As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.

A fault detecting method of ternary logic is proposed by using the spectral coefficients of the Chrestenson function. Fault detecting conditions are derived for a stuck-at fault in case of single input, multiple inputs and internal lines in the ternary logic. The detecting conditions for min/max bridging faults are also considered. When using this fault analysis method, it is possible to detect faults without the test vector and minimize high volume memory for storing the vector and response data. Thus, the computational complexity for the test vector can be decreased.

Misalignment of recording head position results in inter-track interference (ITI), which is a primary factor limiting radial(track) density in current digital magnetic channels. This paper proposes a multi- channel adaptive PRML for digital high-density recording channels, and compares it with the conventional single-channel PRML in the presence of ITI for the per- formance evaluation. Simulation results show that the proposed method removes ITI effectively when heal-misalignment occurs, then improving its performance significantly as compared with the single- channel PRML. As a result, it is confirmed that multi-channel adaptive PRML is well suited for high-density recording in digital magnetic channels.

In this paper, a modified Chebyshev low-pass filter function is proposed. The modified Chebyshev filter function exhibits ripples diminishing toward .omega. = 0 in the passband. So, the modified filter function is realizable in the passive doubly-terminated ladder network for the order n even or odd, thus lending itself amenable to active RC or switched capacitor filters through the simulation techniques. Besides the passive doubly-terminated ladder realizability, lower pole-Q values of the modified function are accountable for improved phase and delay characteristics, as compared to classical function. We have designed the 6th order passive doubly-terminated network using the modified function. And then a continuous-time DDA(Differential Difference Amplifier) filter, which has no matching requirement, is realized by leap-frog simulation technique for fabrication. In the HSPICE simulation results, we confirmed that the designed continuous-time DDA filter characteristics are agreement with the passive filter.

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

A hybrid approach which includes edge- and region-based methods is considered. The modified non-linear Laplacian(MNL) filter is used for feature extraction. The matching algorithm has three steps which are edge, signed region, and residual region matching. At first, the edge points are matched using the sign and direction of edges. Then, the disparity is propagated from edge to inside region. A variable window is used to consider the local method which give accurate matched points and area-based method which can obtain full-resolution disparity map. In addition, a new relaxation algorithm for considering matching possibility derived from normalized error and regional continuity constraint is proposed to reduce the mismatched points. By the result of simulation for various images, this algorithm is insensitive to noise and gives full- resolution disparity map.

The goal of enhancement is to improve the perceptual aspect and visual appearance of images for human viewers. The objectives of image enhancement vary according to its specific application and an image enhancement algorithms used for a specific objective may not be accepted in some other applications. In this paper we review some of conventional enhancement techniques, such as global histogram equalization(GHE), local histogram equalization(LHE), clipped histogram equalization(CHE). We also describe some modified version of these algorithms. The proposed method is to detect detail information. We distinquish edge from nonedge and apply histigram equalization respectively. Simulation results demonstrate the performance of the proposed method for medical image.

In this paper we propose a region-adaptive CELP image coder for still images at low bit rates below 0.5 bpp. The proposed method partitions the image into stochastically similar regions by the minimum spanning tree method and finds prediction coefficients for each region using a 2- dimensional linear prediction model. Coding is carried out on 8$\times$8 blocks and when there are several regions included in a block, an image is synthesized using the prediction coefficients of each region. Computer simulation results show that the proposed method allows improved synthesized image over conventional block-adaptive CELP methods, especially at edges. In addition, performance comparison with the JPEG DCT method shows that while the JPEG method shows block distortion and staircase effects (ragged edges) at bit rates below 0.5 bpp, the proposed CELP method shows improved synthesized images with such effects reduced.

Conventional full search block matching algorithm for motion estimation is computationally intensive and the resulting hardware cost is very high. In this paper, we present an efficient block matching algorithm using a 16:1 subsampling technique, and describe its hardware design. The algorithm reduces the number of pixels in calculating the mean absolute difference at each search location, instead of reducing the search locations.The algorithm is an extension of the block mating algorithm with 4:1 subsampling proposed by Liu and Zaccarin such that the amount of computation is reduced by a fact of 4(16 compared to the full search block matching algorithm) while producing similar performance.The algorithm can efficiently be designed into a hardware for real-time applications.

This paper proposes an algorithm for machine recognition of phonemes in continuously spoken Korean. The proposed algorithm is a static strategy neural network. The algorithm uses, at the stage of training neurons, features such as the rate of zero crossing, short-term energy, and either PARCOR or auditory-like perceptual linear prediction(PLP) but not both, covering a time of 171ms long. Numerical results show that the algorithm with PLP achieves approximately the frame-based phoneme recognition rate of 99% for small vocabulary recognition experiments. Based on this it is concluded that the proposed algorithm with PLP analysis is effective in phoneme recognition.

A data and clock recovery integrated circuit for MAC (Multiplexed Analog Component) TV standard is described. The chip performs the recovery of a system clock from a digitally encoded voice signal, clamping of a video signal for DC-level restoration, and precise gain control of a video signal in the presence of a large amplitude variation. A PLL (Phase Locked Loop) is used for timing recovery and a new gain control circuit is proposed which enhances its accuracy and dynamic range by employing two identical four-quadrant analog multipliers. The chip is designed in full custom with 1.5um BiCMOS technology, and layout verification is completed by post-simulation with the extracted circuit.

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

This paper is a study on the recognition of Korean proverb using neural network and Markov model. The neural network uses, at the stage of training neurons, features such as the rate of zero crossing, short-term energy and PLP-Cepstrum, covering a time of 300ms long. Markov models were generated by the recognized phoneme strings. The recognition of words and proverbs using Markov models have been carried out. Experimental results show that phoneme and word recognition rates are 81. 2%, 94.0% respectively for Korean proverb recognition experiments.

In this paper, an optimal variable structure controller with a multilayer neural inverse identifier is proposed. A multilayer neural network with error back propagation learning algorithm is used for construction the neural inverse identifier which is an observer of the external disturbances and the parameter variations of the system. The variable structure controller with the multilayer neural inverse identifier not only needs a small part of a priori knowledge of the bounds of external disturbances and parameter variations but also alleviates the chattering magnitude of the control input. Also, an optimal sliding line is designed by the optimal linear regulator technique and an integrator is introduced for solving the reaching phase problem. Computer simulation results show that the proposed approach gives the effective control results by reducing the chattering magnitude of control input.

GAs, effective stochastic search algorithms based on the model of natural evolution and genetics, have been successfully applied to various optimization problems. When population size is not large, GAs often suffer from the phenomenon of premature convergence in which all chromosomes in the population lose the diversity of genes before they find the optimal solution. In this paper, we propose that a new heuristic that maintains the diversity of genes by adding some chromosomes with random mutation and selective mutation into population during evolution. And population size changes dynamically with supplement of new chromosomes. Experimental results for several test functions show that when population size is rather small and the length of chromosome is not long, this method is effective.

An integrated fault diagnosis system for heterogeneous manufacturing environments is developed. This system has a contrast with existing diagnosis systems in the respect that they are mostly for diagnosing faults on individual machines. In addition to the usual (e.g., audio, electrical) diagnostic signals, the characteristics of products from the machines are considered as the unifying diagnostic parameters among heterogeneous machines in the diagnosis. The system is composed of a knowledge representation scheme and a diagnostic query processing mechanism. Its knowledge representation scheme allows the diagnostic knowledges from heterogeneous unit diagnostic systems to be uniformly expressed in terms of the causal relations among relevant data items. It is flexible in the sense that causes for one relation can be effects for another may be reflected on our knowledge representation scheme. The diagnosis mechanism is based on a probabilistic inferencing method. This probablistic diagnosis mechanism provides more general diagnosis than existing ones in that it accommodates multiple causes and takes complication among causes into account. These scheme and mechanism are applied to a typical example to demonstrate how our system works.