• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1182-1189
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• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.

• Korean Journal of Computational Design and Engineering
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• v.11 no.5
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• pp.365-374
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• 2006

The ubiquitous environment is to support people in their everyday life in an inconspicuous and unobtrusive way. This environment requires information such as the person, his/her preferences, and habits which is available in the ubiquitous system. In this paper, we propose the context aware system that can provide the tailored information service for user in ubiquitous computing environment. Our system architecture is divided into 4 domain models such as context awareness, presentation, interface and inference domain. Each domain model can perform some predefined tasks independently. And we suggest the hybrid algorithm combined with fuzzy and Bayesian method in order to reason what is the suitable information for user. We show the possibility for the real application through applying the system to the TGA (Tour Guide Assistant) for Kyoungju historical site.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1359-1361
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• 1996

This paper describes a design of real time fuzzy controller using Minimum fuzzy control Rule Selection Method(MRSM). The control algorithm of dynamic systems needs less computation time and memory. To reduce the computation time of fuzzy logic controller, minimum number of rules are to be selected for the fuzzy input variable. The universe of discourse is divided by the number of linguistic labels to allocate the assigned membership function to the fuzzy input variables. In this case, since fuzzy input variables are continuous, scale factor SU is tuned independently. According to increment of SU control surface is improved to adapt the change of system parameter. At this, crisp control surface is increased. With the increament of crisp control surface, fuzzy control surface is reduced. When error state deviates from desirable error state, crisp control surface is more useful than fuzzy control surface for obtaining fast rising time.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.3-9
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• 1996

본 논문에서는 Boole 함수를 기호 다치 논리 함수로 확장하여 법-M(Modulus-M)의 수체계를 기본으로 하는 기호 다치 논리 함수에 대한 MacLaurin 전개의 구조적 성질을 분석한다. 그리고 기호 다치 변수의 상태 변화에 따라 이에 사상된 퍼지 규칙을 자동 생성할 수 있는 기법을 제안한다. 또한 이러한 이론과 성질을 기존의 퍼지 추론 기능과 결합하여 동적인 상태 변화에 적응할 수 있는 퍼지 추론 시스템 설계방법을 제안한다.

• International Journal of Safety
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• v.11 no.1
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• pp.6-10
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• 2012

This paper presents a study on the analysis for reducing the number of false alarms in fire detection system. In order to intelligent algorithm fuzzy logic is adopted in developing fire detection system to reduce false alarm. The intelligent fire detection algorithm compared and analyzed the fire and non-fire signatures measured in circuits simulating flame fire and smoldering fire. The algorithm has input variables obtained by fire experiment with K-type thermocouple and optical smoke sensor. Also triangular membership function is used for inference rules. And the antecedent part of inference rules consists of temperature and smoke density, and the consequent part consists of fire probability. A fire-experiment is conducted with paper, plastic, and n-heptane to simulate actual fire situation. The results show that the intelligent fire detection algorithm suggested in this study can more effectively discriminate signatures between fire and similar fire.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.330-336
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• 2005

Existing Bayesian update method using ultrasonic sensors only for mobile robot map building has a problem of the quality of map being degraded in the wall with irregularity, which is caused by the wide beam distribution. For improving this problem, an infrared sensors aided map building method is presented in this paper. Information of obstacle at each region in ultrasonic sensor beam is acquired using the infrared sensors and the information is used to get the confidence of ultrasonic sensor information via fuzzy inference system and genetic algorithm. Combining the resulting confidence with the result of Bayesian update method, an improve map is constructed. The proposed method showed good results in the simulations and experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.406-434
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• 2019

In general, an information security approach estimates the risk, where the risk is to occur due to an unusual event, and the associated consequences for cloud organization. Information Security and Risk Management (ISRA) practices vary among cloud organizations and disciplines. There are several approaches to compare existing risk management methods for cloud organizations but their scope is limited considering stereo type criteria, rather than developing an agent based task that considers all aspects of the associated risk. It is the lack of considering all existing renowned risk management frameworks, their proper comparison, and agent techniques that motivates this research. This paper proposes Agent Based Information Security Framework for Hybrid Cloud Computing as an all-inclusive method including cloud related methods to review and compare existing different renowned methods for cloud computing risk issues and by adding new tasks from surveyed methods. The concepts of software agent and intelligent agent have been introduced that fetch/collect accurate information used in framework and to develop a decision system that facilitates the organization to take decision against threat agent on the basis of information provided by the security agents. The scope of this research primarily considers risk assessment methods that focus on assets, potential threats, vulnerabilities and their associated measures to calculate consequences. After in-depth comparison of renowned ISRA methods with ABISF, we have found that ISO/IEC 27005:2011 is the most appropriate approach among existing ISRA methods. The proposed framework was implemented using fuzzy inference system based upon fuzzy set theory, and MATLAB(R) fuzzy logic rules were used to test the framework. The fuzzy results confirm that proposed framework could be used for information security in cloud computing environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2414-2434
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• 2015

Among numerous localization schemes proposed specifically for Wireless Sensor Network (WSN), the range-free localization algorithms based on the received signal strength indication (RSSI) have attracted considerable research interest for their simplicity and low cost. As a typical range-free algorithm, Approximate Point In Triangulation test (APIT) suffers from significant estimation errors due to its theoretical defects and RSSI inaccuracy. To address these problems, a novel localization method called FIAPIT, which is a combination of an improved APIT (IAPIT) and a fuzzy logic system, is proposed. The proposed IAPIT addresses the theoretical defects of APIT in near (it's defined as a point adjacent to a sensor is closer to three vertexes of a triangle area where the sensor resides simultaneously) and far (the opposite case of the near case) cases partly. To compensate for negative effects of RSSI inaccuracy, a fuzzy system, whose logic inference is based on IAPIT, is applied. Finally, the sensor's coordinates are estimated as the weighted average of centers of gravity (COGs) of triangles' intersection areas. Each COG has a different weight inferred by FIAPIT. Numerical simulations were performed to compare four algorithms with varying system parameters. The results show that IAPIT corrects the defects of APIT when adjacent nodes are enough, and FIAPIT is better than others when RSSI is inaccuracy.