• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.233-238
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• 2023

This study presents a method for identifying partial discharge defects in an eco-friendly gas insulated system using a backpropagation algorithm. Four partial discharge (PD) electrode systems, namely, a free-moving particle, protrusion on the conductor, protrusion on the enclosure, and voids, were designed to simulate PD defects that can occur during the operation of eco-friendly gas-insulated switchgear. The PD signals were measured using an ultrahigh-frequency sensor as a nonconventional method based on IEC 62478. To identify the types of PD defects, the PD parameters of single PD pulses in the time and frequency domains and the phase-resolved partial discharge patterns were extracted, and a back-propagation algorithm in the artificial neural network was designed using a virtual instrument based on LabVIEW. The backpropagation algorithm proposed in this paper has an accuracy rate of over 90% for identifying the types of PD defects, and the result is expected to be used as a reference database for asset management and maintenance work for eco-friendly gas-insulated power equipment.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1998년도 학술발표대회 논문집 제17권 1호
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• pp.357-360
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• 1998

DS/CDMA system rejected narrow-band interference and additional White Gaussian noise which are occured at multipath, intentional jammer and multiuser to share same bandwidth in mobile communication systems. Because of having not sufficiently obtained processing gain which is related to system performance, they were not effectively suppressed. In this paper, an matched filter channel model using backpropagation neural network based on complex multilayer perceptron is presented for suppressing interference of narrow-band of direct sequence spread spectrum receiver in DS/CDMA mobile communication systems. Recursive least square backpropagation algorithm with backpropagation error is used for fast convergence and better performance in matched filter receiver scheme. According to signal noise ratio and transmission power ratio, computer simulation results show that bit error ratio of matched filter using backpropagation neural network improved than that of RAKE receiver of direct sequence spread spectrum considering of con-channel and narrow-band interference.

• 한국정보통신학회논문지
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• 제11권6호
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• pp.1170-1175
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• 2007

본 논문에서는 신경회로망 알고리즘 중 하나인 backpropagation network을 이용한 악보인식 기법과 그에 필요한 악보 영상에 대한 전처리 기법을 제안한다. 전처리과정으로 이진화, 기울기 보정, 오선제거 등의 과정을 수행하여 인식에 필요한 음악 기호와 음표를 분리한다. 분리된 음악 기호와 음표들은 backpropagation 알고리즘을 사용하여 구성된 음표 인식 신경망과 비음표 인식 신경망을 통해 각각 음표와 비음표 인식과정을 거친다. 다양한 복잡도를 가진 악보를 대상으로 한 실험 및 분석 결과를 통해 제안한 악보 인식 기법의 정확도를 기술하였다.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1017-1020
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• 1999

Recently, a new learning algorithm for multilayer neural networks has been proposed 〔1〕. In the new learning algorithm, each output neuron is considered as a function of weights and the weights are adjusted so that the output neurons produce desired outputs. And the adjustment is accomplished by taking gradients. However, the gradient computation was performed numerically, resulting in a long computation time. In this paper, we derive the all necessary equations so that the gradient computation is performed analytically, resulting in a much faster learning time comparable to the backpropagation. Since the weight adjustments are accomplished by summing the gradients of the output neurons, we will call the new learning algorithm “multi-gradient.” Experiments show that the multi-gradient consistently outperforms the backpropagation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.731-734
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• 2009

본 논문에서는 차량을 포함하고 있는 영상에서 차량의 색상을 인식하는 방법을 제안한다. 영상에서 차량의 색상 특징 벡터를 추출해 다층 신경회로망인 backpropagation 학습 알고리즘을 이용하여 차량의 색상을 인식하게 된다. backpropagation 학습 알고리즘의 입력으로 사용되는 특징벡터는 RGB와 HSI(Hue-Saturation-Intensity) 색상 모델을 이용하여 색상 특징 벡터를 구성하고 각각 신경회로망의 입력으로 사용된다. 차량의 색상 인식은 가장 많이 발견되는 차량의 색상 가운데 7가지 색상으로 흰색, 은색, 검정색, 빨강색, 노란색, 파란색, 초록색으로 인식한다. 제안한 방법의 성능평가를 위해 차량을 포함하고 있는 영상을 이용하여 색상 인식 성능을 실험 하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• 경영과정보연구
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• 제2권
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• pp.75-93
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• 1998

In this paper, we propose a new algorithm(N_BP) to be capable of overcoming limitations of the traditional backpropagation(O_BP). The N_BP is based on the method of conjugate gradients and calculates learning parameters through the line search which may be characterized by order statistics and golden section. Experimental results showed that the N_BP was definitely superior to the O_BP with and without a stochastic term in terms of accuracy and rate of convergence and might surmount the problem of local minima. Furthermore, they confirmed us that the stagnant phenomenon of learning in the O_BP resulted from the limitations of its algorithm in itself and that unessential approaches would never cured it of this phenomenon.