• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.258-263
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• 1995

본 논문은 일반화된 다중 수상돌기 적 (GMDP : Generalized Multi Dendrite Product) 유닛트 신경망을 이용한 PID 적응 위치제어기를 구성하여 직류 서어보 전동기의 위치제어를 실시간 처리 하였다. 제안한 제어기를 위치제어에 적용시켜 실험한 결과 기존의 MLP 신경망 제어기를 이용한 것 보다도 샘플시간을 줄일 수 있다는 장점으로 정밀한 제어 가 가능하다는 것을 확인할 수 있었다. 학습규칙은 기존의 역전파 학습방법이 GMDP 신경 회로망에 적용되었다.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1801-1802
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• 2007

자기 연관 시계열 신경망을 이용하여 식각종말점 패턴-기반 플라즈마 상태를 진단하는 방법을 제안한다. 식각종말점 패턴은 Oxide 박막의 식각공정 중 Optical Emission Spectroscopy를 이용하여 수집하였으며, 역전파 신경망을 이용하여 진단 모델을 개발하였다. 진단 모델은 단일 신경망과 모듈러신경망을 이용하여 개발하였으며, 비교평가결과 모듈러 신경망이더 우수한 성능을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1282-1285
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• 2022

기존의 video domain adaptation은 closed set 환경에서 주로 연구되었다. 하지만 이는 source와 target의 label이 같다는 비현실적인 전제를 요구한다. 따라서 본 논문에서는 target의 label space가 source보다 넓은 open set video domain adaptation 문제를 다룬다. 우린 open set image domain adaptation에서 사용되는 방법들을 video로 확장 시켜 모델을 설계하고 UCF to HMDB, HMDB to UCF 와 같은 video dataset에서 실험하였다. 그 결과 source only 대비 UCF to HMDB에서 12%, HMDB to UCF 7.1% 향상된 결과를 얻었다.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.2
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• pp.1-7
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• 2011

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.499-505
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• 2010

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.522-530
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• 2002

This paper introduces a neural network (NN) -based nonlinear predictor for the LP (Linear Prediction) residual. To evaluate the effectiveness of the NN-based nonlinear predictor for LP-residual, we first compared the average prediction gain of the linear long-term predictor with that of the NN-based nonlinear long-term predictor. Then, the effects on the quantization noise of the nonlinear prediction residuals were investigated for the NN-based nonlinear predictor A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Experimental results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.113-118
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• 2013

This study will suggest methods for a license plate recognition system that is suitable for license plate identification, separation of letters, and recognition of letters in order to recognize a licence plate efficiently. The suggested algorithm had tested a recognition system that onlyused backpropagation, a recognition system that used only SVM, and the suggested recognition system in order to prove efficiency. As a result, recognition rate had increased from the minimum 7.9% to the maximum12.2% as the case of using back propagation recognized the number platefor 87.9%, the case of using SVM for 91.4%, and the suggested had 98.6% of recognition rate.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.45-57
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• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.705-711
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• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.809-818
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• 2007

In this thesis, the color information is used to extract the traffic sign territory, and for recognizing the extracted image, it proposes the traffic sign recognition system that applies the error back propagation algorithm. The proposed method analyzes the color of traffic sign to extract and recognize the possible territory of traffic sign. The method of extracting the possible territory is to use the characteristics of YUV, YIQ, and CMYK color space from the RGB color space. Morphology uses the geometric characteristics of traffic sign to make the image segmentation. The recognition of traffic signs can be recognized by using the error back propagation algorithm. As a result of the experiment, the proposed system has proven its outstanding capability in extraction and recognition of candidate territory without the influence of differences in lighting and input image in various sizes.