• Journal of Electrical Engineering and information Science
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• 제3권3호
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• pp.355-365
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• 1998

The purposes of this study are to propose a network for the characterizing of the input data and to show how to design predictive neural net재가 expert system which doesn't need previous knowledge base. We derived this network from the radial basis function networks(RBFN), and named it as a statistical EBFN. The proposed network can replace the statistical methods for analyzing dynamic relations between target disease and other parameters in medical studies. We compared statistical RBFN with the probabilistic neural network(PNN) and fuzzy logic(FL). And we testified our method in the diabetes prediction and compared our method with the well-known multilayer perceptron(MLP) neural network one, and showed good performance of our network. At last, we developed the diabetes prediction expert system based on the proposed statistical RBFN without previous knowledge base. Not only the applicability of the characterizing of parameters related to diabetes and construction of the diabetes prediction expert system but also wide applicabilities has the proposed statistical RBFN to other similar problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.315-323
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• 2013

Vessel Traffic Service (VTS) is being used at ports and in coastal areas of the world for preventing accidents and improving efficiency of the vessels at sea on the basis of "IMO RESOLUTION A.857 (20) on Guidelines for Vessel Traffic Services". Currently, VTS plays an important role in the prevention of maritime accidents, as ships are required to participate in the system. Ships are diversified and traffic situations in ports and coastal areas have become more complicated than before. The role of VTS operator (VTSO) has been enlarged because of these reasons, and VTSO is required to be clearly aware of maritime situations and take decisions in emergency situations. In this paper, we propose a prediction table to improve the work of VTSO through the Cognitive Work Analysis (CWA), which analyzes the VTS work very systematically. The required data were collected through interviews and observations of 14 VTSOs. The prediction tool supports decision-making in terms of a proactive measure for the prevention of maritime accidents.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.327-336
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• 2004

In distributed autonomous mobile robot system, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where virtual robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.

• International Journal of Naval Architecture and Ocean Engineering
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• 제4권3호
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• pp.199-210
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• 2012

The recent researches on the automatic berthing control problems have used various kinds of tools as a control method such as expert system, fuzzy logic controllers and artificial neural network (ANN). Among them, ANN has proved to be one of the most effective and attractive options. In a marine context, the berthing maneuver is a complicated procedure in which both human experience and intensive control operations are involved. Nowadays, in most cases of berthing operation, auxiliary devices are used to make the schedule safer and faster but none of above researches has taken into account. In this study, ANN is applied to design the controllers for automatic ship berthing using assistant devices such as bow thruster and tug. Using back-propagation algorithm, we trained ANN with set of teaching data to get a minimal error between output values and desired values of four control outputs including rudder, propeller revolution, bow thruster and tug. Then, computer simulations of automatic berthing were carried out to verify the effectiveness of the system. The results of the simulations showed good performance for the proposed berthing control system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.249-254
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• 2009

The today's decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge's, decision making tools and possibly expertise's pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.

• 한국지능시스템학회논문지
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• 제9권4호
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• pp.420-425
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• 1999

최대수요전력 예측과 제어의 목적은 공장 또는 빌딩등의 전력수용가의 입장에서 수시로 변동하는 부하의추이를 파악 예측하여 에너지 합리화 경제성 증대 산업기기의 보호 수용가의 비용절감과 더불어 크게는 국가적인 전력시스템안정화를 가져가기 위함에 있다. 최대수요전력 예측/제어를 위한 기존의 방법들은 수용가 특성이나 계절별 요일별 차이를 고려하지 않고 고정된 알고리즘에 의해 예측값이 결정되므로 환경변화에 적극적인 대응능력이 부족한 단점이있다. 이와같은 문제점의 해결을 위해 본 논문에서는 현재 많은 연구가 되고 있는 SOFM 신경망을 이용한 예측 방법과 예측치의 보정방법으로 퍼지제어길르 추가한 형태의 최대수요전력예측 제어기를 제안한다, 예측방법의 경우 유동적이며 적은 구간을 통하여 순시부하처럼 변동이 많은 데이터에 대하여 예측시간을 단축함과 동시에 오차를 줄여나갈수 있다. 또한 2단계의 학습을 통하여 SOFMd의 출력값이 패턴이 아닌 예측치가 될 수 있도록 변형하였으며 패턴자체의 변화에 대응하여 패턴오차를 이용하여재학습을 하도록 하여 불안정한 전력에 대하여 보완한다. 그리고 예측후반부에 퍼지제어기를 연결하여 예측의 신뢰성을 높이는 안정된 예측구조를 가지고 있다. 실험결과 시계열 예측방법인 지수평활법보다 제안된 예측/제어 방법이 우수함을 확인하였다.

• 한국지리정보학회지
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• 제15권3호
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• pp.119-136
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• 2012

본 연구는 IPCC(Intergovernmental Panel on Climate Change)에서 제시한 기후변화 취약성 개념을 서울시에 적용, 적정 홍수 취약성 지표 산정 및 퍼지모형을 활용하여 기후변화 분야 중 홍수취약성을 평가하고 GIS를 이용하여 취약성도를 작성하였다. 이를 위해 선행연구를 기반으로 지표를 도출하였다. 도출된 지표는 기후노출(일 최대 강수량, 일강수량 80m 이상인 날 수), 민감도(침수지역, 경사, 지질, 고도, 하천으로부터의 거리, 지형, 토양 및 불투수면적) 및 적응능력(홍수조절능력, 자연녹지, 공원녹지) 등의 자료이며, 이를 GIS 기반의 공간데이터베이스로 구축하였다. 구축된 지표값들을 통합하기 위한 방법으로 퍼지모형을 활용했으며, 퍼지소속값 결정을 위해서는 빈도비를 활용하였다. 2010년 침수 발생 자료를 활용하여 항목들간의 상관관계 및 퍼지소속값을 산정하였으며, 2011년 침수 발생 지역으로 작성된 취약성도를 검증하였다. 분석결과 서울지역 홍수피해에 크게 영향을 미치는 지표는 일강수량이 80mm이상인 날수, 하천과의 거리, 불투 수층으로 나타났다. 서울의 경우, 최대강수량이 269mm 이상일 때 적응능력(유수지, 녹지)이 부족하고, 고도가 16~20m 정도이며 하천에서 50m이내에 인접한 지역, 공업용지에서 홍수취약성이 매우 높은 것으로 나타났다. 지역적으로 영등포구, 용산구, 마포구 등 한강 본류의 양안에 위치한 구들이 비교적 취약지역을 많이 포함하고 있는 것으로 나타났다. 본 연구는 기후변화 취약성 평가의 개념을 적용하고, 방법론으로 퍼지모형을 활용함으로써 기존의 취약성 평가기법을 개선하였으며 평가결과는 홍수예방정책에 대한 우선지역 선정과 의사결정의 주요한 근거로 활용될 수 있을 것으로 기대된다.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.75-82
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• 2015

This paper presents a new technique for enhancing power quality by reducing harmonics in the neutral conductor. Three-Phase Four-Wire (3P4W) system is commonly used where single and three phase loads are connected to Point of Common Coupling (PCC). Due to unbalance loads, the 3P4W distribution system becomes unbalance and current flows in the neutral conductor. If loads are non-linear, then the harmonic content of current will flow in neutral conductor. The neutral current that may flow towards transformer neutral point is compensated by using a series active filter. In order to reduce the harmonic content, the series active filter is connected in series with the neutral conductor by which neutral and phase current harmonics are reduced significantly. In this paper, solar PV based inverter circuit is proposed for compensating neutral current harmonics. The simulation is carried out in MATLAB/SIMULINK and also an experimental setup is developed to verify the effectiveness of the proposed method.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.306-316
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• 2013

The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by left-shift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.295-299
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• 2015

The developed security scheme for user authentication, which uses both a password and the various devices, is always open by malicious user. In order to solve that problem, a keystroke dynamics is introduced. A person's keystroke has a unique pattern. That allows the use of keystroke dynamics to authenticate users. However, it has a problem to authenticate users because it has an accuracy problem. And many people use passwords, for which most of them use a simple word such as "password" or numbers such as "1234." Despite people already perceive that a simple password is not secure enough, they still use simple password because it is easy to use and to remember. And they have to use a secure password that includes special characters such as "#!($^*$)^". In this paper, we propose the automatic fortified password generator system which uses special characters and keystroke feature. At first, the keystroke feature is measured while user key in the password. After that, the feature of user's keystroke is classified. We measure the longest or the shortest interval time as user's keystroke feature. As that result, it is possible to change a simple password to a secure one simply by adding a special character to it according to the classified feature. This system is effective even when the cyber attacker knows the password.