• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.223-231
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• 2006

In high-speed fuzzy control systems applied to intelligent systems such as robot control, one of the most important problems is the improvement of the execution speed of the fuzzy inference. In particular, it is more important to have high-speed operations in the consequent part and the defuzzification stage. To improve the speedup of fuzzy controllers for intelligent systems, this paper presents an integer line mapping algorithm to convert [0, 1] real values of the fuzzy membership functions in the consequent part to a $400{\times}30$ grid of integer values. In addition, this paper presents a method of eliminating the unnecessary operations of the zero items in the defuzzification stage. With this representation, a center of gravity method can be implemented with only integer additions and one integer division. The proposed system is analyzed in the air conditioner control system for execution speed and COG, and applied to the truck backer-upper control system. The proposed system shows a significant increase in speed as compared with conventional methods with minimal error; simulations indicate a speedup of an order of magnitude. This system can be applied to real-time high-speed intelligent systems such as robot arm control.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.77-86
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• 1995

The new architecture that fuzzy logic control(FLC) with difficulties for tuning membership function (MF) is parallel with neural networks(NN) to be learned from the output of FLC is proposed. Therefore proposed scheme has the characteristics to utilize the expert knowledge in design process, to be learned during the operation without any learning mode. In this architecture, the function of the FLC is to supply the sliding surface which is constructed on the phase plane by rule base for giving the desired control characteristics and learning criterion of NN and the stabilization of the control performance before NN is learned, The function of the NN is to let the system trajectory be tracked to the sliding surface and reached to the stable point.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.239-248
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• 2014

Most systems used in industrial sites, actually have non-linearity and uncertainty. Therefore there are a lot of difficulties in evaluating conditions of these systems. Generally, the quantitative analysis and expression are found hard because the general public cannot easily make an accurate interpretation on the systems. Thus development of a system that utilizes an expertise from skilled analysts is required. In this research, a real-time sensor signal conditioning system and Fuzzy-expert system have been separately set up into an inference algorithm. So that it ensures a fast, accurate, objective and quantitative operational condition value provided to the manager. Therefore, FE_AFCDM is suggested in this literature, as an effective system for diagnosing the problems related to the air compressor. It can quantify the uncertain and absurd condition to operate the air compressor facilities safely and financially.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.27-42
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• 1989

REA (Restricted Entity Aspect) model is a knowledge representation model to classify the aspect type, the EA model component, into five aspects (IS-A-aspect, A-PART-OF aspect, attribute aspect, role aspect, and operation aspect). EATPS, the knowledge representation system, consists of user interface module, knowledge creation module, instance management module, schema management module, and integrity checking module. EATPS creates and manages interactively REA model based knowledge base. This paper shows the structure and functions of EATPS, the design and interactive construction of the knowledge definition language EAKDL, the functions and algorithm of class creation module, and the functions and algorithm of instance creation module to include inheritance inference mechanism.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.138-147
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• 1993

It is known that the performance of a fuzzy controller is related with fuzzification method, inference rules, defuzzification method, and linguistic variables. Among these, generally, the linguistic variables and control rules are transfered to control engineers from an expert or experts of the controlled system and other parts are designed by control engineers. However, there may be some missed infirmations or uncertainties in the transfered data. The purpose of the paper is to propose an algorithm to tune the membership functions of initially given fuzzy sets To do so, a simple shape of the membership fuction is assumed for the fuzzy sets, and the relations between the shapes of the fuzzy sets and the performance of the control system is derived. According to the relations, the shape of the membership functions are modified during operation of the whole system. The proposed algorithm will be applied to two emample plants, type 1 and type 0 systems.

• The Journal of Information Technology
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• v.5 no.4
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• pp.57-64
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• 2002

The system that is thermal test system for elements has been controlled generally by PID algorithm because of its characteristic. There is not a mathematical model for the system. So the system that is use the PID controller is not properly operated. To solve this problem, we propose a fuzzy algorithm that parameters and rule base is selected by self-searched algorithm for each system. The input fuzzy membership function is adapted based on the set stable range. Output membership function is nearly fixed but some parameter is adjustable. The rule base is changed under basis on the system response. The output value computed through inference and defuzzification is mapped into a value that is proper for the system operation. Through this regulation, it will be possible to prevent the temperature of system to go into the unstable temperature.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2720-2722
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• 2001

기업 네트워크에서 안전하고 효과적이며 안정된 망 운영관리 환경 제공은 당면한 중요과제이며 기업 경쟁력의 핵심인 정보기술을 통한 생산성과도 연계성을 갖고 있다. 이러한 필요에 맞추어 많은 네트워크 관리 시스템들이 개발되어 상용화 되었으나 이들 관리시스템들은 트래픽 모니터링에 의한 통계값 제공과 같은 단순 평면적인 관리 기능만을 제공할 뿐 네트워크의 특성과 환경에 따른 분석, 진단 기능은 제공하지 못하고 있다. 또한 현재의 네트워크 환경은 다양한 통신장비와 서비스들의 개발에 따른 망 구성 요소의 이질성과 복잡성이 증가하고 있으며 이는 전문적인 네트워크 운영관리와 분석기술을 요구하고 있다. 이는 관리자에게 네트워크가 점점 다양화, 복잡화 되는 환경에서 부담으로 작용할 뿐 아니라 결과를 도출하는데 많은 시간과 비용이 소요된다. 따라서 기존 네트워크 관리 시스템의 한계를 극복하고 네트워크 환경 변화를 수용, 관리자의 운영경험지식을 데이터베이스화하여 지능적인 네트워크의 분석과 진단이 가능한 시스템의 개발이 필요하다. 본 논문은 이러한 시스템 구축의 전제조건으로 네트워크 운영경험 데이터베이스를 구축하여 이를 통한 네트워크 환경 변화를 수용할 수 있는 임계값 최적화와 향후 한국전력 사내 데이터통신망의 지능화된 분석과 진단을 위해 활용하게 될 지식 발견 시스템을 설계 및 구현하였다.

• Cross-Cultural Studies
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• v.46
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• pp.297-325
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• 2017

In this paper, I would like to reconsider the evidential category (or the mediation category) in languages with language specific values, especially in Korean and French evidentials. We tried to analyze how the evidentials are represented in both languages including their linguistic markers (grammatical, lexical or discursive) and their semantic meanings. According to the precedent studies from the general linguistic point of view, we would like to reconsider the semantic meanings of both languages' grammatical markers, the so-called Korean retrospective marker '-te-' and French conditionals in the framework of the enunciative operation theory suggested by $Descl{\acute{e}}s$ & $Guentch{\acute{e}}va$ (2000), which proposed to classify the type of discourse by the language-independent description tools conceived after the enunciation theory suggested by Bally (1965), Benveniste (1956), Culioli (1973). Through this approach, we would like to contribute to establishing the linguistic basis not only for the general linguistic research to determine the invariant meaning of linguistic evidentials and their system, but also for the applied linguistics to the language engineering field.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.491-499
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• 2017

Railway turnout systems are one of the most important systems in a railway and abnormal turnout systems can cause serious accidents. To detect an abnormal state of a turnout, turnout clearance detectors are widely used. These devices consider a failure of a turnout clearance detectors to be a failure of the turnout system, that could hinder train operations. Analysis of turnout clearance detector failures is very important to ensure normal train operation. We categorized failures of detectors into four groups to identify failure characteristics of the 140 detectors, which are composed of main line detectors (A), side tracks (B), detectors that are in operation more than 80 times a day (C) and detectors that are in operation fewer than 10 times per day. Failures of detectors have mainly been caused in the control part, in the cables and sensors; failures are classified into four groups (A, B, C and D). We have tried to find failure density distributions for each type of failures, inferring the parameter distributions a priori. Finally, using the Bayesian inference we proposed a maintenance time for control parts through the mean time of the detector, life and the life expectancy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.