• Journal of the Ergonomics Society of Korea
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• v.19 no.2
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.168-171
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• 2006

본 논문에서는 Particle Swarm Optimization 기법을 이용한 뉴로-퍼지 시스템의 파라미터 동정을 실시한다. PSO의 학습 및 군집 특성을 이용하여 시스템을 학습한다. 유전 알고리즘과 같은 무작위 탐색법을 이용하며 하나의 해 군집에 대해 다수 객체들이 탐색하는 기법을 통하여 최적해 부분의 탐색성능을 높여 전체 모델의 학습성능을 개선하고자 한다. 제안된 기법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.197-199
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• 1998

A neuro-fuzzy based torque profile model of SRM with considerably improved accuracy is obtained using the measured data for training. The inferred torque profiles, which comprise magnetic non-linearities, represent the dynamic model of SRM. Then the reference torque signal with optimized waveform and switching angle are decided to control the torque directly. Hence, the presented scheme controls the torque in an instantaneous basis, allowing powerful torque control with minimum torque ripple even during the transient operation of the motor. Simulation and experimental results demonstrating the effectiveness of the proposed torque control scheme are presented.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.631-638
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• 2002

Stochastic model has been widely used for the forecasting of time series. However, this study tries to perform the precipitation forecasting by fuzzy time series model using fuzzy concept. The published fuzzy based models are used for the forecasting of time series and also we suggest that the combination of fuzzy time series models and neuro-fuzzy system can increase the forecastibility of the models. The precipitation time series in illinois, USA is analyzed for the forecasting by the known fuzzy time series models and the suggested methodology in this study. As a result, we know that the suggested methodology shows more exact results than the known models.

• Computers and Concrete
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• v.9 no.6
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• pp.469-486
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• 2012

This paper presents Neuro-Fuzzy (NF) based empirical modelling of torsional strength of RC beams for the first time in literature. The proposed model is based on fuzzy rules. The experimental database used for NF modelling is collected from the literature consisting of 76 RC beam tests. The input variables in the developed rule based on NF model are cross-sectional area of beams, dimensions of closed stirrups, spacing of stirrups, cross-sectional area of one-leg of closed stirrup, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. According to the selected variables, the formulated NFs were trained by using 60 of the 76 sample beams. Then, the method was tested with the other 16 sample beams. The accuracy rates were found to be about 96% for total set. The performance of accuracy of proposed NF model is furthermore compared with existing design codes by using the same database and found to be by far more accurate. The use of NF provided an alternative way for estimating the torsional strength of RC beams. The outcomes of this study are quite satisfactory which may serve NF approach to be widely used in further applications in the field of reinforced concrete structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.37-43
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• 2004

Expasion of computer network and rapid growth of Internet have made computer security very important. As one of the ways to deal with security risk, much research has been made on Intrusion Detection System(IDS). The paper, also, addresses the issue of intrusion detection, but especially with Neuro-Fuzzy model. By applying the fuzzy logic which is known to deal with uncertainty to Anomaly Intrusion, it not only overcomes the difficulty of Misuse Intrusion, but also ultimately aims to detect the intrusions yet to be known.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.31-36
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• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neno-fuzzy model for diagnosing the operation state of analyzer by using input and output signals of TMS to measure NOx and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.