• 전자공학회논문지B
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• 제30B권1호
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.

• 전기전자학회논문지
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• 제20권3호
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• pp.303-306
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• 2016

본 논문에서는 GPGPU를 활용하여 Convolutional neural network의 가속화 방법을 제안한다. Convolutional neural network는 이미지의 특징 값을 학습하여 분류하는 neural network의 일종으로 대량의 데이터를 학습해야하는 영상 처리에 적합하다. 기존의 Convolutional neural network의 convolution layer는 다수의 곱셈 연산을 필요로 하여 임베디드 환경에서 실시간으로 동작하기에 어려움이 있다. 본 논문에서는 이러한 단점을 해결하기 위하여 winograd convolution 연산을 통하여 곱셈 연산을 줄이고 GPGPU의 SIMT 구조를 활용하여 convolution 연산을 병렬 처리한다. 실험은 ModelSim, TestDrive를 사용하여 진행하였고 실험 결과 기존의 convolution 연산보다 처리 시간이 약 17% 개선되었다.

• 컴퓨터교육학회논문지
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• 제16권2호
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• pp.39-48
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• 2013

본 연구에서는 신경망을 활용하여 청소년의 컴퓨터 오락추구 행동을 설명하는 예측모형을 조사하고자 하였다. 이를 위해 한국청소년 패널 조사(KYPS)의 중 2패널의 1차년도 데이터(총 3449명, 남: 1725명, 여: 1724명)를 대상으로 하여 신경망 모형(모형 1)을 구축하였다. 또한 신경망 모형의 성능을 분석하고자 로지스틱 회귀 분석을 실시하고 로지스틱 회귀 분석과의 보다 정확한 비교를 위해 동일한 변수를 입력데이터 값으로 하는 신경망 모형(모형 2)도 구축하여 세 모형의 예측율을 비교하였다. 그 결과, 신경망 모형 1이 가장 높은 분류적중율을 나타냈으며, 이 모형에 따라 성별, 컴퓨터사용시간, 가구월평균소득, 친한친구수, 비행친구수, 개인공부시간, 자기통제력, 사교육시간, 여가시간, 자기신뢰감, 스트레스, 학교적응, 공부고민 등의 변수들로 청소년의 컴퓨터 오락추구 행동을 예측하는 것이 보다 정확하고 효율적임을 제시하였다. 본 연구의 결과는 청소년의 컴퓨터 오락추구 행동을 예측하고 진단하거나 적절하게 조절 대처하는데 사용될 수 있음을 제언한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.23-30
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• 2022

Glaucoma is a chronic neuropathy that affects the optic nerve which can lead to blindness. The detection and prediction of glaucoma become possible using deep neural networks. However, the detection performance relies on the availability of a large number of data. Therefore, we propose different frameworks, including a hybrid of a generative adversarial network and a convolutional neural network to automate and increase the performance of glaucoma detection. The proposed frameworks are evaluated using five public glaucoma datasets. The framework which uses a Deconvolutional Generative Adversarial Network (DCGAN) and a DenseNet pre-trained model achieves 99.6%, 99.08%, 99.4%, 98.69%, and 92.95% of classification accuracy on RIMONE, Drishti-GS, ACRIMA, ORIGA-light, and HRF datasets respectively. Based on the experimental results and evaluation, the proposed framework closely competes with the state-of-the-art methods using the five public glaucoma datasets without requiring any manually preprocessing step.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.41-46
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• 2023

Because of the significant role that harakat plays in Arabic text, this paper used deep learning to extract Arabic text with its harakat from an image. Convolutional neural networks and recurrent neural network algorithms were applied to the dataset, which contained 110 images, each representing one word. The results showed the ability to extract some letters with harakat.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.39-40
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-gateway System)에서 하위의 수 천 개 이상의 센서로부터 데이터를 전송받는 각 게이트웨이의 작업부하(Workload)를 감소시키고 데이터 처리 속도를 향상시키기 위하여 신경망(Neural network) 알고리즘을 이용한 센서 데이터 할당 기법을 소개한다. 각 센서의 중요도에 따른 Weight와 측정 간격에 따른 Bias를 설정하고 학습과정을 통해 Output weight를 산출하여 데이터를 효율적으로 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.47-48
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• 2018

본 논문에서는 NP-hard 문제로 알려진 잡샵 스케쥴링에 대하여 강화학습적 측면에서 접근하는 방식에 대해 제안한다. 다양한 시간이 소요되는 업무들이 가지는 특징들을 최대한 state space aggregation에 고려하고, 이를 neural network를 통해 최적화 시간을 줄이는 방식이다. 잡샵 스케쥴링에 대한 솔루션은 미래에 대한 예측이 불가능하고 다양한 시간이 소요되는 스케쥴링 문제를 최적화하는 것에 대한 가능성을 제시할 것으로 기대된다.