• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.83-88
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• 2002

최근 들어 무선인터넷 및 모바일 컴퓨팅 기술의 급속한 발전과 함께 향후 그 수요가 증대될 것으로 예상되는 분야가 위치기반 서비스(LBS: Location Based Service) 기술이다. 위치기반 서비스는 이동 통신을 통하여 사람 및 사물의 위치를 파악하고 이를 활용한 부가 응용 서비스로 국가 정보기술 인프라의 주요 영역을 점유하고 있는 GIS의 차세대 핵심 기술로 발전이 예상되는 분야이다[3][4]. 현재 3GPP나 3GPP2, OGC, LIF와 같은 여러 표준화 기구 및 산업체에서 위치기반 서비스와 이를 위한 시스템에 대한 연구가 진행중이며 위치기반 서비스를 위한 데이터 모델 표준화 연구는 거의 이루어지지 않고 있는 상황이다. 위치기반 서비스를 위한 데이터 표준화 모델은 이미 구축된 공간 데이터베이스의 재사용과 위치기반 서비스들간의 상호 운용성을 지원할 수 있어야 한다. 본 연구에서는 위치기반 서비스들 간의 상호 호환 및 통합을 가능하게 하고, 기존 공간데이터베이스와 연계하여 이 데이터를 위치기반 서비스에 활용하기 위한 공간 데이터 표준화 모델을 제안하고자 한다. 이를 위해 위치기반 서비스 표준화 사례를 조사하고, 위치기반 서비스를 위한 공간 데이터 모델을 제시하였다. 본 연구에서는 OpenLS의 위치기반 서비스를 기본서비스로 하고, OpenGIS의 공간 데이터 모델을 기반으로 네 가지 기본 위치 데이터 타입과 모델의 요구 사항을 포함하는 공간 데이터 표준모델을 개발하였다. 위치기반 공간 데이터 표준 모델은 위치기반 서비스와 데이터들과의 연계를 쉽게 하고, 위치기반 서비스들 간의 상호 운용성을 높이며, 기존 사용자 시스템의 수정 없이 인터페이스만을 추가함으로써 표준을 수용할 수 있다.\pm}153.2,\;116.1{\pm}94,\;29.4{\pm}30.3,\;45.1{\pm}44$로 Mel 10군과 Mel 30군이 유의적인 감소를 보였으나(p<0.05) 이들 두 군 간의 차이는 나타나지 않았다. 이상의 결과로, 랫트에서 복강수술 후 melatonin 10mg/kg투여가 복강 내 유착 방지에 효과적이라고 생각된다.-1}{\cdot}yr^{-1}$로서 두 생태계에 축적되었다.여한 3,5,7군에서 PUFA 함량이 증가한 반면, SFA 함량은 감소하여 P/S 비율, n-3P/n-6P 비율은 증가하는 경향이었으며 이는 간장의 인지질, 콜레스테롤 에스테르, 총 지질의 지방산조성에서도 같은 경향을 볼 수 있었다.X>$(C_{18:2})$와 n-3계 linolenic acid$(C_{18:3})$가 대부분을 차지하였다. 야생 돌복숭아 과육 중의 지방산 조성은 포화지방산이 16.74%, 단불포화지방산 17.51% 및 다불포화지방산이 65.73%의 함유 비율을 보였는데, 이 중 다불포화지방산인 n-6계 linoleic acid$(C_{18:2})$와 n-3계 linolenic acid$(C_{18:3})$가 지질 구성 총 지방산의 대부분을 차지하는 함유 비율을 나타내었다.했다. 하강하는 약 4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.53-69
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• 2003

Many process variables in modem manufacturing processes have influence on quality of products with complicated relationships. Therefore, it is necessary to control multiple quality variables in order to monitor abnormal signals in the processes. This study proposes an integrated procedure of self-organizing map (SOM) neural network and case-based reasoning (CBR) for multivariate process control. SOM generates patterns of quality variables. The patterns are compared with the reference patterns in order to decide whether their states are normal or abnormal using the goodness-of-fitness test. For validation, it generates artificial datasets consisting of six patterns, normal and abnormal patterns. Experimental results show that the abnormal patterns can be detected effectively. This study also shows that the CBR procedure enables to keep Type 2 error at very low level and reduce Type 1 error gradually, and then the proposed method can be a solution fur multivariate process control.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• 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.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.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.129-138
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• 1999

The aim of this research is the development of an automated fixture planning system for prismatic parts using the case-based reasoning (CBR). CBR is the problem solving paradigm that uses the similarity between a new problem and old cases to solve the new problem. This research uses CBR for the fixture planning. A case is composed with the information of the part, the components of fixture and the method of fixing for the part. The basic procedure is the retrieval and adaptation for the case, and this research presents the method of retrieval that selects most similar case to the new situation. The retrieval-step is divided into an index matching and an aggregated matching. The adaptation is accomplished by the modification, which transforms the selected case to the solution of the situation of the input part by the specified CBR algorithm. The components of fixture and the method of fixing are determined for a new part by the procedure.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.285-288
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• 2006

Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build the prediction model for the customer profitability level. Experimental results show that our GA-optimized CBR approach outperforms other AI techniques for this mulriclass classification problem.

• Annual Conference of KIPS
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• 2017.11a
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• pp.310-313
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• 2017

범죄수사 과정에서 많은 양의 데이터를 시간 내에 분석하는 것은 성공적인 포렌식의 필수 요소이다. 컴퓨터와 사람 모두에게 있어, 시간과 자원의 제한은 수사 결과에 부정적인 영향을 가져온다. 그러므로 현재 사용되고 있는 다양한 포렌식 도구에는 시간과 자원의 효율적인 사용이 필요하다. 사례기반추론 및 멀티에이전트 시스템과 같은 인공지능 기반의 도구를 통해 디지털 포렌식 수사를 효과적으로 도울 수 있다. 본 논문에서는 인공지능을 활용한 지능형 포렌식 도구 및 프레임워크를 분석하고, 오늘날의 프레임워크의 한계점과 미래에 관해 논의한다. 인공지능 기반 시스템의 목적은 수사에서의 증거를 포함한 데이터를 분석하고 연관성을 밝힘으로서 포렌식 전문가에게 중요한 단서를 제공하고 직접 분석해야하는 데이터의 양을 줄이는 것에 있다. 이러한 인공지능의 활용은 많은 양의 데이터를 수사할 때 사람이 간과할 수 있는 증거들을 연결시켜주는 데에 큰 도움이 된다.