Design of a reliable control systems is investigated for a class of uncertain linear plants. The uncertainty considered here is for the ase of uncertainty in the system matrix. A decentralized control scheme with two observer-based feedback controllers is developed, and it is shown that the resulting closed-loop system is reliable in the sense that the control scheme provides guaranteed stability and $H_{\infty}$-norm bounded performance in the event of sensor and/or actuator failures as well as in the presence of parameter uncertainties. We observed that soft-type failures were additional exogenous inputs to the closed-loop system. As a results, the sensor and/or actuator failures can be tolerated in the design, which is achieved by extending the methodology developed in.

This paper describes data placement schemes that provide uniform and balanced to multiple disks load for retrievals of VBR (variable bit rate) video at varying retrieval speeds. To support maximum concurent users at arbitrary-speed playbacks in a disk-arry based system, the hot spot disks should be carefully avoided. In this paper, we extend the proposed scheme, prime round-robin(PRR), for VBR video. In addition, we have compared the performance of PRR and PRR (PRR extension).

본 연구는 경계요소법을 이용하여 토르소 표면전위를 구하는 연구이다. '그린의 2차정리'로 부터 토르소 표면 전위를 구하는 지배방정식을 유도한 후 균질성, 등방성 매질인 경우에 적합한 경계요소법을 적용하였다. 구모델을 이용하여 구의 중심에 소스를 배치하고 구표면의 전위를 경계요소법으로 구한 계산치와 이론치를 비교함으로서 알고리즘의 타당성을 검증하였다. 토르소 모델에 경계요소법을 적용하여 시뮬레이션 한 결과, 심실흥분에 의한 토르소 표면 등전위도 패턴을 정상심전도의 매핑결과와 유사한 패턴을 보였다.

This paper presents a comparative study of four pattern recognition algorithms which are invariant to translations, rotations, and scale changes of the input object; namely, object shape features (OSF), geometrica fourier mellin transform (GFMT), moment invariants (MI), and centered polar exponential transform (CPET). Pattern description is obviously one of the most important aspects of pattern recognition, which is useful to describe the object shape independently of translation, rotation, or size. We first discuss problems that arise in the conventional invariant pattern recognition algorithms, or size. We first discuss problems that arise in the coventional invariant pattern recognition algorithms, then we analyze their performance using the same criterion. Computer simulations with several distorted images show that the CPET algorithm yields better performance than the other ones.

In this paper, we propose a satellite multispectral image data compression method using classified vector quantization. This method classifies each pixel vector considering band characteristics of multispectral images. For each class, we perform both intraband and interband vector quantization to romove spatial and spectral redundancy, respectively. And residual vector quantization for error images is performed to reduce error of interband vector quantization. Thus, this method improves compression efficiency because of removing both intraband(spatial) and interband (spectral) redundancy in multispectral images, effectively. Experiments on landsat TM multispectral image show that compression efficiency of proposed method is better than that of conventional method.

In this paper, a new hierarchical motion estiamtion scheme using the wavelet transformed multi-resolution image layers is proposed. Compared with the full search motion estimation method, the existing hierarchical methods remarkably reduce the amount of the computation but their efficiencies are depreciated by the local minima problem. In order to solve the local minima problem, the multi-resolution image layers are composed using the wavelet transform and the number of layers participated in the motion estimation for a block is determined by considering of its low band energy and higher band energy on the first wavelet transformed layer. The ratio between higher band energy and low band energy of each block is evaluated and in the case of the blocks which include relatively large higher band energy, the motion estimation is carried out in the high resolution layer. Otherwise, all layers are used. The final motion vectors are obtained in the first wavelet transformed layer. So less bits for motion vectors are transmitted, and the decomposition of received image using inverse wavelet transform decreases the blocking effect.

In this paper, we propose a new temporal interpolation method based on spatial filtering. Compared with the conventional method, the proposed one may use a few adjacent frames and apply temporal lowpass filtering. To develop this method, we follow the basic approach of sampling rate conversion. Additionally, we use some assumption of video sequence : moving object has constant velocity rigid translational motion. From them, spatial filtering for temporal sampling rate conversion is described. This method has a lot of noise immunity on a motion vector and doesn't make a great difference from the original frame. The interpolated frame shows moderate change even there is a great time difference. This method has exactly same description of motion adaptive spatial filter which has an efficient temporal band-limiting characteristics. It imposes the possibility to make video sequence with good pictural quality.

In this paper, we propose a novel color image segmentation algorithm based on clustering in 3-dimensional color space employing the mathematical morphology. More specifically, since we take into account the topological properties such as the shape, connectivity and distribution of clusters in the clustering process, the number of clusters in the color cube, as well as their centers, can be easily obtained, without a priori knowledge on the input images. Intensive computer simulation has been performed and the results are discussed in this paper. The resutls of the simulation on the images in various color coordinates show that the segmentation is independent of the choice of color coordinates and the shape of clustes. Segmentation results of the vector quantizer are also presented for the comparison purpose.

In this paper, we propose a motio estiamtion algorithm using low-resolution quantization to reduce the computation of the full search algorithm. The proposed algorithm consists of the low-resolution search which determins the candidate motion vectors by comparing the low-resolution image and the full-resolution search which determines the motion vector by comparing the full-resolution image on the positions of the candidate motion vectors. The low-resolution image is generated by subtracting each pixel value in the reference block or the search window by the mean of the reference block, and by quantizing it is 2-bit resolution. The candidate motion vectors are determined by counting the number of pixels in the reference block whose quantized codes are unmatched to those in the search window. Simulation results show that the required computational cost of the proposed algorithm is reduced to 1/12 of the full search algorithm while its performance degradation is 0.03~0.12 dB.

In this paper, we propose a new algorithm that obtains the surfac eorientation of the texture image using structural approaches. The proposed method showed that structural approaches can be effectively used in 3-D analysis of textures as well as description and segmentation without additional information. By examining fourier power spectrum of the texture image, we detemine the tilt of the textured surface. Then, 1-D projection information of the texture in the obtained tilt direction is used to compute the slant. Using the obtained information, we can compute the vanishing point, and rearrange the textured surface with lines converging to the vanishing point and lines perpendicular to the tilt direction. In the experimental results, we have ascertained the proposed algorithm can make a rpecise 3-D analysis of structural textures.

In this paper, a new korean digita speech recognition technique was proposed using muktolayer perceptron (MLP). In spite of its weakness in dynamic signal recognition, MLP was adapted for this model, cecause korean syllable could give static features. It is so simle in its structure and fast in its computing that MLP was used to the suggested system. MLP's input vectors was transformed using karhunen-loeve transformation (KLT), which compress signal successfully without losin gits separateness, but its physical properties is changed. Because the suggested technique could extract static features while it is not affected from the changes of syllable lengths, it is effectively useful for korean numeric recognition system. Without decreasing classification rates, we can save the time and memory size for computation using KLT. The proposed feature extraction technique extracts same size of features form the tow same parts, front and end of a syllable. This technique makes frames, where features are extracted, using unique size of windows. It could be applied for continuous speech recognition that was not easy for the normal neural network recognition system.

In this paper, we propose an automatic recognition algorithm using the object centroid of a facial image. First, we separate the facial image from the background image using the chroma-key technique and we find the centroid of the separated facial image. Second, we search nose in the facial image based on knowledge of human faces and the coordinate of the object centroid and, we calculate 17 feature parameters automatically. Finally, we recognize the facial image by using feature parameters in the neural networks which are trained through error backpropagation algorithm. It is illustrated by experiments by experiments using the proposed recogniton system that facial images can be recognized in spite of the variation of the size and the position of images.

This paper presents the implementation of variable structure control system for a linear or nonlinear system using neural networks. The overall control system consists of neural network controller and a reaching mode controller. While the former approximates the equivalent control input on the sliding surface, the latter is used to bring the entire system trajectories toward the sliding surface. No supervised learning procedures are needed and the weights of the neural network are tuned on-line automatically. The neural netowrk-based variable structure control system is applied to a nonlinare unstable inverted pendulum system through computer simulations, and implemented using a microcomputer (80486-50MHz) and applied to the DC servomotor position control system. Simulation and experimental results show the expected approximation sliding property is occurred. The proposed controller is compared with a PID controller and shows better performance than the PID controller in abrupt plant parameter change.