• Structural Engineering and Mechanics
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• 제63권1호
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• 대한기계학회논문집A
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• 제27권2호
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• 전자공학회논문지C
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• 제34C권5호
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.506-508
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• 1998

In this paper, the optimal digital redesign is studied within the framework of fuzzy systems and dual-rate sampling control theory. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the optimal feedback gains developed in the continuous-time system, the constructed fuzzy system is converted into a continuous-time system. The developed continuous-time control law is converted into an equivalent slow-rate digital control law using the proposed digital redesign method. The digital redesign technique using a fuzzy model is employed to simulate the inverted pendulum dynamics.

• 산업경영시스템학회지
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• 제28권4호
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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

• 한국음향학회지
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• 제13권1호
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• pp.32-41
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• 1994

LPC열로 구성된 음성패턴의 주파수변동을 해결하기위해 LPC와 스펙트럼, LPC차수와 스펙트럼의 관계를 고찰하여 새로운 형태의 멤버쉽함수를 제안하였다. 또한, 시간변동을 해결하기위해서는 음성구간을 여러구간으로 등간격분할하는 다구간 등분할법을 사용하였으며, 이때 오인식은 주로 동일음절이 같은 발성위치에 있을때 발생되었다. 이러한 오인식을 줄이기위해 제안된 멤버쉽함수로 퍼지추론한뒤 구간별 확신도에 가중치를 부여하고, 세번째후보까지를 인식대상으로 하는 판정알고리즘을 제안하였다. 본 방법의 타당성을 검증하기위해, DDD지역명 28개를 대상으로 인식실험한결과, 삼각형멤버쉽함수에 의한 퍼지추론은 $92.0\%$, 삼각형멤버쉽함수에의한 퍼지추론과 판정알고리즘은 $92.9\%$, 제안된 멤버쉽함수에의한 퍼지추론과 판정알고리즘은 $93.8\%$의 인식률을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권4호
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• 한국콘텐츠학회논문지
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• 제20권10호
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• pp.167-176
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• 2020

현재 국내에서 활용되고 있는 자동화된 어류 급이 장치는 특정 시간과 일정량의 사료를 시간에 맞추어 수조에 공급하는 방식이다. 이는 고령화되고 고가인 양식장 관리의 인건비는 줄일 수 있으나 양식 생산성에 결정적 요인이 되는 고가의 사료량을 지능적으로 적절히 조절하기는 매우 어렵다. 본 논문에서는 이러한 기존 자동급이 장치의 문제점을 해결하고, 양식장에서 어류의 성장률을 적절하게 유지하면서 사료 공급의 효율성을 극대화할 수 있는 퍼지추론 기반의 지능형 어류 자동 급이 모델인 FIIFF 추론 모델(Fuzzy Inference based Intelligent Fish Feeding Model)을 제안한다. 본 논문에서 제안하는 FIIFF 지능형 급이 추론모델은 양식어류의 현재 생육 환경 정보 및 실시간 활동 상태를 기반으로 급이량을 산출하기 때문에 사료 급이량 적절성이 매우 높다. 본 연구에서 제안한 FIIFF 추론 모델의 급이량 산출 실험 결과에서는 8개월 동안 양식장에서 실제 투입한 급이량보다 14.8%를 절감하는 효과를 보여준다.

• 전기학회논문지
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• 제59권5호
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• 한국컴퓨터정보학회논문지
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• 제16권10호
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• pp.63-71
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• 2011

본 논문은 퍼지추론을 이용하여 신경회로망의 필기체 숫자 인식 개선 방법을 제안하였고 실험을 통하여 확인하였다. 신경회로망은 학습 시간이 오래 걸리고, 학습한 패턴에서는 100% 인식률을 보였다. 그러나 신경회로망은 시험 패턴에서는 좋은 결과를 보여주지 못했다. 실험결과 신경회로망의 인식률과 오인식률이 각각 초기 89.6%, 10.4%에서 90.2%, 9.8%로 각각 향상되었다. 특히, 숫자 3과 5에서 오인식률을 크게 감소시켰다. 실험에서 퍼지 소속 함수의 추출을 숫자의 밀도로 사용하였으나 필기체 숫자는 입력 패턴이 다양하기 때문에 다양한 특성을 추출하고 복합적으로 퍼지 추론을 사용해 더 나은 인식률을 높여야 한다. 또한 퍼지추론을 엄격하게 적용하기보다는 입력 패턴을 매칭 할 때 퍼지 추론을 적용하는 것을 제안한다.