• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.407-414
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• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.813-819
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• 2009

Designing actuator controllers of aircraft, which control aileron, flap, elevator and so on, is quiet difficult, because they have time variant nonlinear mechanical structures and also have many kinds of disturbances which are not been able to model easily. This paper reports about the performance of Fuzzy Auto gain tuning Control algorithm applied unmaned aerial vehicle. Fuzzy Auto gain tuning PID control uses PID control and Fuzzy control, therefore It can be applied very easily and it also has advances of PID control. It can control a unmaned aerial vehicle actuators adaptively even though the designer does not have enough information of plant.

• Journal of Navigation and Port Research
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• v.26 no.1
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• pp.8-14
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• 2002

The collisions of vessel at sea show high among the whole marine accidents. Especially, the accidents of fishing vessels take the largest portion of the collisions. Therefore, a technique to reduce these accidents should be developed. The automatic control for avoiding collision suggested in this study consists of two steps. The first is recognizing collision risk with fuzzy Theory and the other is maneuvering the model ship on the basis of collision risk calculated from the first step. The information form the position and estimated time of collision point(DCPA and TCPA) is used to assess the collision risk. To verify this system, a fishing vessel was simulated according to MMG mathematical model. The simulations result shows quite good application in avoiding the collision of ship.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.75-75
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• 2010

배변 후 toilet flushing 시 다량의 세균을 포함한 물방울들이 화장실 곳곳으로 퍼지는 현상이 있다. 이러한 현상을 방지하기 위해 변기 뚜껑에 자기 세정 효과를 갖는 초발수 표면을 위해 플라즈마를 이용한 표면 처리가 시도되고 있으며, 이 연구의 일환으로 flushing시의 변기내의 유동 분석을 초고속 카메라를 이용하여 수행하였다. Toilet flushing 시 물 튀김 현상은 육안으로는 잘 관찰하기 어렵지만 최고 1000 frame/sec의 속도를 갖는 CCD camera를 이용하여 정량적으로 물 튀김에 의한 오염 가능성을 촬영 분석하였다, 두 번째로 소변 시의 변기 표면에서의 튀김현상을 분석하기 위하여 소변의 발사각도 및 속도를 가장 실제와 유사한 조건으로 설정하고 이를 상용 전산 유체 역학 소프트웨어인 CFD- ACE+의 자유 표면 계산 기능과 두 가지 유체(액체 및 기체)의 혼합 계산 모델을 사용한 계산 결과와 비교 하였다. 그 결과 변기 표면의 표면장력을 아주 작게 설정한 경우(작은 접촉각, 친수성)에는 중력의 영향을 고려하였음에도 불구하고 소변이 변기에 충돌 후 상부로 상당부분 튀어 올라가는 결과를 얻었다. 여러 가지 각도와 발사 속도, 실제의 인체와 유사한 발사 부위의 형상 변화로 인한 유체 표면의 난류 발생과 이에 따른 변기 표면 충돌 현상 변화 등을 수치적으로 고찰하였다. 한 예로 5.6 mm 직경의 노즐에서 소변이 나오는 경우를 발사 속도 3 m/s, 각도 $10^{\circ}$로 주고 중력을 고려하여 10초 동안을 계산하면, 방뇨 시 toilet bowl 내부에서의 물의 유동과 toilet 표면을 맞고 튀기는 현상을 그림 1과 같이 볼 수 있었다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.608-610
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• 2006

Supply Chain Management(SCM) system is a critical investment that can affect future competitiveness and performance of a company. When adopting a new SCM system, organizations experience increasing difficulty in decision making because information technology is changing so rapidly these days. Therefore, organizations have been looking for industry standards and proven methods of selection that they can utilize to choose the best SCM system. To select an optimum solution, we need to consider a number of different quantitative and qualitative factors such as cost, user interface and convenience, reference site, and so on. In this study, we propose a solution selection model of SCM systems using Fuzzy AHP to maximize the return on investment in information technology. The proposed model can systematically construct the objectives of SCM system selection to support the business goals.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.4
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• pp.109-115
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• 2005

In the study, Methods for operating measures in equipment security to find out dangerousness timely in the system and to need for the prevention and measures. The method for analyzing and reconstructing the causes of accident of equipment in site, and try to save the information of site in real-time and to analyze the state of equipment to look for the factors of accidents. By this analysis, one plan for efficiency of production, Equipment Fault Diagnosis Management and security is integrating and building module of using the Fuzzy Inference based on fuzzy theory. The case study is applied to the industrial electric motors that are necessarily used to all manufacturing equipment. Using the sensor for temperature is attached to gain the site information in real time and to design the hardware module for signal processing. In software, realize the system supervising and automatically saving to management data base by the algorithm based in fuzzy theory from the existing manual input system