• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.64-83
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• 2012

The number of smart phone users is rapidly growing due to recent increase in wireless Internet usage, development of a wide variety of applications, and activation of M2M (Machine to machine) services. Although the smart phone offers benefits of mobility and convenience, it also has serious security problems. To utilize M2M services in the smart phone, a flexible integrated authentication and access control facility is an essential requirement. To solve these problems, we propose a context-aware single sign-on and access control system that uses context-awareness, integrated authentication, access control, and an OSGi service platform in the smart phone environment. In addition, we recommend Fuzzy Logic and MAUT (Multi-Attribute Utility Theory) in handling diverse contexts properly as well as in determining the appropriate security level. We also propose a security system whose properties are flexible and convenient through a typical scenario in the smart phone environment. The proposed context-aware security system can provide a flexible, secure and seamless security service by adopting diverse contexts in the smart phone environment.

• 전력전자학회논문지
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• 제18권5호
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• pp.466-476
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• 2013

This paper describes a fuzzy control method to control the output voltage of the three-phase Z-source PWM rectifier. A fuzzy control system is a control system based on fuzzy logic, and the fuzzy controller uses a single input fuzzy theory with its fuzzification. Analytical structure of the simplest fuzzy controller is derived through the triangular membership functions with its fuzzification. By setting the membership functions of the fuzzy rules, fuzzy control is achieved. The PI portion of the output DC voltage controller is controlled by fuzzy method. To confirm the validity of the proposed method, the simulation and experiment were performed, The simulation is performed with PSIM and MATLAB/SIMULINK. For the experiment, we used a DSP(TMS320F28335) controller to compute the reference value and generate the PWM pulses. For the transient state performance of the output DC voltage control of Z-source PWM rectifier, the PI controller and fuzzy controller were compared, also the conventional PWM rectifier and Z-source PWM rectifier were compared. From the results, the Z-source rectifier could allow to buck or boost of the output DC voltage. Through the analysis of the transient state, we could observe that the fuzzy controller has better performance than the conventional PI controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권1호
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• pp.19-24
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• 2011

Edges represent significant boundary information between objects or classes. Various methods, which are based on differential operation, such as Sobel, Prewitt, Roberts, Canny, and etc. have been proposed and widely used. The methods are based on a linear convolution of mask with pre-assigned coefficients. In this paper, we propose an edge detection method based on Dempster-Shafer's evidence theory to evaluate edgeness of the given pixel. The effectiveness of the proposed method is shown through experimental results on several test images and compared with conventional methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.167-172
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• 2006

As ubiquitous devices are fast spreading, the communication problem between humans and these devices is on the rise. The use of context is important in interactive application such as handhold and ubiquitous computing. Context is not crisp data, so it is necessary to introduce the fuzzy concept. The proxity relation is represented by the degree of closeness or similarity between data objects of a scalar domain. A context manager of context-awareness system evaluates imprecise queries with the proximity relations. in this paper, a systematic proximity elicitation method are proposed. The proposed generation method is simple and systematic. It is based on the well-known fuzzy set theory and applicable to the real world applications because it has tuning parameter and weighting factor. The proposed representations of proximity relation is more efficient than the ordinary matrix representation since it reflects some properties of a proximity relation to save space. We show an experiments of quantitative calculate for the proximity relation. And we analyze the time complexity and the space occupancy of the proposed representation method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.534-536
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• 1999

Fuzzy Systems which are based on membership functions and rules, can control nonlinear, uncertain, complex systems well. However, Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm, it takes long time to converge into global, optimal parameters. Well-developed linear system theory should not be replaced by FLC, but instead, it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• 한국구조물진단유지관리공학회 논문집
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• 제1권2호
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• pp.131-140
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• 1997

콘크리트 교량을 저렴하고 신속하게 상태 평가를 하기 위하여 성능 지수(Performance Index)를 제안한다. 이 기법은 육안 검사에 의하여 발견된 결함의 범위와 심각도를 사용하여 콘크리트 교량의 전반적인 상태를 신속하게 등급화 하고 콘크리트 교량의 노출 조건을 고려하여 콘크리트 성능을 정량적으로 평가한다. 또한 본 연구에서는 상기한 성능 지수 기법의 타당성을 증명할 수 있는 정밀 안전 진단 시험결과를 활용하여 6개의 주요 노후화 원인을 고려하는 또 다른 성능 지수를 제안한다. 이러한 두 상태 평가 방법이 영국의 실제 교량 상태 평가 자료를 바탕으로 한 상태 평가 결과를 퍼지 집합 이론(fuzzy set theory)으로 분석한 결과와 비교하여 방법의 정당성 및 신뢰성을 논의한다.

• 한국정보통신학회논문지
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• 제7권5호
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• pp.853-862
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• 2003

본 논문에서는 퍼지 이론을 이용한 학습 평가 방법을 제안하였다. 제안된 학습 평가 방법은 정보처리 데이터베이스 과목에 대한 기출문제의 출제 빈도수를 3등급으로 분류하고 이것을 중요도라 정의하였다. 학습 중요도에 따른 학습 횟수에 대한 퍼지 소속도와 형성평가 점수에 대한 퍼지 소속도를 각각 9개의 퍼지 추론 규칙에 적용하여 학습 이해도를 평가하였다. 최종적인 학습 평가는 각 장별 학습 이해도에 대한 퍼지 등급과 총괄평가 점수에 대한 소속도를 이용하여 퍼지 추론규칙에 적용하고 비퍼지화하여 평가하였다. 제시된 퍼지 이론을 이용한 학습 평가 방법은 학습자가 스스로 학습한 내용을 진단 할 수 있도록 도와주며, 학습목표의 성취여부를 종합적이고 객관적으로 판단할 수 있는 방법을 제공한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.104-112
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• 2001

Simply, Nonlinear dynamics theory means the complicated and noise-like phenomena originated form nonlinearity involved in deterministic dynamical system. An almost all the natural signals have nonlinear property. However, there exist few analysis software tool or package for a research and development of applications. We develop nonlinear time series analysis simulator is to provide a common and useful tool for this purpose and to promote research and development of nonlinear dynamics theory. This simulator is consists of the following four modules such as generation module, preprocessing module, analysis module and ICA module. In this paper, we applied to Electroencephalograph (EEG), as it turned out, our simulator is able to analyze nonlinear time series. Besides, we could get the useful results using the various parameters. These results are used to diagnostic the brain diseases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권2호
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.