• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2001.12a
• /
• pp.327-339
• /
• 2001

사출금형 공정계획이란 금형설계를 완료한 후에 설계된 금형을 경제적, 효율적으로 생산하기 위하여 수행해야 할 제조공정에 대한 계획이다. 이러한 공정계획은 전문가의 경험에 의존함은 물론 많은 시간이 소요된다. 그리고 사출금형 공정계획은 현장경험을 토대로 완전 수작업에 의존하고 있으므로 공정계획전문가의 경험과 숙련 등에 따른 변동, 공정설계용 데이터의 부정확 등에 의한 공정계획 그 자체가 갖고 있는 부정확도에 따라 많은 문제점이 있다. 이러한 문제점과 함께 공정계획 전문가의 부족현상, CAD/CAM시스템의 보급 및 생산형태의 다품종소량화 현상에 따라 공정계획의 자동화가 필요하게 되었다. 본 논문에서는 사출금형 공정계획을 자동화하기 위해 사례기반추론(Case Based Reasoning)을 이용하였다. 사출금형의 공정계획은 성형품의 종류에 따라 다양하고 복잡하기 때문에 지식으로서 접근하는데는 한계가 있었다 그래서 본 논문에서는 전문가들의 경험지식을 이용한 사례기반추론을 이용한 공정계획시스템인 IIMPPS(Intelligent Injection Mold Process Planning System)를 개발하였다. 사례기반추론 공정계획 시스템을 개발하기 위해 과거 공정계획을 적합한 사례로서 표현 및 구성하고, 적절한 공정계획을 수립하기 위한 사례의 검색 및 조정방법을 제안하였다. 본 시스템은 차후에 가상생산 에이전트(최형림 등, 2000) 중에서 공정계획 에이전트의 엔진으로서 역할을 수행할 것이다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.516-525
• /
• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.23 no.5
• /
• pp.942-949
• /
• 2009

We in this paper propose the method for diagnosis patients through the reasoning based on the diagnosis ontology in oriental medicine. In prior studies, it is simply diagnosed with the information of main symptoms, optional symptoms, and tongue / pulse. In addition, ontology itself has subjective opinions of oriental medical doctors for patients in form of axioms. There is a problem in latter case that it is difficult for other oriental medical doctors to change knowledge within the ontology. In order to solve these problems, we have constructed the diagnosis ontology and the reasoning algorithm as followings: First, in order to raise the diagnosis accuracy, we constructed the diagnosis ontology with pattern identifications, main symptoms, optional symptoms, and tongue / pulse. We also utilize the diagnosis points described in the pathology textbook, which has been studied in all of domestic oriental medical colleges. This information is represented as OWL instances in ontology, not OWL axioms so that it can be easily updated. Second, we suggest the algorithms for diagnosis reasoning and learning method based on the ontology. We have implemented the reasoning and learning system according to the diagnosis algorithm. In future study, we will construct the diagnosis ontology with all of pattern identifications and symptoms within the pathology textbook.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.53-69
• /
• 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
• /
• v.11 no.2
• /
• pp.175-195
• /
• 2005

사례기반추론(case-based reasoning)은 사례간 유사도를 평가하여 유사한 이웃사례를 찾아내고, 이웃사례의 결과를 이용하여 새로운 사례에 대한 예측결과를 생성하는 전통적인 인공지능기법 중 하나다. 이러한 사례기반추론이 최근 적용이 쉽고 간단하다는 장점과 모형의 갱신이 실시간으로 이루어진다는 점 등으로 인해, 온라인 환경에서의 고객관계관리를 위한 도구로 학계와 실무에서 주목을 받고 있다 하지만, 전통적인 사례기반추론의 경우, 타 인공지능기법에 비해 정확도가 상대적으로 크게 떨어진다는 점이 종종 문제점으로 제기되어 왔다. 이에, 본 연구에서는 사례기반추론의 성과를 획기적으로 개선하기 위한 방법으로 유전자 알고리즘을 활용한 사례기반추론의 동시 최적화 모형을 제안하고자 한다. 본 연구가 제안하는 모형에서는 기존 연구에서 사례기반추론의 성과에 중대한 영향을 미치는 요소들로 제시된 바 있는 사례 특징변수의 상대적 가중치 선정(feature weighting)과 참조사례 선정(instance selection)을 유전자 알고리즘을 이용해 최적화함으로서, 사례간 유사도를 보다 정밀하게 도출하는 동시에 추론의 결과를 왜곡할 수 있는 오류사례의 영향을 최소화하고자 하였다. 제안모형의 유용성을 검증하기 위해, 본 연구에서는 국내 한 전문 인터넷 쇼핑몰의 구매예측모형 구축사례에 제안모형을 적용하여 그 성과를 살펴보았다. 그 결과, 제안모형이 지금까지 기존 연구에서 제안된 다른 사례기반추론 개선모형들은 물론, 로지스틱 회귀분석(LOGIT), 다중판별분석(MDA), 인공신경망(ANN), SVM 등 다른 인공지능 기법들에 비해서도 상대적으로 우수한 성과를 도출할 수 있음을 확인할 수 있었다.

• Journal of Information Technology Applications and Management
• /
• v.10 no.4
• /
• pp.119-133
• /
• 2003

맞춤형 금융상품 설계시스템(Financial Product Factory System)이란 온라인으로 접근하는 고객의 요구사항을 고려하여 고객에게 가장 적합한 금융상품을 실시간으로 설계하여 제공하는 시스템이다. 최근 들어 인터넷 뱅킹 고객의 수가 급증함에 따라 맞춤형 금융상품 설계시스템의 필요성이 대두되고 있으나, 이러한 시스템의 정의나 성격, 필요 기능, 구축 방안에 대한 연구가 되어 있지 않은 실정이다. 본 연구에서는 맞춤형 금융상품 설계시스템의 정의를 내리고, 이 시스템이 갖추어야 할 요구사항을 시스템과 서비스 측면에서 분석한 후, 이 요구사항을 반영하는 시스템의 아키텍처를 제안ㆍ구현한다

• Journal of Korean Association for Spatial Structures
• /
• v.5 no.3 s.17
• /
• pp.75-82
• /
• 2005

The conceptual structural design consists of selecting structural material and form of the building, producing a preliminary dimensional layout. The information such as height of the building use, typical live load, wind velocity, design acceleration, maximum lateral deflection, span, story height is a important factor in conceptual design phase. In this case, the knowledge solutions for past similar problems cam be used in the process of defining and finding a solution to the design problems. In this paper, the conceptual structural design method using case-based reasoning which is intended to assist engineers in the conceptual phase of the structural design of tall buildings is introduced. Inductive retrieval method and nearest-neighbor retrieval method are used for selecting structural system and similar design case, respectively.

• Journal of Advanced Navigation Technology
• /
• v.14 no.1
• /
• pp.108-118
• /
• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.65-80
• /
• 1999

선박설계는 그 과정이 매우 복잡하고 많은 양의 데이터를 다루고 있는 작업으로서 그 환경이 분산화, 이질화 됨에따라 최근들어 CSCW(Computer Supported Collaborative Work)의 요구가 증대되고 있다. 본 논문에서는 분산된 선박설계 환경에서의 협동작업을 지원하기 위한 에이전트 기반 선박설계 시스템을 개발하였다. 특히 여기서는 설계 에이전트간의 정보교환 및 지식공유를 통한 선박설계의 의사결정 과정에서 발생하는 의사충돌 문제를 해결하기 위하여 사례기반 추론 기법을 이용하여 이의 해결을 시도하였다. 설계 에이전트, 이들을 중재하는 퍼실리테이터, 충돌 처리기, 그리고 사례기반 시스템의 유기적인 도움을 받아 설계자는 설계과정에서 발생하는 설계 시스템간의 의사충돌 문제에 대한 의사결정을 과거의 유사한 문제해결 사례로부터 효과적으로 처리할 수 있다.