• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.572-575
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• 2004

Since the 1990s, the advancement of semiconductor technology has resulted in the development of microprocessor technology, auxiliary computer technology, and application technology such as intelligent algorithms (neural network, fuzzy, etc.). These based the development of intelligent machines. An agent is autonomous software that recognizes environment, exchanges knowledge with other agents and makes decisions. We designed agent-based sensor structure. For the purpose, first, it modeled the function of an intelligent machine. Second, it designed sensory function on the agent level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1722-1741
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• 2017

Intrusion detection techniques based on virtual machine introspection (VMI) provide high temper-resistance in comparison with traditional in-host anti-virus tools. However, the presence of semantic gap also leads to the performance and compatibility problems. In order to map raw bits of hardware to meaningful information of virtual machine, detailed knowledge of different guest OS is required. In this work, we present VDSM, a lightweight and general approach based on driver separation mechanism: divide semantic view reconstruction into online driver of view generation and offline driver of semantics extraction. We have developed a prototype of VDSM and used it to do intrusion detection on 13 operation systems. The evaluation results show VDSM is effective and practical with a small performance overhead.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.9
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• pp.192-203
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• 2001

The design of gate and runner(delivery system) is one of the most important subject in injection molding. Delivery system is a channel to flow the polymer melt from the injection molding machine to the mold cavities. Also, delivery system affect quality and productivity of the part. The synthesis of delivery system of injection molding has been done empirically, since it requires profound knowledge about the moldability and causal effects on the properties of the part, which are not available to designers through the current CAD systems. GATEWAY is a knowledge module which contains knowledge to permit non-experts as well as mold design experts to generate the acceptable geometries of gate and runner far injection molded parts. A knowledge-based CAD system is constructed by adding the knowledge module, GATEWAY, to an existing geometric modeler. A knowledge-based CAD system is a new tool which enables the concurrent design and CIM with integrated and balanced design decisions at the initial design of injection molding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.407-420
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• 2015

Natural Language Question Answering (NLQA) and Prescriptive Analytics (PA) have been identified as innovative, emerging technologies in 2015 by the Gartner group. These technologies require knowledge bases that consist of data that has been extracted from unstructured texts. Every business requires a knowledge base for business analytics as it can enhance companies' competitiveness in their industry. Most intelligent or analytic services depend a lot upon on knowledge bases. However, building a qualified knowledge base is very time consuming and requires a considerable amount of effort, especially if it is to be manually created. Another problem that occurs when creating a knowledge base is that it will be outdated by the time it is completed and will require constant updating even when it is ready in use. For these reason, it is more advisable to create a computerized knowledge base. This research focuses on building a computerized knowledge base for business using a supervised learning and rule-based method. The method proposed in this paper is based on information extraction, but it has been specialized and modified to extract information related only to a business. The business knowledge base created by our system can also be used for advanced functions such as presenting the hierarchy of technologies and products, and the relations between technologies and products. Using our method, these relations can be expanded and customized according to business requirements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.13-23
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• 2001

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Hence, these systems are inflexible in the sense that they cannot easily be adapted to other applications. In this paper, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify \\\"defects\\\" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning if knowledge that allows concurrent parallel processing during recognition.cognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.719-724
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• 1998

This paper presents a systematic developement of a formal approach to inference in approximate reasoning. We introduce some measures of similarity and discuss their properties. Using the concept of similarity index we formulate two methods for inferring from vague knowledge. In order to illustrate the effectiveness of the proposed technique we use it to develop a vowel recognition system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.589-594
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the losed loop problem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and efficiently. This algorithm is a hybrid type of using both declarative description (constraint represention) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.116-123
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.