In this paper, a robust receding horizon control algorithm, which can be employed for a wide class of nonlinear systems with control and state constraints, modeling errors, and disturbances, is considered. In a neighborhood of the origin, a linear feedback controlelr for the linearized system is applied. Outside this neighborhood, a receding horizon control is applied. Robust stability is proved considering the time taken to solve an optimal control problem so that the proposed algorithm can be applied as an on-line controller.

Many recent supercomputers employ a kind of switch-based multistage interconnection network architectrues (MINs) for constructing scalabel parallel compters. This paper proposed a new routing method, hybrid wormhole and virtual-cut through routing (HWCR) for the prevention of rapid performance degradation comming from a conflict in links usage at hot traffic situation. This HWCR through (VCT) for the fast removing temporal stagger, result in seamless flow of packet stream. When the blocked link is removed, wormhole routing is resumed. The HWCR method adopted a hierachical buffer scheme for improving the network performance and reducing the cost in BMINs. We could get optimum buffer size and communicatin latency through the computer simulation based on proposed HWCR, and the results were compared to those using wormhole and VCT.

A current-controlled OTA-R schemitt trigger circuit is described. It consists of two OTA's and two resistors. The output level of the circuit is determined by one OTA and one resistor, and the threshold level by the other OtA and resistor. The theory of operation is presented and computer simulation results and experimental results using CMOS arrays are used to verify theoretical predictions. The results show close agreement between predicted behaviour and experimental performance.

In this paper, we propose a fast hierarchical image segmentation using mathematical morphology. The proposed segmentation method is composed of five basic steps; multi-thresholding, open-close by reconstructing, mode operation, marker extraction, and region decision. In the multi-thresholding, an input image is simplified by Lloyd clustering algorithm. The multi-thresholded image then is more simplified by open-close by reconstruction and mode operating. In the region decision, to which region each uncertainty pixel belongs finally is decided by a watershed algorithm. Experimental results show that the quality of the segmentation results by the proposed method is not inferior to that by the conventional method and the average times elapsed by the proposed method can be reduced by one tghird of those elapsed by the conventional method.

A parallel looking stereo camera was mainly used as an input sensor for digital image processing, image understanding and the extraction of 3 dimensional information. Theoretical analysis and performance evaluation are dealt in this paper for a convergent looking stereo camera model having a fixation point with the result of crossing optical axes. The quantization error, depth resolution and equidepth map due to digital pixels, and the misalignments effects of pan, tilt and roll angles are analyzed by using rhe relationship between the reference and image coordinate systems. Also horopter, epipolar lines, probability density functions of the depth error, and stereo fusion areas for the two camera models are discussed.

This paper proposes a method which analyzes facial shape and extracts positions of eyes regardless of the tilt and the size of input iamge. With the extracted feature parameters of facial element by the method, full human faces are recognized by a neural network which BP algorithm is applied on. Input image is changed into binary codes, and then labelled. Area, circumference, and circular degree of the labelled binary image are obtained by using chain code and defined as feature parameters of face image. We first extract two eyes from the similarity and distance of feature parameter of each facial element, and then input face image is corrected by standardizing on two extracted eyes. After a mask is genrated line historgram is applied to finding the feature points of facial elements. Distances and angles between the feature points are used as parameters to recognize full face. To show the validity learning algorithm. We confirmed that the proposed algorithm shows 100% recognition rate on both learned and non-learned data for 20 persons.

We developed a speech coder that can transfer data as well as speech for CDMA digital cellular system. We describe the design method of the speech coder that uses QCELP algorithm for speech coding. The speech coder is implemented on a single fixed-point DSP chip (TMS320C50). the coder has the complexity such as 4K words in RAM, 10K words in ROM, and 33 MIPS in execution time. The developed speech coder is fully tested and successfully working on the CDMA base station system.

This paper proposes a new COA (center of area) defuzzifier that is working in the accurate and fast manner. The proposed COA defuzzifier involves both membership values and the spans of membership functions in clauclating a crisp value. In additon, it avoid division by replacing the COA calculation with the searching of the momentum equilibrium point. The moment equilibrium point is searched in the coarse-to-fine manner such that the moment computing points during the coarse searching are moved in the interval of fuzzy terms until they are reached at two adjacent fuzzy terms searching method accerlates the finding of the moment equilibrium point by O(M) mazimally when compared iwth the equal interval searching method of ruitz. In order to verify the accuracy of the proposed COA defuzzifier, the crisp values obtained form the proposed coarse-to-fine searching are compared with the precise crisp values from the arithmetic calculation. Application to the truck backer-upper control problem of the proposed COA defuzzifier is presented. The control performance is compared with that of the conventional COA defuzzifier in tems of the average tracing distance.

This paper presents an efficient parallel implementation and its performance evaluations of a SOFM neural netowrk on the multicomputer. We investigate the parallel performance as the size of a neural network N, the number of the patterns L, and the number of the processors p increase. We propose an analytica performance evaluation model for eac of the parallel implementations and verified the validity of the model through experiments. Analytical result show that the number of processors for a maximum speedup of the network decomposition nd the training-set decomposition increases in proportion to .root.N and .root.L, respectively. The performances of the both decompositions depend on the number of training patterns L and the size of the neural network N and, if L.geq.0.423N, the performance of trhe training-set decomposition is proved to be better than that of the network decomposition.

This paper proposes an algorithm for analyzing illocutionary force type (IfT)s in a dialogue, based on the context and modal information in the ending of a word. In korean, the variation of an illocutionary force type that represents a speaker's intention frequently occurs at the ending of a word, according to the type of modal information. And in an analysis of speech acts, the modal information illocutionary force types. In this paper, we analyze real dialogue dta, classify the types of illocutionary forces, perform the manual tagging of IFTs and show the freqency of each IFT's occurence. And we also propose an algorithm to extract IFTs, based on the relationship between the analyzed IFTs and the endings of a word. And we use this proposed algorithm to make an experiment on dialogue data and show its efficiency.

In this paper, we propose multiple component neural network(MCNN) which learn partitioned patterns in each multiple component neural networks by reducing dimensions of input pattern vector using PCA (principal component analysis). Procesed neural network use Oja's rule that has a role of PCA, output patterns are used a slearning patterns on small component neural networks and we call it CBP. For simply not solved patterns in a network, we solves it by regenerating new CBP neural networks and by performing dynamic partitioned pattern learning. Simulation results shows that proposed MCNN neural networks are very small size networks and have very fast learning speed compared with multilayer neural network EBP.

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

In this paper, we propose a neural network which detect edges of different orentation and spatial frequency in arbitrary image data. We constructed the proposed neural network iwth two different types neural network. A diffusion network performs the gaussian operation efficiently by the diffusion process. And the spatial difference network has specially designed connections suitble to detect the contours of a specific oriention. Simulation results showed that the proposed neural network can extract the edges of selected orientation efficiently by applying the neural network to a test pattern and the real image.

In this study a two-stage fast pulse generaor using magnetic switches is proposed. The scheme consist of a switch, an inductor and two pairs of capacitor and saturable inductors, a linear transformer. The basic principle and the operation are described using a set of given parameters. The main issue of the magnetic pulse genration scheme is the system efficiency. This study focuses on the system efficiency improvement using magnetic switches. The voltage compression ratio, energy transfer with respect to core area are investigated. The output voltage and transferred energy as a function of input voltage are also included. Also, an analysis and experiments are performed to verify the porposed topology by implementing a 10[J] class experimental circuit. The efficiency of the transferred energy a tload side is 82%.

Increasing the switching frquency is essential to achieve the high density of switched mode power supplies, but this leads to the increase of switching losses. A number of new soft switching converters have been presented ot reduce switching losses, but most of them may have some demerits, such as the increase of voltage/current stresses and high conduction losses. To overcome these problems, the ZVT-PWM converter has recently been presented. in this paper, the operation characteristics of the ZVT-PWM boost converter is analyzed, and the steady-states (DC) and small-signal model of this converter are derived and analyzed, and then the transfer functions of this converter are derived. The transfer functions of ZVT-PWM boost converter are similar to those of the conventional PWM boost converter, but the transfer characteristics are affecsted by te duty ratio and the switching frequency.