• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• 대한산업공학회지
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• 제20권1호
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• pp.71-85
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• 1994

Overhead cranes which carry heavy items in construction or production areas consist of structure and electric modules. More than 80% of breakdowns bring out of the electric module. As operators do not know all about cranes, it sometimes takes much time to repair the cranes. In order to resolve this problem, the expert system which can diagnose causes of faults and give instructions for repair to operators, has been developed. The scope of the paper is limited to the electric module. First of all, analyzing symptoms and causes, we have developed a rule base with the expert system shell, EXSYS. Furthermore, for the facility maintenance including repair instructions against the causes, the instruction data base was developed with FOXPRO. On the other hand, for the help of user's understanding the fault causes, the graphic animation module which shows malfunctioning component ports or motions in 3D was developed with the graphic software, TOPAS VGA.

• 지능정보연구
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• 제3권1호
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• pp.13-30
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• 1997

인공지능 분야 중 하나의 전문시스템 기술은 현재 산업현장에서 여러 가지 분야의 문제 해결에 이용되고 있다. 본 논문은 공압설계를 자동화하기 위한 전문가시스템 PDES(Pneumatic Design Expert System)의 프로토타입 개발에 관한 것이다. 공압설계를 위한 요구 조건이 시스템의 입력정보로 제공되면 공압설계 시방과 공압회로도, 공압작업요소의 영상이 시스템의 출력으로 제시된다. PDES의 지식 베이스는 산업분야 전문가의 전문지식과 경험적 지식을 획득, 분석하여 전문가 시스템 쉘을 이용하여 구성하였으며 추론전략으로는 전향추론을 적용한 규칙기반추론(Rule-Based Reasoning)을 이용하였다. PDES는 공압설계시 설계 업무를 자동화 하여 능률을 향상시켜 줄 뿐만 아니라 공압 관련 비전문가가 공압설계를 할 때에는 큰도움을 줄 수 있다.

• 지능정보연구
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• 제2권2호
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• pp.55-68
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• 1996

인공 지능의 주요 분야 중 하나의 전문가시스템 기술은 현재 산업현장에서 여러 가지 분야의 문제해결에 이용되고 있다. 본 논문은 축 대칭 원통형 제품의 deep drawing 공정 설계를 위한 전문가시스템 DOX(Deep drawing Opertion eXpert)의 개발에 관한 것이다. 시스템의 입력은 원통형 제품의 형상 정보와 재질 정보이며 자동 인식 또는 수동입력의 방법으로 입력된다. 시스템은 이러한 정보를 받아들여 deep drawing 공정설계에 필요한 다수의 요소들을 결정하며, 최종결과로서 process layout 도면을 CAD 시스템에서 자동 출력한다. 개발된 전문가시스템의 지식베이스는 산업 분야 전문가의 전문 지식과 경험지식을 획득, 분석하여 구성하였으며, 추론 전략으로는 규칙기반추론(rule-based reasoning)을 이용하였다.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1998년도 추계학술대회 및 정기총회
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• pp.51-56
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• 1998

본 연구는 산림, 기상, 지형, 소방 정보 등을 토대로 최적의 산불 진화 전략 수립을 위한 산불 진화 전문가 시스템의 설계 방법론 제시를 주목적으로 한다. 기존의 산불 정보 시스템들은 GIS 데이터와 기상 관련 데이터, 산불 발생 지점에 대한 지형 데이터를 이용하여, 산불 확산에 따른 피해 정도 및 확산 범위에 예측을 목표로 접근하고 있다. 그러나, 이를 활용하여 최적의 진화 전략을 생성시킬 수 있는 연구는 아직까지 제시된 바가 없다. 따라서 본 연구에서는 기존의 산불 정보 시스템을 기반으로 이산 사건 모델링 및 시뮬레이션 기법, 규칙기반 SES (RUSES: Rule-based System Entity Structure), 그리고 유전 알고리즘 등을 이용하여 최적의 산분 진화 전략을 생성할 수 있는 산불 진화 전문가 시스템의 설계 방법론을 제안한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• 산업공학
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• 제6권1호
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• pp.31-45
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• 1993

Setup planning for machining processes is a part of fixture planning which is also a part of process planning. A setup of a part is defined as a group of features which are machined while the part is fixtured in one single fixture. Setup planning includes a number of tasks such as the selection of setup, sequence of setups and datum frame for each setup. Setup planning is an important function in fixture planning which must be able to support and to clamp a workpiece to prevent deflections caused by machining and clamping loads. This paper presents setup planning system using expert system approach(SPES) for prismatic parts which can be machined on vertical milling machine. SPES consists of preprocessing module and main processing module. Preprocessing module executes the conversion of feature data to frame type data and the determination of setups, and main processing module executes the determination of datum frame of each setup and sequance of setups. Preprocessing module is coded by C language and main processing module is a rule-based expert system using EXSYS pro. The performance of SPES is evaluated through case studies and the results show successful work except for operation sequence of machining holes. This is due to the limited rules for machining holes.

• 한국컴퓨터정보학회논문지
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• 제12권1호
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• pp.127-135
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• 2007

인터넷의 광범위한 보급에 따라 컴퓨터를 이용한 불법적인 범죄가 증가하고 있고, 이러한 범죄를 막기 위한 정보보호 기술자체가 국가의 경쟁력이 되어 가고 있다. 본 논문에서는 퍼지 논리를 네트워크 침입탐지시스템에 적용하여 보안 전문가와 유사한 결과를 얻을 수 있는 자동화된 퍼지 논리기반의 침입탐지시스템을 제안한다. 프로토콜의 유사성과 시간적인 연속성을 통한 세션화된 패킷분류방식을 통한 퍼지 규칙을 본 시스템에 적용함으로서 다양하고 다변적인 공격패턴으로부터 신속한 침입 판정을 내릴 수 있다. 또한, 대용량의 네트워크 트래픽을 처리해야하는 현재의 네트워크 환경에서, 퍼지추론을 통한 자동화된 트래픽의 프로토콜별/세션별 분석결과를 보여 줌으로써 보안전문가들의 분석 시간과 비용을 절감할 수 있는 장점을 제공한다.

• 전자공학회논문지C
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• 제34C권5호
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• 전자공학회논문지S
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• 제35S권2호
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.