• 상하수도학회지
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• 제19권6호
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• 한국지반공학회논문집
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• 제24권10호
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• pp.57-70
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• 2008

피에조콘 관입시험의 장점은 연속적인 데이터의 취득이 보장되며 결국 대상지반의 신뢰성 있는 분석이 가능하다는 점이다. 따라서 지난 수십년간 국내외에서 콘 관입시험결과로부터 흙분류를 수행하는 많은 연구가 진행되었으며 차트나 도표 등의 형태로 흙분류 방법들이 제안되었다. 그러나 대부분의 차트 또는 방법들은 한국을 제외한 세계 각국의 자료들을 바탕으로 제안되어 국내 지반의 적용성에 대한 검증이 이루어져야 한다. 뿐만 아니라 기존 방법들에서는 사용된 입력자료에 따라 흙분류 결과가 상이한 경우가 있어 적용과 판단에 어려움이 있다. 그러나 불행히도 이러한 차트 형태로 제안된 기존 도표의 경우 지역성 등이 반영되어 수정 또는 보완이 필요하나 수정에 어려움이 있거나 거의 불가능하다. 이에 본 연구에서는 국내 17개 현장에서 수행된 피에조콘 관입시험결과와 채취된 시료에 대한 주상도 및 흙분류결과를 바탕으로 클러스터링 기법과 뉴로-퍼지 이론을 이용한 흙분류 모델을 제안하였다. 제안된 모델을 검증하기 위해 모델 학습 시 사용되지 않는 새로운 피에조콘 관입시험 데이터에 대한 흙분류 결과를 실제 시추결과와 비교하였다. 또한 기존의 소프트컴퓨팅 모델과 Robertson 방법에 의한 흙분류 결과와 제안된 모델의 흙분류 결과를 비교하여 제안된 모델의 효율성을 검토하였다.

• 한국응용과학기술학회지
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• 제25권4호
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• pp.438-445
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• 2008

For recipe prediction of colorant proportion showing nonlinear behavior, we modeled the effects of colorant proportion of basic colors on the target colors and predicted colorant proportion necessary for making target colors. First, colorant proportion of basic colors and color information indicated by the instrument was applied by a linear model and a multi-layer perceptrons model with back-propagation learning method. However, satisfactory results were not obtained because of nonlinear property of colors. Thus, in this study the neuro-fuzzy model with merit of artificial neural networks and fuzzy systems was presented. The proposed model was trained with test data and colorant proportion was predicted. The effectiveness of the proposed model was verified by evaluation of color difference(${\Delta}E$).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 A
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전기물성,응용부문
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• pp.224-226
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• 2006

In this paper, we have studied for analysis of the partial discharge(PD) signal in power transmission line. The PD signal has estimated as detected signal accumulation of a PRPDA method by using Labview, and analyzed with neuro-fuzzy algorithm. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for Hipotronics Company's 22.9kV or 154kV setup has generated and then has applied with 18kV,20kV with 1:1 time probe. It's also used the LDPE O.27mmt (scratch error O.05mmt) to sample for making PD. Our new class of PD detected algorithm has also compared with previous PRPDA or Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• 대한인간공학회지
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• 제19권2호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1990-1992
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• 2001

In this paper, a NeuroFuzzy controller (NFC) with enhanced packet reservation multiple access (PRMA) protocol for QoS-guaranteed multimedia communication systems is proposed. The enhanced PRMA protocol adopts mini-slot technique for reducing contention cost, and these minislot are futher partitioned into multiple MAC regions for access requests coming from users with their respective QoS (quality-of-service) requirements. And NFC is designed to properly determine the MAC regions and access probability for enhancing the PRMA efficiency under QoS constraint. It mainly contains voice traffic estimator including the slot information estimator with recurrent neural networks (RNNs) using real-time recurrent learning (RTRL), and fuzzy logic controller with Mandani- and Sugeno-type of fuzzy rules. Simulation results show that the enhanced PRMA protocol with NFC can guarantee QoS requirements for all traffic loads and further achieves higher system utilization and less non real-time packet delay, compared to previously studied PRMA, IPRMA, SIR, HAR, and F2RAC.

• 전기학회논문지
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• 제57권1호
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• pp.92-97
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• 2008

This is a research to apply 1310nm single-mode optical fiber to a temperature sensor. The existing study of optical fiber sensor is complicated because it was made with various equipment. To vary scattering, the variation of optical frequency is measured by using Bragg(lattice) or pulse generator and also bulk system is created by YAG laser but there were some difficulties creating experimental environment and it was a problem that the stability of measured data was low. The temperature sensor system using the suggested sBs(stimulated Brillouin scattering:sBs) from this research is much more simplified straight-line system. To improve the trust and accuracy of noises from optical frequency and unclear results, it was analysed by using Neuro-Fuzzy algorithm. we tried to get more correct data than existing system. sBs measure that optical frequency changed due to the variation of temperature. The analyzed change rate of outcome by Fuzzy theory is 1.1 MHz.

• ETRI Journal
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• 제31권2호
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• pp.182-192
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• 2009

Rain attenuation and intercell interference are two crucial factors in the performance of broadband wireless access networks such as local multipoint distribution systems (LMDS) operating at frequencies above 20 GHz. Power control can enhance the performance of downlink CDMA-based LMDS systems by reducing intercell interference under clear sky conditions; however, it may damage system performance under rainy conditions. To ensure robust operation under both clear sky and rainy conditions, we propose a novel power-control scheme which applies an adaptive neuro-fuzzy inference system (ANFIS) for downlink CDMA-based LMDS systems. In the proposed system, the rain rate and the number of users are two inputs of the fuzzy inference system, and output is defined as channel quality, which is applied in the power control scheme to adjust the power control region. Moreover, ITU-R P.530 is employed to estimate the rain attenuation. The influence of the rain rate and the number of users on the distance-based power control (DBPC) scheme is included in the simulation model as the training database. Simulation results indicate that the proposed scheme improves the throughput of the DBPC scheme.