• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.290-295
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• 1989

This paper proposes a Expert CAPP System for integrating CAD/CAM of rotational work-part by rule based approach. The CAD/CAPP integration is performed by the recognition of machined features from the 2-D CAD data (IGES) file. Selecting functions of the process planning are performed in modularized rule base by forward chaining inference, and operation sequences are determined by means of heuristic search algorithm. For CAPP/CAM integration, post-processor generates NC code from route sheet file. This system coded in OPS5 and C language on PC/AT, and EMCO CNC lathe interfaced with PC through DNC and RS-232C.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.8
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• pp.54-61
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• 1999

In this paper, we apply one of modified Genetic Algorithms, a Multi-population Genetic Algorithm(MGA) that improves the genetic diversity to determine the fuzzy rule base and the shape of membership functions. The generation of the fuzzy rule base for fuzzy control, generally, depends on expert's experience. We suggest a new evaluation function to optimize fuzzy rule base. Simulation shows that the proposed method has good result.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.625-631
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• 2000

In designing fuzzy model, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious-and-error process. This paper presents an approach to automatic design of optimal fuzzy rule bases for modeling using evolutionary programming. Evolutionary programming evolves simultaneously the structure and the parameter of fuzzy rule base a given task. To check the effectiveness of the suggested approach, 3 examples for modeling are examined, and the performance of the identified models are demonstrated.

• Journal of the military operations research society of Korea
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• v.2 no.1
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• pp.177-184
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• 1976

As the organizational size of a military service or business increases and its management becomes complex, the success in its management depends less on static type of management but more on careful, dynamic type of management. In this thesis, an operations research technique is applied to the problems of determining optimal air traffic control rule and of optimal capacity of air base for a military air base. An airport runway is regarded as the service facility in a queueing mechanism, used by landing, low approach, and departing aircraft. The usual order of service gives priority different classes of aircraft such as landings, departures, and low approaches; here service disciplines are considered assigning priorities to different classes of aricraft grouped according to required runway time. Several such priority rules are compared by means of a steady-state queueing model with non-preemptive priorities. From the survey conducted for the thesis development, it was found that the flight pattern such as departure, law approach, and landing within a control zone, follows a Poisson distribution and the service time follows an Erlang distribution. In the problem of choosing the optimal air traffic control rule, the control rule of giving service priority to the aircraft with a minimum average waiting cost, regardless of flight patterns, was found to be the optimal one. Through a simulation with data collected at K-O O Air Base, the optimal take-off interval and the optimal capacity of aircraft to be employed were determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.

• ETRI Journal
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• v.39 no.4
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• pp.592-604
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• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.