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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 8, Issue 6 - Nov 1998
Volume 8, Issue 5 - Oct 1998
Volume 8, Issue 4 - Aug 1998
Volume 8, Issue 3 - Jun 1998
Volume 8, Issue 2 - Apr 1998
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Logic Processor Modeling of a Steam Generator in Nuclear Power Plant
Chun, Myung-Geun ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 1~11
In this work, we propose a modeling method based on an artifical intelligence technique for a stem generator in a nuclear power plant. Modeling the steam generator is known to be difficult due to several facts; especially, the dynamics of the steam generator is nonminimum phase which is mainly caused by the swell and shrink phenomena from thermal effects. In order to overcome this difficulty, we adopt so-called logic processor whose structure itself has a logical meaning to be easily established and also efficiently learned. Such a manner, we could derive an useful model simulating the dynamics of the steam generator in a nuclear power plant.
On Designing a Robust Control System Using Immune Algorithm
Seo, Jae-Yong ; Won, Kyoung-Jae ; Kim, Seong-Hyun ; Cho, Hyun-Chan ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 12~20
As an approach to develope a control system with high robustness in changing control environment conditions, this paper will propose a robust control system, using multilayer neural network and biological immune system. The proposed control system adjusts weights of the multilayer neural network(MNN) with the immune algorithm. This algorithm is made up of two major divisions, the innate immune algorithm as a first line of defence and the adaptive immune algorithm as a barrier of self-adjustment. Using the proposed control system based on immune algorithm, we will work out a design for the controller of a robot manipulator. And we will demonstrate the effectiveness of the control system of robot manipulator with computer simulations.
Some Characterizations of TL-subgroups
Kim, Han-Doo ; Kim, Dong-Seog ; Kim, Jae-Gyeom ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 21~26
In this paper, we show that if a TL-subgroup can be written as the intersection of all its minimal TL-p-subgroups then some properties of the TL-subgroups characterize the properties of all its minimal TL-p-subgroups and investigate the properties of the join of a directed family of TL-subgroups.
Integrated Method Based on Rough Sets for Knowledge Discovery
Chung, Hong ; Chung, Hwan-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 27~36
This paper suggests an integrated method based on rough sets for discovering useful knowledge from a large databse. Our approach applies attribute-oriented concept hierarchy ascension technique to extract generalized data from actual data in database, induction of decision trees to measure the information gain, and knowledge reduction method of rough set theory to remove superfluous attributes and attribute values. The integrated algorithm first reduces the size of database through the concept generalization, reduces the number of attributes by means of eliminating condition attributes which have little influence on decision attribute, and finally induces simplified decision rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes.
Design of Controller for Rapid Thermal Process Using Evolutionary Computation Algorithm and Fuzzy Logic
Hwang, Min-Woong ; Do, Hyun-Min ; Choi, Jin-Young ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 37~47
This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.
A Note on Fuzzy S-mappings
Min, Won-Keun ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 48~52
We introduce the concepts of fuzzy s-continuous mappings, s-open mappings, and s-closed mappings. We investigate several properties of such mappings. In particular, we study the relation between fuzzy s-continuous mappings and fuzzy s-open mappings(s-closed mappings).
A Note on Fuzzy Strong Continuities and Fuzzy Midly Normal Spaces
Heo, Geol ; Mun, Ju-Ran ; Ryou, Jang-Hyun ; Yu, Mi-Jeong ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 53~57
We study some properties of fuzzy strong continuities. And we introduce the concept of fuzzy midly normal space and investigate its properties.
A Neuro-Fuzzy Approach to Integration and Control of Industrial Processes:Part I
Kim, Sung-Shin ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 58~69
This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.
A Causal Knowledge-Driven Inference Engine for Expert System
Lee, Kun-Chang ; Kim, Hyun-Soo ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 70~77
Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven inference engine named CAKES and then experimented with some illustrative examples.
Traffic Signal Control with Fuzzy Membership Functions Generated by Genetic Algorithms
Kim, Jong-Wan ; Kim, Byeong-Man ; Kim, Ju-Youn ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 78~84
In this paper, a fuzzy traffic controller using genetic algorithms is presented. Conventional fuzzy traffic controllers use membership functions generated by humans. However, this approach does not guarantee the optimal solution to design the fuzzy controller. Genetic algorithm is a good problem solving method requiring domain-specific knowledge that is often heuristic. To find fuzzy membership functions showing good performance, a fitness function must be defined. However it's not easy in traffic control to define such a function as a numeric expression. Thus, we use simulation approach, namely, the fitness value of a solution is determined by use of a performance measure that is obtained by traffic simulator. The proposed method outperforms the conventional fuzzy controllers.
A Stereo Matching Algorithm with Image Fuzzification
Chung, Young-June ; Jun, Hyo-Byung ; Sim, Kwee-Bo ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 85~90
The most important step image processing is stereo matching process. That is finding pixels of 3 dimensional pair in the left and right image. There are two matching methods. One is an area based approach and the other is a feature based approach. An area based approach needs much calculation time. In the other hand, we have the advantage of calculation time in the feature based approach, but can not obtain matched data for all pixels in the image. In recent years, fuzzy image processing methods are developed to manage vagueness and noise in image and ambiguous, inconsistent knowledge in recognition step. In this paper, we propose a fuzzy stereo matching algorithm. This method converts brightness data of image to fuzzy membership value and processes an area based approach method for stereo matching algorithm. We experiment with some stereo images to validate effectiveness of this algorithm.
Real Time Modeling of Discrete Event Systems and Its Application
Jeong, Yong-Man ; Hwang, Hyung-Soo ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 91~98
A DEDS is a system whose stated change in response to the occurrence of events from a predefined event set. A major difficulty in developing analytical results for the system is the lack of appropriate modeling techniques. In this paper, we consider the modeling and control problem for Discrete Event Dynamic Systems(DEDS) in the Temporal Logic framework(TLF) which have been recently defined. The traditional TLF is enhanced with time functions for real time control of Discrete Event Dynamic Systems. A sequence of event which drive the system from a given initial state to a given final state is generated by pertinently operating the given plants. This paper proposes the use of Real-time Temporal Logic as a modeling tool for the analysis and control of DEDS. An given example of fixed-time traffic control problem is shown to illustrate our results with Real-time Temporal Logic Framework.
Fuzzy Nonlinear Regression Model
Hwang, Seung-Gook ; Park, Young-Man ; Seo, Yoo-Jin ; Park, Kwang-Pak ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 99~105
This paper is to propose the fuzzy regression model using genetic algorithm which is fuzzy nonlinear regression model. Genetic algorithm is used to classify the input data for better fuzzy regression analysis. From this partition. each data can be have the grade of membership function which is belonged to a divided data group. The data group, from optimal partition of the region of each variable, have different fuzzy parameters of fuzzy linear regression model one another. We compound the fuzzy output of each data group so as to obtain the final fuzzy number for a data. We show the efficiency of this method by means of demonstration of a case study.
Fuzzy Identification by Means of an Auto-Tuning Algorithm and a Weighted Performance Index
Oh, Sung-Kwun ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 106~118
The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.
Fuzzy Similarity Measure
Lee, Kwang-Hyung ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 119~121
For a fuzzy system modeled by a fuzzy hypergraph, two fuzzy similarity measures are proposed:one for the fuzzy similarity between fuzzy sets and the other between elements in fuzzy sets. The proposed measures can represent the realistic similarities which can not be given by the existing measures. With an example, it is shown that it can be used in the system analysis.
Image Sequence Compression based on Adaptive Classification of Interframe Difference Image Blocks
Ahn, Chul-Joon ; Kong, Seong-Gon ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 122~128
This paper presents compression of image sequences based on the classification of interframe difference image blocks. classification process consists of image activity classification and energy distribution classification. In the activity classification, interframe difference image blocks are classified into activity blocks and non-activity blocks using the edge detection. In the distribution classification, activity blocks are further classified into vertical blocks, horizontal blocks, and small activity blocks using the AC energy distribution features. The RBFN, trained with numerical classification results, successfully classifies difference image blocks according to image details. Image sequence compressing based on the classification of interframe difference image blocks using the RBFN shows better compression results and less training time than the classical sorting method and the MLP network.
Isolated Digit Recognition Combined with Recurrent Neural Prediction Models and Chaotic Neural Networks
Kim, Seok-Hyun ; Ryeo, Ji-Hwan ;
Journal of Korean Institute of Intelligent Systems, volume 8, issue 6, 1998, Pages 129~135
In this paper, the recognition rate of isolated digits has been improved using the multiple neural networks combined with chaotic recurrent neural networks and MLP. Generally, the recognition rate has been increased from 1.2% to 2.5%. The experiments tell that the recognition rate is increased because MLP and CRNN(chaotic recurrent neural network) compensate for each other. Besides this, the chaotic dynamic properties have helped more in speech recognition. The best recognition rate is when the algorithm combined with MLP and chaotic multiple recurrent neural network has been used. However, in the respect of simple algorithm and reliability, the multiple neural networks combined with MLP and chaotic single recurrent neural networks have better properties. Largely, MLP has very good recognition rate in korean digits "il", "oh", while the chaotic recurrent neural network has best recognition in "young", "sam", "chil".