• 한국지능시스템학회논문지
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• 제26권5호
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• pp.343-350
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• 2016

Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

• Journal of Astronomy and Space Sciences
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• 제29권4호
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• 한국안전학회지
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• 제37권1호
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• 콘크리트학회논문집
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• 제17권1호
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• pp.157-166
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• 2005

This article involves architecting prototype-fuzzy expert system for designing the nominal cover thickness by means of fuzzy inference for quantitatively representing the environment affecting factor to reinforced concrete in chloride-induced corrosion environment. In this work, nominal cover thickness to reinforcement in concrete was determined by the sum of minimum cover thickness and tolerance to that defined from skill level, constructability and the significance of member. Several variables defining the quality of concrete and environment affecting factor (EAF) including relative humidity, temperature, cyclic wet and dry, and the distance from coast were treated as fuzzy variables. To qualify EAF the environment conditions of cycle degree of wet-dry, relative humidity, distance from coast and temperature were used as input variables. To determine the nominal cover thickness a qualified EAF, concrete grade, and water-cement ratio were used. The membership functions of each fuzzy variable were generated from the engineering knowledge and intuition based on some references as well as some international codes of practice.

• Smart Structures and Systems
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• 제15권4호
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• 대한인간공학회지
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• 제14권1호
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• pp.97-104
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• 1995

The widespread use of VDU has improved the efficiency of information transmission between man and machine, but has caused new occupational health and ergonomics problems. In this study, we tried to construct a fuzzy hyman performance model of VDU workers in Korea. Fuzzy inferences of human perfor- mance are obtained from the fuzzy inference rule with the job difficulty, CFF, SACL, Type A. and the degree of concentration in VDU work. Eight healthy female undergraduate students at Kyungnam university for subjects aged 20 to 23 years were examined in this experiment. They calculated continuous addition, subtraction, and multiplication of 1 or 2 digit numbers that were produced randomly on the CRT. Subjects peoformed two types of a numeric operation, which easy and difficult work produced 400 and 600 problems within a 40 minute work session, respectively. Subjects were tested over two workdays according to the type of work(easy and difficult) consisting of four 40 minutes work sessions in the morning. Each work lasted for five minutes with a ten minutes rest break. 117 fuzzy inference rules were obtained from the experimental data. The value of consequent part was obtained by a descent method. The difference between real human error and estimated value of fuzzy inference was $1.8075{\pm}1.8591%(M{\pm}SD)$. The difference in easy and diffcult works were $2.69{\pm}2.13%$ and $0.92{\pm}0.93%$, respectively.

• Spatial Information Research
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• 제7권2호
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• pp.191-207
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• 1999

공간과 관련된 의사결정문제 해결에 필요한 취득가능한 자료나 정보는 불완전하거나 부정확하며, 많은 부분 자연산어(natural language)로 기술되어 있다. 이 같은 정보들을 컴퓨터를 이용하여 처리하기 위해서는 결국 컴퓨터로 하여금 인간이 사용하는 자연어를 이해할 수 있도록 애매한 특성의 언어값(Linguistic value)을 정량적으로 기술할 필요가 있다. 이를 위해 퍼지집합(fuzzy set) 이론을 퍼지논리(fuzzy logic)가 대표적인 방법론으로 이용되고 있다. 본 논문에서는 부정확하거나 불명확한 자료 및 정보를 기반으로 의사결정문제를 지능적으로 처리하기위해 사용자가 가장 이해하기 쉬운 자연어로 『언어모델』을 구축하고, 평가사안이나 의사결정문제가 불명확하게 서술될 경우 컴퓨터를 이용한 구조화 및 추론을 통한 문제해결이 가능하도록 퍼지추론기관구축을 위한 일련의 논리적 개념과 구축과정을 연구하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• 조명전기설비학회논문지
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• 제15권3호
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• pp.57-66
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• 2001

본 논문에서는 전력계통의 안정도를 향상시키기 위하여 동기 발전기와 정지형 무효전력 보상기예 대한 퍼지-PI 제어기를 설계하기 위한 제어 기법을 설명하였다. 정지형 무효전력 보상기는 고정된 용량의 커패시터와 싸이리스터 제어에 의하여 용량이 가변되는 인덕터가 병렬로 연결된 구조를 가지고 있으며, 시스템 전압을 제어할 뿐만 아니라 동기 발전기의 제동을 개선하기 위해 설계되었다. 본 논문에서 제안한 SVC 계통의 퍼지-PI 제어기의 파라미터는 퍼지 추론 기법에 의해 자동 동조되어진다. 퍼지 추론 기법은 일반적인 기법과는 달리 인간의 경험과 전문가의 지식을 제어 규칙으로 제어 동작을 결정하였다. 그리하여 인간의 추론 과정과 매우 유사한 MMGM을 이용하여 PI 이득의 퍼지 추론 기법을 SVC 계통에 적용하여 설명하였다. 제안된 방법의 강인성을 입증하기 위해 중부하시, 정상부하시 및 경부하시에 초기 전력을 변동시킨 경우에 대하여 시스템의 회전자각, 각속도 편차 특성 및 단자전압의 동특성을 고찰하여 기존의 전력시스템안정화장치보다 응답특성이 우수함을 보였다.