The purpose of multiple alarm processing is to give the operator the correct information and perception of the malfunction present in the plant. In this thesis, an APS(Alarm Processing System) is studied for fossil power plants. This APS is based on a cause-consequence trees in the knowledge representation aspect for alarm and plant and adapts alarm filtering methods using fired time information in the decision aspect. Through the cause-consequence trees and filtering methods, the Alarm Processing System finds the cause alarm among the fired multiple alarms and calculates the cause degree which represents the possibility of a fault occurring in the instruments of the plant with the information of fired alarm. The knowledge base is built via interviews and questionaries with the expert operators on the Seoul power plant unit 4. Finally, the validity of the studied APS is shown via simulations.

This paper describes an approach for constucting a Rectilinear Steiner Tree (RST) derivable from a Minimum Spanning Tree (MST), using Associative Processor (AP). We propose a fast parallel algorithm using AP's basic algorithms which can be realized by the processing capability of rudimentary logic and the selective matching capability of Content- Addressable Memory (CAM). The main idea behind the proposed algorithm is to maximize the overlaps between the consecutive edges in MST, thus minimizing the cost of a RST. An efficient parallel linear algorithm with O(n) complexity to construct a RST is proposed using an algorithm to find a MST, where n is the number of nodes. A node insertion method is introduced to allow the Z-type layout. The routing process which only depends on the neighbor edges and the no-rerouting strategy both help to speed up finding a RST.

On a bus-based shared memory multiprocessing system, the system bus monitoring and analysis are crucial for system integration test and performance analysis. In this paper, the design and implementation of a bus monitoring instrument for the TICOM-III system are decribed. The instrument dedicated to TICOM-III, which is called the Bus Information Procssing Unit, analyzes the bus state and measures the bus utilization. It performs many useful functions to help debugging the system, and offers a simple user interface.

In this paper, we investigate edge extraction and segmentation of range images. We first discuss problems that arise in the conventional region-based segmentation methods and edge-based ones using principal curvatures, then we propose an edge-based algorithm. In the proposed algorithm, we extract edge contours by using the Gaussian filter and directional derivatives, and segment a range image based on extracted edge contours, Also we present the problem that arises in the conventional thresholding, then we propose a new threshold selection method. To solve the problem that local maxima of the first- and second- order derivatives gather near step edges, we first find closed roof edge contours, fill the step edge region, and finally thin edge boundaries. Computer simulations with several range images show that the proposed method yields better performance than the conventional one.

The Discrete Cosine Transform(DCT) is effective technique for image compression, which is widely used in the area of digital signal processing. In this paper, an efficient DCT processor is proposed and simulated by using Verilog HDL. This algorithm is improved 60% in processing speed, but it's somewhat complicate compared with Y. Arai's algorithm. This algorithm will be used efficiently for real time image processing.

This paper proposes an efficient noiseless encoding method of vector quantization(VQ) index using a dynamic index mapping. Using high interblock correlation, the proposed index mapper transforms an index into a new one with lower entropy. In order to achieve good performance with low computational complexity, we adopt 'the sum of differences in pixel values on the block boundaries' as the cost function for index mapping. Simulation results show that the proposed scheme reduces the average bitrate by 40 - 50 % in ordinary VQ system for image compression. In addition, it is shown that the proposed index mapping method can be also applied to mean-residual VQ system, which allows the reduction of bitrate for VQ index by 20 - 30 %(10 - 20 % reduction in total bitrate). Since the proposed scheme is one for noiseless encoding of VQ index, it provides the same quality of the reconstructed image as the conventional VQ system.

By using a concept of interrelation quadruplet between line segments, a new method for recognition of object families is introduced. The interrelation quadruplet, which is invariant under similarity transform of a pair of line segments, is used as a feature information for polygonal shape recognition. Several useful propertes of the interrelation quadruplet are derived in relation to efficient recognition of object families. Compared with the previous methods, the proposed method requires only small space of storage and is shown to be computationally simple and efficient.

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

In this paper, a morphological shape decomposition method is proposed for the purpose of pattern recognition and image compression. In the method, a structuring element that geometrical characteristics is more similar to the shape function is preselected. The shape is decomposed into the primitive elements corresponding to the structuring element. A gray scale image also is transformed into 8 bit plane images for the hierarchical reconstruction required in image communication systems. The shape in each bitplane is decomposed to the proposed method. Through the experiment. it is proved that the description error is reduced and the coding efficiency is improved.