• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.210-212
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• 2007

본 논문에서는 컴퓨터 바이러스 정보 구축과 탐색에 학습기능을 도입함으로 새로 발생하는 바이러스를 찾아내어 대처할 수 있도록 설계된 퍼지 진단 시스템 FDS를 제안한다. FDS에서는 FCM 알고리즘을 사용하여 알려진 정보의 클러스터를 형성하고 이에 전문가의 지식을 포함하는 지식베이스를 구축한다. 진단을 위한 컴퓨터 파일에 대하여 그 파일의 결정 상태를 확인하고 이미 저장된 지식베이스를 바탕으로 바이러스 침입에 대한 정보를 보고하도록 설계되어있다. 이 시스템은 이미 알려진 테스트 데이터와 이전에 알려지지 않은 새로운 테스트 데이터를 실험데이터로 준비하여 그 성능을 테스트 한다. 제안된 시스템이 알려지지 않은 컴퓨터 바이러스의 경우도 효과적으로 진단할 수 있는 타당성을 보이고 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.68-71
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• 2002

의사들은 환자들의 건강 상태와 관련하여 다양한 유형의 정보들을 수집하고 분석하여 개별적인 환자들의 진단을 내리게 된다. 의사들이 한 명의 환자와 관련된 다양한 정보로부터 질환을 결정 내리기까지에는 여러 단계에서 다양한 의사결정이 필요하며 매우 복잡한 과정을 거치게 된다. 그러므로 의사들에게 또는 환자들에게 보조적인 도움을 주고자 많은 의료진단 시스템들이 개발되었다. 현재까지 개발된 대부분의 의료 진단시스템들은 특정한 의사의 경험이나 한 유형의 질환에 고정되어 있다. 그래서 환자들이 급성복통과 같은 여러 가지 유형의 질환에 관련되어 있는 증상을 호소할 때 의사들이 적절한 의사결정을 내리기가 쉽지 않다. 본 논문에서는 급성복통과 관련된 여러 가지 유형의 질환을 진단할 수 있는 시스템을 퍼지 논리를 이용하여 설계하고 구현해 본다.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.481-486
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• 2007

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. The large number of sensor nodes in a WSN means that there will often be some nodes which give erroneous sensor data owing to several reasons such as power shortage and transmission error. Generally, these sensor data are gathered by a sink node to monitor and diagnose the current environment. Therefore, this can make it difficult to get an effective monitoring and diagnosis. In this paper, to overcome the aforementioned problems, intelligent sensor data validation method based on PCA(Principle Component Analysis) is utilized. Furthermore, a practical implementation using embedded system is given to show the feasibility of the proposed scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.130-133
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• 2006

본 연구에서는 진단분야에서의 시스템의 성능을 향상시키고 최적의 해를 찾고자 사례기반추론과 인공 신경망을 혼합한 시스템을 제안한다. 사례기반추론은 과거의 사례(경험)를 통해 현재의 제시된 문제를 해결하는 추론방식으로, 지식이 획득이 덜 복잡하고, 정형화되기 어려운 규칙이나 문제영역이 불분명한 분야에 효율적으로 활용되었다. 그러나 사례의 양이 방대해야 효율적인 추론을 할 수 있으며, 검색된 시간 또한 지연되는 단점이 있다. 이러한 문제를 보완하고자 본 논문에서는 인공 신경망의 학습을 통해 저장된 ANN Library를 생성하여, 사례기반추론에서의 부적절한 해를 유추하는 것을 방지하고, 효율적이고 신뢰성이 높은 해를 유추해 내는데 목적이 있다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.73-76
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• 2005

The advanced information and communication technology gives vehicles another role of the third digital space, merging a physical space with a virtual space in a ubiquitous society. In the ubiquitous environment, the vehicle becomes a sensor node, which has a computing and communication capability in the digital space of wired and wireless network. An intelligent vehicle information system with a remote control and diagnosis is one of the future vehicle systems that we can expect in the ubiquitous environment. However, for the intelligent vehicle system, many issues such as vehicle mobility, in-vehicle communication, service platform and network convergence should be resolved. In this paper, an in-vehicle gateway is presented for an intelligent vehicle information system to make an access to heterogeneous networks. It gives an access to the server systems on the internet via CDMA-based hierarchical module architecture. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixec place, 707ms at rural area and 910ms at urban area.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.307-312
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• 2020

This paper proposed intelligent motor control system that replaced smart motor devices, such as motor protection relays, smart circuit breakers and variable speed drives, with one integrated module to perform efficient motor control at industrial sites. The proposed intelligent motor control system provides easy monitoring of critical data for each motor or load connected to an intelligent motor control system over a CIP(Common Industrial Protocol)-based network, which enables accurate process control at all times, real-time access to fault information and records to simplify diagnosis and minimize equipment downtime.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.516-518
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• 1995

This paper presents an integrated fault diagnosis expert system for power systems. The proposed system diagnoses various faults occurred in both substations and transmission lines even in the case that substation fault is spreaded over the network. To cope with this problem, A meta-inference method is proposed. This scheme shares same the data structure with the pre-developed intelligent operational aid expert system installed in a practical sub-control center, without modification. This advanced integrated diagnosis system is developed using a low cost personal computer owing to the special modular programming technique. Case studies show a promising possibility of the proposed method.