• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.94-100
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• 2010

Nowadays, several nontraditional machining(NTM) processes are widely used to machine a complex and accurate shape part of hard materials, such as titanium, ceramics, high strength temperature resistant and refractory materials which are difficult to machine and having high strength, hardness, toughness. Machining of these complex shapes in such materials by traditional machining processes are very difficult. The NTM processes is important in the areas of micro- and nano scale machining, where high accuracy and superior surface characteristics are required, which can only be achieved using these NTM processes. So, for effective selection of different NTM processes, careful decision making for a given NTM application is often necessary. An appropriate NTM process for a given material and shape condition is very difficult for the novice engineers. In this paper, an expert system based on an analytic network process(ANP) is suggested for a best selection of NTM process in a NTM application considering an prior interdependency effect among various factors.

• Journal of Biosystems Engineering
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• v.23 no.4
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• pp.391-398
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• 1998

Effective use of agricultural informations is crucial for 21 century farming. An interactive agricultural machinery information system was developed. It consisted of databases, knowledge bases, expert systems, a server engine, clients for a multimedia agricultural machinery information system based on Java and distributed objects technology. It is supposed to be served through a nationwide super information communication network for my farmers to utilize it interactively and quickly. Several user-friendly interface programs for users were developed to use texts, static k dynamic images, and expert systems easily. Tools for providing the informations were built with JAVA. Information providers permitted to access to the system such as agricultural machinery companies can easily add or modify the informations using distributed object technology. This system can interact among three groups : prime information provider (or system manager), permitted information providers, and farmers.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.6
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• pp.54-60
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• 2014

In industry, several casting process are widely used to manufacture complex and accurate blank part of hard materials such as aluminum, casting steels, bronze and magnesium alloys which are difficult to manufacture in a blank shape. Even if the casting process does not high accuracy superior surface characteristics other machining process, the casting process is widely used in manufacturing blank part. Furthermore, it is difficult to select appropriate casting process a part among several casting process. for effective selection different process, a careful decision given casting application is necessary. An appropriate casting for a given material and shape condition must be selected for novice engineers in industry. In this paper, an expert system based on an analytic network process(ANP) is suggested for best selection of casting considering a prior interdependency effect among various factors such as material, geometry, process capability, economy and equipment.

• 한국IT서비스학회:학술대회논문집
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• 2010.05a
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• pp.317-321
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• 2010

최근 몇 년 동안 웹 기반의 서비스는 웹 2.0이라는 단어로 설명되면서 집단지성, 소셜네트 워크서비스 등 다양한 형태로 발전되어 왔다. 특히 소셜네트워크서비스는 모바일 환경의 발전과 함께 수년간 계속 발전할 것으로 예상된다. 그리고 집단지성의 경우 이제는 웹기반 서비스에서 고객의 요구를 판별하고, 새로운 지식을 작성하는 방법으로 많이 활용되고 있다. 본 논문에서는 인공지능의 한 방법인 전문가시스템(Expert System)과 집단지성의 기술을 활용하여 전문지식을 관리하는 소셜네트워크서비스의 설계 및 구현의 과정을 설명하고자 한다.

• IE interfaces
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• v.8 no.3
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• pp.249-257
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• 1995

Expert system is an effective approach for quality system to be automated and thus to be an essential integrating mechanism in any move towards CIM(Computer Integrated Manufacturing). A quality control expert system is introduced and its relationship to CIM is illustrated in a case study. Process control expert system developed by Kim and Sin[6] has been improved via ODBC(Open DataBase Connectivity) for efficient information network, graph representation using Windows API for rapid response and some new rules for identification of patterns in control charts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.492-495
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• 2003

Fuzzy Min-Max Neural Network(FMMNN) is a powerful classifier, It has, however, some problems. Learning result depends on the presentation order of input data and the training parameter that limits the size of hyperbox. The latter problem affects the result seriously. In this paper, the new approach to alleviate that without loss of on-line learning ability is proposed. The committee machine is used to achieve the multi-resolution FMMNN. Each expert is a FMMNN with fixed training parameter. The advantages of small and large training parameters are used at the same time. The parameters are selected by performance and independence measures. The Decision of each expert is guided by the gating network. Therefore the regional and parametric divide and conquer scheme are used. Simulation shows that the proposed method has better classification performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.709-712
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• 1996

Generating a project network of a specific construction project is very time consuming and difficult task in the field. To effectiviely automate and support the planning process, we design a case-based project planning expert system inspired by the fact a human expert project planner uses previous cases for planning a new project. A construction project case consist of its specific characteristics and the corresponding project network (i.e. project plan). Using frame based representation. we represent the project features affecting the progress network and the entities composing the project plan such as the buildings, construction methods, WBS (work breakdown structure), activities, and resources. The project planning process runs through most similar case retrieval, case adaptation, and user requirement satisfaction. We represent the construction domain knowledge for each procedure using constraints and rules. We develop the methodology for constraint-based case adaption. Case adaptation process mainly consist of activity generation/deletion and predecence constraint satisfaction, for which we develop the dynamic constraint generation method and connect user-level requirement representation the system-level network modification knowledge. The methodology is being applied to the prototype for apartment construction project planning.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.269-272
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• 2002

In this study, we designed the expert system for the diagnosis of stroke. The causes of stroke in central nervous systems are very diverse, so a doctor who treats the patients with stroke must have the expert knowledge for the quick and correct diagnosis and for the adequate medical management. But the primary physician who engaged in the primary care of the patient with stroke does not have the export knowledge for the stroke. So, we need to develop the expert system for assisting the diagnosis of stroke. Also the diagnosis system can be used as simulator for the medical students who study the neurology. In this study, we developed the diagnosis expert system that offer a pathological name provided by artificial neural networks. And we designed the inference engine and interfaces. The artificial neural network is a system that provide a possible diagnosis of stroke. We implemented the system using Windows2000 Server, IIS5.0 and ASP.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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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.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.358-361
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• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.