• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 C
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• pp.1540-1542
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• 1994

This paper describes an experimental study of pattern recognition of partial discharge for three different discharge sources by using neural network(NN) system. The NN system is three layer feedforward connections and its learning method is a backpropagation algorithm incorporating an external teacher signal. Input information for NN is a statistical parameters of a discharge magnitude and the number of pulse count. After learning three typical input patterns, NN system offers good discrimination between different defects.

• 한국항해항만학회지
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• 제31권4호
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• pp.289-293
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• 2007

In this paper, new application of adaptive neural network to design a ship's Rudder-Roll Damping(RRD) control system is presented Firstly, the ANNAI neural network controller is presented. Secondly, new RRD control system using this neural network approach is developed. It uses two neural network controllers for heading control and roll damping control separately. Finally, Computer simulation of this RRD control system is carried out to compare with a linear quadratic optimal RRD control system; discussions and conclusions are provided. The simulation results show the feasibility of using ANNAI controller for RRD. Also, the necessity of mathematical ship model in designing RRD control system is removed by using NN control technique.

• 한국건설관리학회논문집
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• 제10권3호
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• pp.139-147
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• 2009

Accurate construction cost estimation in the initial stage of building project plays a key role for project success and for mitigation of disputes. Total construction cost(TCC) estimation of apartment projects in Vietnam has become more important because those projects increasingly rise in quantity with the urbanization and population growth. This paper presents the application of artificial neural networks(ANNs) in estimating TCC of apartment projects. Ninety-one questionnaires were collected to identify input variables. Fourteen data sets of completed apartment projects were obtained and processed for training and generalizing the neural network(NN). MATLAB software was used to train the NN. A program was constructed using Visual C++ in order to apply the neural network to realistic projects. The results suggest that this model is reasonable in predicting TCCs for apartment projects and reinforce the reliability of using neural networks to cost models. Although the proposed model is not validated in a rigorous way, the ANN-based model may be useful for both practitioners and researchers. It facilitates systematic predictions in early phases of construction projects. Practitioners are more proactive in estimating construction costs and making consistent decisions in initial phases of apartment projects. Researchers should benefit from exploring insights into its implementation in the real world. The findings are useful not only to researchers and practitioners in the Vietnam Construction Industry(VCI) but also to participants in other developing countries in South East Asia. Since Korea has emerged as the first largest foreign investor in Vietnam, the results of this study may be also useful to participants in Korea.

• 융합신호처리학회논문지
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• 제3권2호
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• pp.30-36
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• 2002

본 논문에서는 선형적응필터를 사용하고 있는 기존의 적응잡음제거 기 의 단점을 보완하기 위해 신경망 적응필터를 이용한 비선형 적응잡음제거기를 다루고 있다. 제안된 적응잡음제거기는 광대역 시변 잡음신호를 사용하여 잡음제거 성능을 조사하였으며 상대평가를 위해 TDL (tapped-delay -line) 선형필터의 적응잡음제거기와 비교하였다. 실험결과에 의하면 적응잡음 제거기의 주입력에 포함된 잡음과 기준입력 사이에 비선형적인 상관관계가 존재하는 경우 신경망 적응잡음제거기는 평균자승오차값을 기준으로 선형잡음제거기보다 더 우수한 성능을 보여주었으며, 또한 리커런트 신경망 적응필터가 순방향 신경망 필터보다 성능이 우수하였다. 따라서 적응잡음제거기에서 광대역 시변잡음을 제거하는데 신경망 적응필터가 선형 적응필터보다 효과적임을 확인하였다.

• 한국수자원학회논문집
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• 제36권4호
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• pp.633-645
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• 2003

GIS기법과 원격탐사 기법은 수문학의 지형자료 구축과 응용 분야에 활발하게 이용되고 있으며 다방면에서 많은 연구가 진행 중이다. 본 연구에서는 산악지역에서 토양 특성과 토지 피복 상태에 따라 유출 특성이 어떻게 나타나는지를 CN값을 산정하여 평가 하였다. 토지 피복 분류에 신경망 기법을 사용하여 보다 적합한 분류 방법을 모색하고자 했고, CN값 산정을 위한 연산에 GIS기법출 사용하였다. 우선 샘플지역을 선정하여 토지 피복의 정확도를 평가하면, 기존의 최우도법(80.9%)과 신경망 기법(84.1%)에서 신경망 기법 분류 결과가 상대적으로 우수하므로 신경망 기법으로 토지 피복을 분류하였다. 그리고 SCS방법으로 토양도를 이용하여 AMC-II 조건하에서 CN값을 산정하면 수작업 토지이용도는 55, 신경망 분류 토지 피복도는 57로 비슷한 결과로 나타났다. 이를 토대로 전체 유역에 대해서 신경망 기법으로 분류한 토지 피복도를 사용하여 CN값을 산정하여 적용함으로써 타당성을 증명했다. 앞으로 신경망 기법을 이용한 토지 피복 분류와 GIS기법의 적용으로 보다 정확하고 신속한 CN값 산정이 가능할 것으로 사료된다.

• 한국정보통신학회논문지
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• 제10권5호
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• pp.956-961
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• 2006

키스트로크 시간간격은 컴퓨터사용자의 검증 및 인식에서 분별적인 특징이 될 수 있다. 본 논문은 키스트로크 시간간격을 특징으로, 신경망의 역전파 알고리즘과 Bayesian 분류기, 그리고 k-NN을 이용한 분류기의 사용자 인식 성능을 비교 실험하였다. 실험 결과, 사용자당 샘플의 개수가 작을 경우에는 k-NN 알고리즘이 가장 성능이 좋았고, 사용자당 샘플의 개수가 많을 경우에는 Bayesian 분류기의 성능이 가장 뛰어난 결과를 보였다. 따라서 웹기반 온라인 사용자인식을 위해서는 사용자별 키스트로크 샘플의 수에 따라 k-NN이나 Bayesian 분류기를 선택적으로 사용하는 것이 바람직할 것으로 보인다.

• Journal of Power Electronics
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• 제2권1호
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• pp.46-54
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• 2002

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system (WECS) employing a permanent magnet synchronous generator is proposed. The permanent magnet generator (PMG) supplies a dc load via a bridge rectifier and two buck-boost converters. Adjusting the switching frequency of the first buck-boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck-boost converter allows output voltage regulation. The on-time of the switching devices of the two converters are supplied by the developed neural network (NN). The effect of sudden changes in wind speed and/ or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simulation with the developed neural network controllers. The results proved also the fast response and robustness of the proposed control system.

• Environmental Engineering Research
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• 제25권4호
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

• Structural Engineering and Mechanics
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• 제46권5호
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• pp.673-693
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• 2013

This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.