• International Journal of Safety
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• v.11 no.2
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• pp.15-21
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• 2012

This paper is aimed to develop an electrical fire detection system (EFDS) which can analyze the possibility of electrical fire for overcurrent, leakage current and arc signals of panel board in traditional market shop. The EFDS adopted fuzzy logic and precursory data for overcurrent, leakage current and arc signals to evaluate the possibility of electrical fire. The signals are obtained directly from panel board in traditional market shops and fuzzy membership function is obtained from experiment, simulation, expert's advice. The overcurrent data is acquired by thermal data of normal and abnormal states (partial disconnection) on the insulated electrical wire, in accordance with the increase of the current signal, The leakage current data is obtained under various environments. The arc signal is acquisited by waveforms of instantaneous value in time domain and frequency band in frequency domain. The Fuzzy algorithm for DB of EFDS consists of fuzzification, inference engine by Mamdani's method and defuzzification by center of gravity method. In order to verify the performance and reliability of EFDS, it was applied to Jeon-Ju traditional market shops (90 shops) in Korea. Results show that EFDS in this paper is useful in alarming the fire case, which will prevent severe damage to human beings and properties, and reduce the electrical fires in a vulnerable area of electrical disaster.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.21-31
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• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.

• Journal of the Korea Concrete Institute
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• v.28 no.6
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• pp.723-731
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• 2016

In pretensioned concrete members, a certain bond length from the end of the member is required to secure the effective prestress in the strands, which is defined as the transfer length. However, due to the complex bond mechanism between strands and concrete, most transfer length models based on the deterministic approach have uncertainties and do not provide accurate estimations. Therefore, in this study, Adaptive Neuro-Fuzzy Inference System (ANFIS), a Neuro-Fuzzy System, is introduced to reduce the uncertainties and to estimate the transfer length more accurately in pretensioned concrete member. A total of 253 transfer length test results have been collected from literatures to train ANFIS, and the trained ANFIS algorithm estimated the transfer length very accurately. In addition, a design equation was proposed to calculate the transfer length based on parametric studies and dimensional analyses. Consequently, the proposed equation provided accurate results on the transfer length which are comparable to the ANFIS analysis results.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.81-90
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• 2000

Parallel manipulator is suitable for the high precise task because it than has higher stiffness, larger load capacity and more excellent precision, due to the closed-lop structure, than serial manipulator. But the controller design for parallel manipulator is difficult because the parallel manipulator has both the complexity of structure and the interference of actuators. The precision improvement of parallel manipulator using a classical linear control scheme is difficult because the parallel manipulator has the tough nonlinear characteristics. In this paper, firstly, the kinematic analysis of a parallel manipulator used at the experiments is performed so as to show the controllability. The analysis of internal singular configuration of the workspace is performed using the kinematic isotropic index so a sto show the limitation of control performance of a simple linear controller with fixed control gains. Secondly, a control scheme is designed by using an adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller is often used for the control of nonlinear system because it has both the inference ability and the learning ability. Lastly, the effeciency of designed control scheme is demonstrated by the real-time control experiments with IBM PC interface logic H/W and S/W of my won making. The experimental results was a success.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.128-132
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• 2012

물은 생물의 생존을 위해 필수적인 요소로 인류가 시작된 이래로 물을 효율적으로 이용하고 안전하게 관리하기 위한 노력은 계속되어 왔다. 최근 지구 온난화가 주요 원인으로 알려진 국지성 집중호우의 피해는 매우 심각하며, 이로 인해 치수에 대한 중요성은 날로 커지고 있다. 지금까지 사용해 왔던 홍수 예 경보 과정은 특정 지점의 유출량을 예측하기 위해서 강우-유출 모형을 운영하였다. 그러나 물리적 모형의 경우 운영에 필요한 매개변수의 결정과정이 복잡하고, 매개변수 결정을 위해 많은 자료를 필요로 한다. 또한 그 매개변수의 결정과정은 많은 불확실성을 포함하고 있어서 모형의 운영을 위한 전처리과정과 계산과정을 거치는 동안 발생한 오차가 누적되어 결과물 속에는 많은 오차가 포함되어 있다. 본 연구에서는 기존의 홍수 예 경보 시스템의 문제점과 불확실성을 최대한 감소시키고 더 우수한 유출량 예측을 위해 neuro-fuzzy 추론 기법을 이용한 모형인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 사용하여 하천수위를 예측하였다. ANFIS는 신경회로망과 퍼지이론을 결합한 기법으로 신경회로망의 구조와 학습 능력을 이용하여 제어환경에서 획득한 입 출력 정보로부터 언어변수의 membership 함수와 제어규칙을 제어 대상에 적합하도록 자동으로 조종하는 기법이다. 본 연구에서는 ANFIS를 사용하여 탄천 하류에 위치한 대곡교의 수위를 예측하였다. 분석을 위해 2007년부터 2011년까지의 탄천 유역의 관측 강우자료와 수위 자료 중 강우강도와 지속시간, 강우 형태에 따라 7개의 강우사상을 선정하였다. 학습자료 및 보정자료의 변화에 따른 예측 오차를 비교하여 모형의 적용성과 적정성을 평가하였다. 적용결과 입력자료 구성의 경우 해당 시간의 강우량 및 수위자료와 10분 전 강우자료를 이용한 모델이 가장 우수한 예측을 보였고, 학습자료의 경우 자료의 길이가 길고, 최대홍수량이 큰 경우 가장 우수한 예측 결과를 보였다. 본 연구의 적용결과 가장 우수한 모형의 경우 30분 예측 첨두수위 오차는 0.32%, RMSE는 0.05m 이고 예측시간이 길어짐에 따라 오차가 비선형적으로 증가하는 경향을 보였다.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.35-45
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• 2008

In order to constructan ubiquitous computing environment, it is necessary to develop a technology that can recognize users and circumstances. In this regard, the question of recognizing and expressing user Context regardless of computer and language types has emerged as an important task under the heterogeneous distributed processing system. As a means to solve this task of representing user Context in the ubiquitous environment, this paper proposes to describe user Context as the most similar form of human thinking by using semantic web and fuzzy concept independentof language and computer types. Because the conventional method of representing Context using an usual collection has some limitations in expressing the environment of the real world, this paper has chosen to use Fuzzy OWL language, a fusion of fuzzy concept and standard web ontology language OWL. Accordingly, this paper suggests the following method. First we represent user contacted environmental information with a numerical value and states, and describe it with OWL. After that we transform the converted OWL Context into Fuzzy OWL. As a last step, we prove whether the automatic circumstances are possible in this procedure when we use fuzzy inference engine FiRE. With use the suggested method in this paper, we can describe Context which can be used in the ubiquitous computing environment. This method is more effective in expressing degree and status of the Context due to using fuzzy concept. Moreover, on the basis of the stated Context we can also infer the user contacted status of the environment. It is also possible to enable this system to function automatically in compliance with the inferred state.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.243-255
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• 2003

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and ${\gamma}$ operator shows the better prediction power than the results using max and min operators from the validation procedure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1203-1208
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• 2016

For young children who are not spontaneous or not accurate in verbal communication of their emotions and experiences, drawing is a good means of expressing their status in mind and thus drawing analysis with chromatics is a traditional tool for art therapy. Recently, children enjoy digital drawing via painting tools thus there is a growing needs to develop an automatic digital drawing analysis tool based on chromatics and art therapy theory. In this paper, we propose such an analyzing tool based on dominant color analysis. Technically, we use ART2 clustering and fuzzy logic to understand the fuzziness of subjects' status of mind expressed in their digital drawings. The frequency of color usage is fuzzified with respect to the membership functions. After applying fuzzy logic to this fuzzified central vector, we determine the dominant color and supporting colors from the digital drawings and children's status of mind is then analyzed according to the color-personality relationships based on Alschuler and Hattwick's historical researches.