• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.322-325
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• 2007

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms. (c) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.719-722
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• 2016

운전자의 주의력 감쇠는 교통사고 요인에 있어서 큰 비중을 차지한다. 주의력 감쇠는 무선 통화, 기기 조작, 졸음으로 나타날 수 있는데 자동차 대형사고의 대부분은 졸음운전으로 인하여 일어나며, 졸음운전 시에는 운전자의 운전조작 및 방어 조작 능력이 현저하게 저하한다. 본 시스템은 카메라로부터 실시간으로 영상 데이터를 입력 받아 처리하여 운전자의 졸음 상태를 인식하는 시스템으로 운전자에게 졸음방지 기능을 제공한다. Haar-Like Feature cascade classifier 방법을 사용하여 얼굴 및 눈 영역 검출을 하였고 Open Eye, Closed Eye가 학습된 MLP(Multi-Layer Perceptron)를 이용해 눈 깜박임을 인식하여 PERCLOS(Percentage of Eye Close)방법으로 졸음을 판단하였다. 본 논문에서 제안한 방법의 인식률의 정확도를 검증하기 위해 인식률 테스트를 하였다.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1346-1349
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• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.135-148
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• 1995

Neural Networks have been advocated as an alternative to statistical forecasting methods. However, the empirical evidences are not consistent. In the present experiments, multi-layered perceptron (MLP) are adopted as approximator to the time series generating processes. To prevent the MLP from being overfitted to the given time series, the information obtained from ARMA modeling is used to determine the architecture of MLP. The proposed approach was tested empirically using the subsamples of the 111 time series used in the first Markridakis Competition. The forecasting results were analyzed to find out the factors that affect the performance of MLP. The experimental results show that the proposed approach outperforms ARMA models in terms of fitting and forecasting accuracy. In addition, it is found that the use of deseasonalized data improves the forecasting accuracy of MLP.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.109-116
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• 1998

In this paper, a back propagation neural network teaming algorithm based on the complex multilyer perceptron is represented for suppressing narrowband interference of the received signals in DS-SS mobile communication system. We proposed neural network adaptive correlator(NNAC) which has fast convergence rate and good performance with combining back propagation neural network and the receiver of DS-SS. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow-band interference and the co-channel interference.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.173-176
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• 2000

음성은 인간이 가진 가장 편리한 제어전송수단으로 이를 통한 제어는 인간에게 많은 편리함을 제공할 것이다. 본 논문에서는 다층구조 신경망(Multi-Layer Perceptron)을 이용하여 간단한 음성인식 명령처리시스템을 Matlab 상에서 구성해 보았다. 음성인식을 통한 제어의 목적을 위해 화자종속, 고립단어인식기를 목표로 설정하여 연구를 수행하였다. 음성의 시작점과 끝점을 검출하기 위해 단구간 에너지와 영교차율(ZCR)을 이용하였고 인식기의 특징파라미터로는 12차 LPC켑스트럼 계수를 사용하였다. 그리고 신경망의 출력값을 기동, 정지시에 활성화되도록 3개의 계층으로 하였고, 신경망의 뉴런의 개수를 각각 12, 12, 2으로 설정하였다. 먼저 기준음성패턴으로 학습시킨 후에 Matlab 환경하에 동작하는 dSPACE 실시간처리보드에 변환된 C프로그램을 다운로드하고, 음성을 입력하여 인식 후 dSPACE보드의 D/A컨버터의 출력단에 연결된 DC모터를 기동, 정지제어를 수행하였다. 실시간 음성인식 명령처리 시스템 구현을 통하여 원격제어와 같은 음성명령을 통한 제어가 가능함을 확인할 수 있었다.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.65-68
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• 1996

Neural networks are increasingly being used to enhance the classification and recognition powers of data collected from sensor array. This papers reports the effectiveness of multilayer perceptron network based on back-propagation algorithm combined with the outputs from "Electronic Nose" using electrically conducting polymers as sensor materials. Robust performance and classification results are produced with preprocessing method.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.208-210
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• 2004

최근 인터넷, 인트라넷과 같은 통신 기술 발전에 따라 거의 모든 시스템이 서로 연결되었고, 사용자들은 손쉽게 정보를 공유할 수 있게 되었다. 따라서 시스템 침입을 통한 데이터의 변형과 인증 받지 않은 접근과 같은 컴퓨터 범죄가 급속도로 증가하고 있다. 그러므로 이러한 컴퓨터 범죄를 막기 위한 침입 탐지 기술 개발은 매우 중요하다. 전통적인 침입 탐지 모델은 단지 네트워크 패킷 데이터만을 사용하고 있으며. 침입탐지 시스템의 성능을 높이기 위해 서로 다른 분류 알고리즘을 결합하는 방법을 사용해왔다. 그러나 이러한 모델은 일반적으로 성능향상에 있어서 제한적이다. 본 논문에서는 침입탐지 시스템의 성능을 개선하기 위해 네트워크 데이터와 시스템 콜 데이터를 융합하는 방법을 제안하였으며. 데이터 융합 모델로서 Multi-Layer Perceptron (MLP)를 사용하였다. 그리고 DARPA 에서 생성한 네트워크 데이터와 본 논문에서 가상으로 생성한 시스템 콜 데이터를 함께 결합하여 모델을 생성 한 뒤 실험을 수행하였다. 본 논문에서의 실험결과로. 단순히 네트워크 데이터만을 사용한 모델에 비해 시스템 콜 데이터를 함께 결합한 모델이 훨씬 더 놓은 인식률을 보인다는 것을 확인할 수 있다

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.542-546
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• 2009

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow - band interference and the co-channel interference.