be optimized. A control task does not have a fixed period but a range of periods in which the control performance varies. Hence, when more than one control tasks are scheduled on a single processor, an optimization problem appears. Furthermore, when an energy saving technique such as dynamic voltage scaling is used, its properties affect the control performance.Using a performance index that involves control performance and energy consumption, a static solution is proposed to obtain the optimal processor speed and a set of periods for given control tasks in O(k). Also a dynamic solution is proposed to utilize system services of real-time operating systems to overcome unavoidable deficiencies of the static solution and to further reduce the energy consumption of the overall system. The performances of proposed solutions are revealed via simulation studies.Hyung Sun Lee received his B.S. and M.S. degrees in electronics engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2000 and 2002, respectively. He is currently a Ph.D. student in the Department of Electrical Engineering and Computer Science (EECS) at KAIST. His research interests include real-time control and power-aware real-time embedded systems.Byung Kook Kim received his B.S. degree in Electronics Engineering from Seoul National University in 1975, and his M.S. and Ph.D. degrees from KAIST in 1977 and 1981, respectively. Dr. Kim was a manager and founder of the Calibration Laboratory, Woojin Instrument Co. Ltd, in 1981. He performed his postdoctoral research at the University of Michigan, Ann Arbor, Michigan, from 1982 to 1983. He returned to Woojin Instruments as a chief researcher of the R&D Department from 1984 to 1986. He joined the faculty of the Department of Electrical Engineering at KAIST in 1986, where he is currently a professor. His research interests include real-time systems, parallel and distributed systems, fault-tolerant computing, mobile robot sensing and navigation, and manipulator control.     

Static extensivity analysis for λ-definable functions over lattices

We employ a static analysis to examine the extensivity (∀x:x≤f(x)) of functions defined over lattices in a λ-calculus augmented with lattice operations. The need for such a verification procedure has arisen in our work on a generator system (called Zoo) of static program-analyzers. The input to Zoo is a static analysis specification that consists of lattice definitions and function definitions over the lattices. Once the extensivity of the functions is ascertained, the generated analyzer is guaranteed to terminate when the lattices have finite-heights. The extensivity analysis consists of a sound syntax-driven deductive rules whose satisfiability check is done by a constraint solving procedure. Hyunjun Eo: He is a Ph.D. candidate of Computer Science Dept. at KAIST (Korea Advanced Institute of Science and Technology). He received his B.S. and M.S. in Computer Science from KAIST in 1996 and 1998, respectively. For 1998–2003, he was a research assistant of the National Creative Research Initiative Center for Research On Program Analysis System. His research interest has been on static program analysis, program logics, and higher-order and typed languages. He is currently working on developing a tool for automatic generation of program analyzers. Kwangkeun Yi, Ph.D.: His research interest has been on semantic-based program analysis and systems application of language technologies. After his Ph.D. from University of Illinois at Urbana-Champaign he joined the Software Principles Research Department at Bell Laboratories, where he worked on various static analysis approaches for higher-order and typed programming languages. For 1995–2003, he was a faculty member in the Department of Computer Science, Korea Advanced Institute of Science and Technology. Since Fall 2003, he has been a faculty member in the School of Computer Science and Engineering, Seoul National University. Kwang-Moo Choe, Ph.D.: He is a professor of Computer Science at Korea Advanced Institute of Science and Technology. He received his B.S. from Seoul National University in 1976, and his M.S. and Ph.D. from Korea Advanced Institute of Science and Technology in 1978 and 1984, respectively. For 1985–1986, he was a technical staff of AT&T Bell Labs at Murray Hill. His research interest is formal language theory, parallel evaluation of logic programs, and optimizing compilers.