• Proceedings of the KSME Conference
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• 2001.06a
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• pp.114-119
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• 2001

An expert system for estimation of fatigue properties from simple tensile data of material is developed, considering nearly all important estimation methods proposed so far, i.e., 7 estimation methods. The expert system is developed to utilize for the case of only hardness data available. The knowledge base is constructed with production rules and frames using an expert system shell, UNIK. Forward chaining is employed as a reasoning method. The expert system has three major functions including the function to update the knowledge base. The performance of the expert system is tested using the 54 $\varepsilon$-N curves consisting of 381 $\varepsilon$-N data points obtained for 22 materials. It is found that the expert system developed has excellent performance especially for steel materials, and reasonably good for aluminum alloys.

• Journal of the Korean Society of Safety
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• v.7 no.1
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• pp.31-38
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• 1992

This paper presents a study on application of expert system technique for the diagnosis of fire-causes in plants. A need is recognized for new methods to diagnose exactly the causes of fires without the help of the human experts. To cope with the difficulty, the expert system techiuque is applied to this area. The expert system suggested in this paper is developed to infer the causes of fires(or, ignition source ) by using the information drawn from the circumstances in fire. For the convenience of inference, ignition sources we classified into eight types ; elecoic spark, adiabatic compression, welding spark, material of high temperature, impact and friction, spontaneous ignition, naked fire, and static electricity. The knowledge base is composed of the rule base and dynamic database, which contain the rules and facts obtained by the expenence in this area, respectively. Both depth-first search and backward chaining schemes are used in reasoning process. This expert system is written in an artificial intelligence language "PROLOG", and its availability is demonstrated through the case study.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.164-168
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• 2001

This paper introduces an agent system that assists in user's work on the Internet. First, the agent receives requests from the user or other agents. Since there are various kinds of requests, it is difficult to describe a completes set of request -handling rules in advance. Therefore, the agent makes a plan referring to old cases. The agent executes the plane which is a sequence of basic operation. If the agent fails to execute basic operation or to create a plan. then it makes a new plan by interacting with the user or other agent. Finally the agent stores this new case with user's evaluation score into the case base.

• Asia pacific journal of information systems
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• v.9 no.2
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• pp.19-37
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• 1999

Computer aided process planning(CAPP) is a key for implementing CIM. It is bridge between CAD and CAM and translates the design information into manufacturing instructions. Generally, manufacturing is an area where intelligent systems will not be able to rely on methods requiring formalized knowledge. Manufacturing lacks a body of knowledge that is specific, formalized, and rigorous, and which can be coded as rules or procedures. Thus expertise in manufacturing is developed over a period of many years. Case-based reasoning(CBR) offers a new approach for developing intelligent system. In the case-based approach the problem solving experience of the experts is encoded in the form of cases. CBR's retrieval process can be divided to two step. The first step is matching step, and the second step is selection step. For selecting base case, new preference heuristics were introduced using similarity concept. Similarity concept has three has three dimensions, i.e. entity similarity, structural similarity, and goal similarity. In this paper, bolt's process planning was selected an application domain. Following the test result, the new preference heuristics were approved as a useful procedure in CAPP.

• Journal of Information Technology Application
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• v.1
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• pp.99-127
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• 1999

Under the concept of global economy, the enterprises are assigning design and production environments around the world in different areas. A serious problem of information exchange emerges as companies use traditional hardware and very distinct softwares appropriate to their field of expertise. To overcome the decreased productivity due to the interruption of information, the concept of simultaneous engineering and concurrent design becomes very significant. In this article, an agent-based ship design system is developed in order to support a cooperation in distributed ship design environments. Above all, the conflicts that occur in the middle of knowledge sharing in the system must be resolved. An approach to do this is the case-based conflict resolution strategy formulated to resolve current conflict on the basis of previous resolved similar cases in agent-based collaborative design system environments. To do this conflict cases that occur in initial ship design stage are extracted. On the basis of the extracted cases, case-base is constructed. In addition conflict resolution handler located in the facilitator is developed to treat conflict problems effectively by reasoning of the case-base and thus presenting an appropriate solution. The validation of developed case-based conflict resolution strategy is evaluated by applying to collaborative design process in initial ship design stage, especially the machinery outfitting design, the preliminary design, the hullform design, and the structural design. Through the help of the cooperation of the design agents, the facilitator, the conflict resolution handler, and the case-based system, a designer can be supported effectively in his/her decision-making based on the previous cases resolved similarly.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.433-436
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• 2002

본 논문은 인터넷상에서 전자우편물을 자동으로 생성하기 위한 에이전트에 관한 연구로서, 사례기반추론(Case-Based Reasoning:CBR) 모델을 통하여 우편물 발송자의 특성에 적응하는 에이전트의 설계 방안을 제안한다. 먼저, 지능형 에이전트와 사례기반 추론에 관하여 간략하게 조사한 후, 전자우편분석 에이전트, 색인 에이전트, 검색엔진 등으로 구성되는 다중 에이전트 시스템을 보여준다. 특히, 인공지능 기법 중의 하나인 사례기반추론의 유사도 계산 방식과 새로운 CBR 처리주기를 이용하여 전자우편물을 자동으로 생성하는 에이전트 시스템을 제안한다. 그리고 Databases와 Case-Bases를 설명하고 전자우편 자동생성 에이전트를 위한 CBR 처리주기를 제안한다. 그 다음, 에이전트와 사례 연구를 위한 프로토타입을 제공한다. 향후, Data-Mining 기법의 연구는 이 시스템이 사용자의 다양한 취향에 적응할 수 있는 유용한 시스템으로 발전하는데 도움이 될 것이다.

• ETRI Journal
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• v.39 no.4
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• pp.592-604
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• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.285-292
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• 2013

The method of recent information retrieval passes into an semantic search to provide more accurate results than keyword-based search. But in common user case, they are still accustomed to using existing keyword-based search. Hence they are hard to create a typed structured query language. In this paper, we propose to ontology knowledge-base scheme for query interpretation of these user. The proposed scheme was designed based on the OWL-DL for description logic reasoning, it can provide a richer representation of the relationship between the object by using SWRL(Semantic Web Rule Language). Finally, we are describe the experimental results of the similarity measurement for verification of a user query semantic interpretation.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.