• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.174-182
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• 1999

This paper describes a research work of developing a computer-aided design of product with bending constraints and piercing for progressive working. an approach to the CAD system is based on the knowledge-based rules. Knowledge for the CAD system is formulated from plasticity theories, experimental results and the empirical knowledge of field experts. The system has been written AutoLISP on the AutoCAD with a personal computer and is composed of four main modules, which are input and shape treatment, flat pattern layout, production feasibility check, and strip-layout module. Based on knowledge-based rules, the system is designed by considering several factors, such as radius and angle of bend, material and thickness of product, complexities of blank geometry and punch profile, bending sequence, availability of press. Strip layout drawing generated by piercing with punch profiles divide into automatically for external area is shown into graphic forms, including bending sequences for the product with piercing and bending constraints. Results obtained using the modules enable the designer and manufacturer of piercing and bending dies to be more efficient in this field.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.688-710
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• 2016

Our approach permits to capitalize the expert's knowledge as business rules by using an agent-based platform. The objective of our approach is to allow experts to manage the daily evolutions of business domains without having to use a technician, and to allow them to be implied, and to participate in the development of the application to accomplish the daily tasks of their work. Therefore, the manipulation of an expert's knowledge generates the need for information security and other associated technologies. The notion of cryptography has emerged as a basic concept in business rules modeling. The purpose of this paper is to present a cryptographic algorithm based approach to integrate the security aspect in business rules modeling. We propose integrating an agent-based approach in the framework. This solution utilizes a security agent with domain ontology. This agent applies an encryption/decryption algorithm to allow for the confidentiality, authenticity, and integrity of the most important rules. To increase the security of these rules, we used hybrid cryptography in order to take advantage of symmetric and asymmetric algorithms. We performed some experiments to find the best encryption algorithm, which provides improvement in terms of response time, space memory, and security.

• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.55-61
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• 2005

We will introduce a new web-based knowledge management system in progress, in which XML-based web information extraction and our structuring knowledge technologies are combined using ontology-based natural language processing. Our aim is to provide efficient access to heterogeneous information on the web, enabling users to use a wide range of textual and non textual resources, such as newspapers and databases, effortlessly to accelerate knowledge acquisition from such knowledge sources. In order to achieve the efficient knowledge management, we propose at first an XML-based Web information extraction which contains a sophisticated control language to extract data from Web pages. With using standard XML Technologies in the system, our approach can make extracting information easy because of a) detaching rules from processing, b) restricting target for processing, c) Interactive operations for developing extracting rules. Then we propose a structuring knowledge system which includes, 1) automatic term recognition, 2) domain oriented automatic term clustering, 3) similarity-based document retrieval, 4) real-time document clustering, and 5) visualization. The system supports integrating different types of databases (textual and non textual) and retrieving different types of information simultaneously. Through further explanation to the specification and the implementation technique of the system, we will demonstrate how the system can accelerate knowledge acquisition on the Web even for novice users of the field.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.720-728
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• 1990

A learning control method is proposed in this paper, using a knowledge base which contains control rules, data, and patterns of the past experience of a plant. The knowledge for plant control is retrieved from measurement data during operation and continually modified after control performance evaluation. A control method is proposed using tinually modified after control performance evaluation. A control method is proposed using fuzzy model of the plant and a recursive statistic decision method of fuzzy subset for control rule generation. Also, the resulting knowledge-based control algorithm has been applied to aprocess and its performance improvement and proper generation of appropriate control rules have been verified.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.232-241
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• 2000

This study suggests an integrated method based on concept hierarchies, information gain, and rough set theory for efficient discovery rules from a large amount of data, and implements an optimal rules discovery system. Our approach applies attribute-oriented concept ascension technique to extract generalized knowledge from a database, knowledge reduction technique to remove superfluous attributes and attribute values, and significance of attributes to induce optimal rules. The system first reduces the size of database by removing the duplicate tuples through the condition attributes which have no influences on the decision attributes, and finally induces simplified optimal rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes. And we induce some decision rules from actual data by using the system and test rules to new data, and evaluate that the rules are well suited to them.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.59-70
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• 1999

This paper deals with automated computer-aided process planning by which designers can determine operation sequences even if they have little experience in the design of press working products. The computer-aided process planning in hot forging, deep drawing and blanking requires many kinds of technical and empirical skills based on investigation and collection of the knowledge of their processes. An approach to the CAPP system is based on the knowledge-based rules and the process knowledge base consisting of process planning rules is built. The methodology adopted to develop the system is elaborated in this paper. This system has been written in AutoLISP on the AutoCAD with a personal computer and provides powerful capabilities for process planning of hot forging, cold forging, deep drawing and blanking products.