• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.160-171
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• 2017

US national research laboratories developed the first Vital Area Identification (VAI) method for the physical protection of nuclear power plants that is based on Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) techniques in 1970s. Then, Korea Atomic Energy Research Institute proposed advanced VAI method that takes advantage of fire and flooding Probabilistic Safety Assessment (PSA) results. In this study, in order to minimize the burden and difficulty of VAI, (1) a set of streamlined VAI rules were developed, and (2) this set of rules was applied to PSA fault tree and event tree at the initial stage of VAI process. This new rule-based VAI method is explained, and its efficiency and correctness are demonstrated throughout this paper. This new rule-based VAI method drastically reduces problem size by (1) performing PSA event tree simplification by applying VAI rules to the PSA event tree, (2) calculating preliminary prevention sets with event tree headings, (3) converting the shortest preliminary prevention set into a sabotage fault tree, and (4) performing usual VAI procedure. Since this new rule-based VAI method drastically reduces VAI problem size, it provides very quick and economical VAI procedure. In spite of an extremely reduced sabotage fault tree, this method generates identical vital areas to those by traditional VAI method. It is strongly recommended that this new rule-based VAI method be applied to the physical protection of nuclear power plants and other complex safety-critical systems such as chemical and military systems.

• The Journal of Society for e-Business Studies
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• v.12 no.1
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• pp.99-122
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• 2007

The rule based system is inference engine which can correspond quickly to new business model change and improvement requirement by dealing with the business know-how and expert knowledge as well as business process of enterprise and has been trying to apply to the various industries. As a part of application cases for rule-based system, we develop and implement the rule-based insurance product recommend and design system for the efficient decision making of insurance product in insurance industry which is sensitively affected by needs of customers, various kinds of product, and environment changes. The process of fuzzy inference of the developed system helps to recommend and design the proper Insurance product using the information of the present customer and the previous members. This approach is expected that it will be the core technology for the recommendation and design of the tailored insurance product by deciding and corresponding needs of various kinds of customer quickly in future insurance industry.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.65-70
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• 2020

PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.25-41
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• 2009

The objective of this study is to develop an analytic framework to code systematically the gamer's behaviour in MMO(Massively Multi-player Online) gameplay experience, to explore their gameplay as a problem solving procedure empirically. Previous studies about model human processor, content based protocol, and procedure based protocol are reviewed in order to build the outline of the analytic framework related to MMO gameplay. The specific gameplay actions and contents were derived by using concurrent protocol analysis method through the empirical experiment executed in MMORPG gameplay. Consequently, gameplay are divided into six actions : kinematics, perception, function, representation, simulation, and rule (heuristics, following, and transcedence). The analytic framework suitable for MMO gameplay was built. As a result of this study, we found three rule spaces in the problem solving domain of gameplay that are an heuristics, a following of the rule, and a transcendence of the rule. 'Heuristics' denotes the rule action that discovers the rule of game through trial-and-error. 'Following' indicates the rule action that follows the rule of game embedded in game by game designers. 'Transcendence' presents the rule action that transcends that. The new discovered rule spaces where 'Following' and 'Transcendence' actions occur and the gameplay pattern in them is provided with the key basis to determine the level design elements of MMO game, such as terrain feature, monster attribute, item, and skill et cetera. Therefore, this study is concludes with key implications to support game design to improve the quality of MMO game product.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.

• 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.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.40-49
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• 2010

A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.492-497
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• 1999

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skillful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop, the other is the quantitative method which utilizes the experimental data obtained by sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are not easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based rule, which is strongly depended upon experience and intuition, is described.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.81-88
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• 2016

The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.