In the established virtual-reality system, although it is possible to transact a faculty of sensation and graphic in a single PC, virtual object forcibly treated with rigid body for the reason of the huge amount of calculation, and the number of polygon is restricted. Furthermore, there is some difficulty in the financial aspect and a program field, because the existing virtual-reality system needs at least two workstations or super computers. In this study, the new force-reflecting algorithm called as "Proxy" and a finite element method of Hyperion are applied to this system in order to transact in real-time. Consequently, though the number of polygon, which brings about an obstacle is increased in the real-time graphic transaction, this system makes it possible to transact in the real-time, not being influenced by the size of the virtual object.

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

In this paper, we propose a design method of PID controller using an improved Tabu Search. Tabu Search is improved by neighbor solution creation using Gaussian random distribution and generalized Hermite Biehler Theorem for stable bounds. The range of admissible proportional gains are determined first in closed form. Next the optimal PID gains are selected by improved Tabu Search. The results of Computer simulations represent that the proposed Tabu Search algorithm shows a fast convergence speed and a good control performance.

Target motion analysis with a sonar system in general uses a regular sampling time and thus obtains regular target information regardless of the target maneuver status. This often results in overconsumption of the limited sonar resources. We propose two methods of the IMM(interacting Multiple Model) PDAF algorithm for sonar resource management to improve target motion analysis performance and to save sonar resources in this paper. In the first method, two different process noise covariance which are used as mode sets are combined based on probability. In the second method, resource time which are processed from two mode sets is calculated based on probability and then considered as update time at next step. Performance of the proposed algorithms are compared with the other algorithms by a series of Monte Carlo simulation.

This paper presents a sensorless speed control of a DC servo motor using a fuzzy-sliding observer in the presences of load disturbances. A fuzzy-sliding observer is proposed in order to estimate the speed of a motor rotor. First, a sliding observer is used to estimate the derivative of the armature current directly using the armature current mesured in the DC servo motor. Second, the optimal gain of the Luenberger observer is set up using the fuzzy control. Experimental results show the good performance in the DC servo motor system with the proposed fuzzy-sliding observer.

Currently, high data rate PLC(Power Line Communications), up to 100 Mbps, which use frequency bandwidth between 2 MHz and 30 MHz is investigated very hard, and commercial PLC modem for low voltage powerline network (indoor) is coming soon into communication market. For the purpose of developing a fit communication system which has little distortion of signal and attenuation, it is surely necessary to know about channel environments of powerline. Especially, the impedance measurement of the powerline and impedance matching are very important. As is known, since medium-voltage powerline (22.9 ㎸) is still working, it is not so simple to measure the powerline impedance. In our study, a portable impedance measurement equipment is developed. It consists of coupling capacitor, a drain coil and impedance matching transformer. The equipment is easily connected to medium voltage line and impedance of power line is measured using a network analyzer. Also, measurement results are used for impedance matching of PLC signal. In fact, matching transformer with several different impedances are used. The matching transformer is connected between coupling capacitor and signal port. In this paper, the developed portable impedance measurement equipment and impedance measurement results are presented. Also impedance matching technique using matching transformers will be explained. We showed the result of the improved performance by the impedance matching.

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

This paper describes design of an algorithm for analyzing human activity using body-fixed 3-axis accelerometer in the small of the back. In the first step, we distinguish static and dynamic activity period using AC signal analysis. Then five postures were classified by applying the threshold in DC signal corresponding to the static activity period. Also, after comparison of average power and taking negative peak signal in the dynamic activity period, the four dynamic activities were classified by adaptive threshold method. To evaluate the performance of the proposed algorithm, the measured signals obtained from six subjects were applied to the proposed algorithm and the results were compared with the simultaneously measured video data. As a result, the activity classification rate of 95.7％ on average was obtained. Overall results show that the proposed classification algorithm has a possibility to be used to analyze the static and dynamic physical activity.

Surface visualization can be useful, particularly for internet-based education and simulation system. Since the mesh data size directly affects the downloading and operational performance, the problem that should be solved for efficient surface visualization is to reduce the total number of polygons, constituting the surface geometry as much as Possible. In this paper, an efficient polygon reduction algorithm based on Stokes＇ theorem, and topology preservation to delete several adjacent vertices simultaneously for past polygon reduction is proposed. The algorithm is irrespective of the shape of polygon, and the number of the polygon. It can also reduce the number of polygons to the minimum number at one time. The performance and the usefulness for medical imaging application was demonstrated using synthesized geometrical objects including plane. cube. cylinder. and sphere. as well as a real human data.

This paper proposes an algorithm for human sensibility evaluation using 4-channel EEG signals of the prefrontal and the parietal lobes. The algorithm uses an artificial neural network and the multiple templates. The linear prediction coefficients are used as the feature parameters of human sensibility. Comfortableness for chairs and temperature/humidity are evaluated. Many conventional researches have emphasized that a wave of left prefrontal lobe is activated in case of positive sensibility and that of right prefrontal lobe is activated in case of negative sensibility. So the power ratio of a wave is obtained from FFT computation and the results are compared. The results of the comfortableness evaluation for temperature and humidity showed that the outputs of the proposed algorithm coincided with corresponding sensibilities depending on the task of the temperature and the humidity. The . conventional method using a wave is hardly related with comfortableness. And it is also observed that the uncomfortable state due to the high temperature and humidity can be easily changed to the comfortable state by small drop of the temperature and the humidity. It seems to be good results to get 66.7％ of evaluation performance in spite of using EEG and the subject independent approach.

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.