• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.33.2-33.2
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• 2010

고층건물의 최상층 수평변위는 해당 건물의 안전성 및 사용성 평가에 중요한 지표가 된다 이러한 건물의 수평변위는 주로 풍하중에 기인한다 본 논문에서는 이러한 구조반응을 풍하중에 기인한 풍속데이터로부터 직접 추정하기 위해서 인공신경망(Artificial Neural Network, ANN)을 도입하였다 이에 대한 적용성을 판단하기 위해서 고층건물을 형상화한 모형테스트를 실시하고 풍향, 풍속, 변위 값을 얻었다. 이후 인공신경망에 적용시켜 실제 실험 데이터와의 비교를 통해 타당성을 검토하였다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.551-552
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• 2007

Increasingly nonlinear dynamic loads have been connected into power systems. This adds to the harmonics in the power system. In traditional load modeling techniques, the harmonics has not been considered. Thus, the harmonics problems in load modeling are considered and ANN load modeling is proposed.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.133-134
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• 2018

배터리의 효율적인 사용을 위해 배터리 관리 시스템(BMS)는 중요하다. 그 중 배터리의 잔존 수명을 나타내는 지표인 SOH(State of Health)를 예측하기 위해 본 논문에서는 18650 리튬이온 셀에 전기적 노화 실험(Cycle Life Test)을 적용하였다. 방전 용량 및 저항 변화에 의한 SOH 변화를 인공 신경망(Artificial Neural Network)을 사용하여 예측하도록 설계하고 이에 대한 검증을 수행하였다.

• Journal of the Korean Geotechnical Society
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• v.21 no.1
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• pp.15-27
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• 2005

In this paper, a method reasonably predicting soil water characteristic curve of domestic weathered granite soils was suggested, based on the test results obtained through experiments. In other words, a method to estimate the parameters of Fredlund and Xing's equation using an ANN (artificial neural network) was proposed. The particle size distribution, compacted water content and void ratio were used as input data in the ANN model for predicting the parameters, since it was found that these basic soil properties affect the parameters obtained from the test results and the fitting results of SWCC. The network model proposed in this study to obtain the parameters of Fredlund and Xing's SWCC equation produced reliable predictions, and the precision of the prediction results from the proposed method was high, in comparison with the prediction results of other methods.

• Journal of Korean Society of Steel Construction
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• v.16 no.4 s.71
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• pp.443-452
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• 2004

Recently, the Artificial Neural Network(ANN) which can organize complex non-linear problems by effectively applying the parallel computational model that is similar to the human brain, was adopted in the wide department of technology and resulted in many successful applications. In this study, a more appropriate formal method is suggested for the preliminary structural design stage controlled merely by the designer's experience and intuition. To do so, this study proposes a multi-level ANN according to the general progressive structural design procedure, using Back-Propagation Algorithm (BP) and Genetic Algorithm (GA) for the ANN learning. The preliminary structural design of cable-stayed bridges was applied to illustrate the applicability of the study formulated as stated above, and the results of two different learning methods were compared.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.1-13
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• 2010

The aim of this study is to analyze gold-silver mineral potential in the Taebaeksan mineralized district, Korea using a Geographic Information System(GIS) and an artificial neural network(ANN) model. A spatial database considering Au and Ag deposit, geology, fault structure and geochemical data of As, Cu, Mo, Ni, Pb and Zn was constructed for the study area using the GIS. The 46 Au and Ag mineral deposits were randomly divided into a training set to analyze mineral potential using ANN and a test set to verify mineral potential map. In the ANN model, training sets for areas with mineral deposits and without them were selected randomly from the lower 10% areas of the mineral potential index derived from existing mineral deposits using likelihood ratio. To support the reliability of the Au-Ag mineral potential map, some of rock samples were selected in the upper 5% areas of the mineral potential index without known deposits and analyzed for Au, Ag, As, Cu, Pb and Zn. As the result, No. 4 of sample exhibited more enrichments of all elements than the others.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.359-373
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• 2019

Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.583-592
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• 2018

Recently, concern has arisen regarding the lowering of groundwater levels in the hinterland caused by the development of high-capacity radial collector wells in riverbank filtration areas. In this study, groundwater levels are estimated using Modflow software in relation to the water volume pumped by the radial collector well in Anseongcheon Stream. Using the water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level. We estimate that increasing the pumping rate of the horizontal well HW-6, which is drilled parallel to the stream direction, is necessary to minimize the reduction of groundwater levels in wells OW-7 and OB-11. We also note that the number of input data and the classification of training and test data affect the results of the ANN model. This type of approach, which supplements ANN modeling with observed data, should contribute to the future groundwater management of hinterland areas.