• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.73-74
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• 2006

Design repository is considered an effective method to manage a set of heterogeneous design knowledge. In this paper, methodologies for modeling and managing different types of design knowledge - ontology for mold design task as well as mold components, rule bases, and library containing standard parts, material property, molding condition, etc. - in a design repository are described.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.362-366
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• 2007

최근 기업 경영활동의 의사결정시 사용되는 비즈니스 룰(Business Rule)을 정형화하고 하나의 시스템으로 구축하여 효과적으로 기업의 경쟁력을 제고하기 위한 노력이 다양하게 시도되고 있다. 비즈니스 룰 시스템 구축 작업의 경우 기업 내부에 비정형적으로 존재하는 비즈니스 룰을 체계적으로 관리하기 위해 BRMS(Business Rule Management System)와 같은 전문 관리 도구를 도입하나, 대부분의 경우 비즈니스 룰 리포지토리(Repository)를 단순히 기능별 혹은 업무별로 구성함으로 인해 동일한 내용의 룰이 서로 다른 룰 리포지토리에 중복 존재하게 되는 등 구조상의 문제점을 발생시킨다. 이로 인해 각 어플리케이션 간의 룰 또는 룰 세트(Rule Set) 공유 관계가 수동 관리되거나 중복 룰의 수정으로 인한 룰 세트별 버전 관리 문제 등 비즈니스 룰 리포지토리 운영의 어려움에 봉착하게 된다. 본 연구에서는 금융보험사의 룰웨어하우스 구축 사례를 통해 다양한 어플리케이션에서 참조되는 전사 수준의 비즈니스 룰 관리 아키텍처 구성 방법 및 각 방법이 지닌 장단점에 대해 분석한다. 본 연구의 결과를 토대로 다양한 어플리케이션에서 참조되고 수시로 변경되는 전사 수준의 통합 비즈니스 룰 관리 시스템 구축 방안에 대한 연구가 활성화되기를 기대한다.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.11-17
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• 2007

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it cannot explain how the classification result is obtained. In order to overcome this problem, we propose an incremental teaming algorithm based on RPA (Recursive Partition Averaging) to extract IF-THEN rules that describe regularities inherent in training patterns. But rules generated by RPA eventually show an overfitting phenomenon, because they depend too strongly on the details of given training patterns. Also RPA produces more number of rules than necessary, due to over-partitioning of the pattern space. Consequently, we present the IREA (Incremental Rule Extraction Algorithm) that overcomes overfitting problem by removing useless conditions from rules and reduces the number of rules at the same time. We verify the performance of proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.113-130
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• 2000

As software projects become larger and more complex, we need to take a more systematic approach to quality assurance. One plausible approach is the use of SQA (software quality assurance) methodology. Since this SQA methodology was not available, our study presents a SQA methodology. This methodology has a repository in which a set of quality assurance tasks with their related techniques and deliverables is defined and from which one can select only appropriate tasks based upon characteristics of project. This study further suggests a rule-based approach for supporting task selection process.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.223-234
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• 2011

Loosely coupled properties of SOA(Service Oriented Architecture) services do not guarantee that service applications always work properly. Service errors may also influence other services of SOA. These characteristics adversely affect software reliability. Therefore, it is a challenge to effectively manage system change and errors for operating services normally. In this study, we propose a SOA based framework using AOP(Aspect Oriented Programming) for reliable service applications. AOP provides a way to manipulate cross-cutting concerns such as logging, security and reliability and these concerns can be added to applications through weaving process. We define a service specification and an aspect specification for this framework. This framework also includes service provider, requester, repository, platform, manager, and aspect weaver to handle changes and exceptions of applications. Independent Exception Handler is stored to exhibited external Aspect Service Repository. When exception happened, Exception Handler is linked dynamically according to aspect rule that is defined in aspect specification and offer function that handle exception alternate suitable service in systematic error situation. By separating cross-cutting concerns independently, we expect that developer can concentrate on core service implementation and reusability, understanding, maintainability increase. Finally, we have implemented a prototype system to demonstrate the feasibility of our framework in case study.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.125-132
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• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.480-483
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• 2003

We propose a new framework that aims at multi-purpose image recognition, a difficult task for the conventional rule-based systems. This framework is farmed based on the idea of computer-based learning algorithm. In this research, we introduce the new functions of an additional learning and a knowledge reconstruction on the Fuzzy Inference Neural Network (FINN) (1) to enable the system to accommodate new objects and enhance the accuracy as necessary. We examine the capability of the proposed framework using two examples. The first one is the capital letter recognition task from UCI machine learning repository to estimate the effectiveness of the framework itself, Even though the whole training data was not given in advance, the proposed framework operated with a small loss of accuracy by introducing functions of the additional learning and the knowledge reconstruction. The other is the scenery image recognition. We confirmed that the proposed framework could recognize images with high accuracy and accommodate new object recursively.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.79-100
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• 1993

This paper proposes an idea of reverse modeling that analyzes LP models and then converts them into an object-oriented model repository. The process of reverse modeling consists of (1) identifying and analyzing source models by meta processor, (2) model decomposition and generalization to scan the models and divide them into model components, and (3) deriving model selection rules from the components by rule generator. Through the process, we can extract reusable model components and build a model base with model selectioon rules. Examples with models created by SML and MODLER modeling languages are given to illustrate the methods. The model base management capabilities provided by reverse modeling can increase the reusabioity of current modeling tools.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.149-156
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• 2014

NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.