• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.18-24
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• 1998

This paper attempts to improve domestic natural gas pricing system, thereby optimizing social welfare. This is done by deriving theoretical frameworks of natural gas pricing, which make use of both Ramsey component pricing rule and Efficient component pricing rule based on the theory of marginal cost. Allocative efficiency and social welfare between gas prices derived from the three pricing mechanism, present Cost-based pricing, Ramsey component pricing rule and Efficient component pricing rule, are analysed and compared in the case study. For the city gas, allocative efficiency of Cost-based pricing is higher than that of Ramsey component pricing rule and Efficient component pricing rule. In contrast, for the natural gas consumed for power generation, allocative efficiency of Cost-based pricing is lower than the other two pricing systems. It also turns out that social welfare is improved by the prices driven from Ramsey component pricing rule and Efficient component pricing rule rather than present Cost-based pricing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2287-2297
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• 2007

The proposed ANIDS(Advanced Network Intrusion Detection System) which is network-based IDS using Association Rule Mining, collects the packets on the network, analyze the associations of the packets, generates the pattern graph by using the highly associated packets using Association Rule Mining, and detects the intrusion by using the generated pattern graph. ANIDS consists of PMM(Packet Management Module) collecting and managing packets, PGGM(Pattern Graph Generate Module) generating pattern graphs, and IDM(Intrusion Detection Module) detecting intrusions. Specially, PGGM finds the candidate packets of Association Rule large than $Sup_{min}$ using Apriori algorithm, measures the Confidence of Association Rule, and generates pattern graph of association rules large than $Conf_{min}$. ANIDS reduces the false positive by using pattern graph even before finalizing the new pattern graph, the pattern graph which is being generated is compared with the existing one stored in DB. If they are the same, we can estimate it is an intrusion. Therefore, this paper can reduce the speed of intrusion detection and the false positive and increase the detection ratio of intrusion.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1189-1196
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• 2004

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper product to the perspective purchaser. Customer information like customer's fondness, age, gender, etc. in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of products to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of products using case-based reasoning and rule-based reasoning for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved for the system intelligence by recognizing and learning the changes of customer's desire and shopping trend.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1329-1332
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• 1987

This paper describes a design of the expert system for diagnosis of abnormal gait patients. This system makes the rule-based representation that can easily extend the knowledge-base and naturally represent the uncertainty, and the inference engine that uses forward chaining which covers the reasoning from the first condition to the goal. The results of inferring various maladies using this system are as follows: 1) In cases of progressive muscular dystrophy, cerebral vascular accident, peripheral neuropathic lesion and peroneal nerve injury, the result of inference is the same as that of medical specialists' with 100% accuracy. 2) In cases of Neuritis, Paralysis agitan and Brain tumor, the accuracy of inference is less than 50% compared to that of medical specialists. With above results, we decide that the rule-based representations of some maladies ard accurate relatively, but that the correction and the extention of some rules and some methods of problem solving are required in order to construct the complete expert system for diagnosis of abnormal gait patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.666-668
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• 2013

Wireless network environment is spreading due to the increase of using mobile devices, causing wireless network abuse. Network security and intrusion detection have been paid attention to wireless as well as wired existing and studied actively Snort-based intrusion detection system (Intrusion Detection System) is a proven open source system which is widely used for the detection of malicious activity in the existing wired network. Snort Wireless has been developed in order to enable the 802.11 wireless detection feature. In this paper, Snort Wireless Rule is analyzed. Based on the results of the analysis, present the traveling direction of future research.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.2
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• pp.30-36
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• 1993

An expert system to help a novice engineer in designing midship section is developed. The system is developed based on a general-purpose expert system shell, NEXPERT. Firstly, the design knowledge is extracted from an existing rule scantling program. The knowledge has been grouped and structured into a hierarchy by applying object-oriented concepts. Secondly, the knowledge base is integrated with a database of existing ships and engineering analysis modules through the Application Programming Interface(API)technique. Graphical User Interface which is developed using Motif wiget set is adopted. These altogether enable construction of an user friendly expert system.

• KIEAE Journal
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• v.14 no.3
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• pp.97-103
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• 2014

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.