• 전자공학회논문지B
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• 제30B권2호
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.125-130
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• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1377-1382
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• 1990

In Multi-Layer Perceptron (MLP) which realizes continuous mappings, the output errors is directly affected by the weight errors which may be caused by the limited precision of digital or analog hardware in implementations. So, it is important to study the sensitivity due to the perturbation of connection weights between neurons. In this paper, we derive a sensitivity function to the statistical weight perturbations in MLP with differentiable activation functions. This sensitivity function can be regarded as an ensemble average of deterministic sensitivity measures due to the perturbations of weights. Hence, this sensitivity function can be used as the criteria for selecting weights with the minimum sensitivity among possible sets of connection weights in MLP. For the verification of the validity of the proposed sensitivity function, computer simulations have been performed and through the simulations we find good agreement between the theoretical and simulation results.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1110-1118
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• 1995

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2017년도 제55차 동계학술대회논문집 25권1호
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• pp.19-20
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• 2017

인공지능이 사회적으로 대두되면서 많은 양의 관련 연구가 시작되고 있다. 본 논문에서는 다층 퍼셉트론 신경망에서 역전파 학습의 진행 과정을 시각화 하는 것을 목표로 하고 있다. 다층 퍼셉트론 신경망은 학습의 진행 과정과 그 방식은 잘 알려져 있으나 각 신경의 값이 어떻게 변화되어 가는 지는 눈에 보이지 않는다. 이러한 과정에 대해 시각화를 통해 값이 변하는 과정을 눈으로 쉽게 관찰할 수 있도록 하는 것이 이 논문의 목표이다. 본 연구결과는 향후 다층 퍼셉트론 신경망을 기반으로 하는 다른 모델의 시각화에 대한 기초자료로 활용될 수 있을 것이다.

• 제어로봇시스템학회논문지
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• 제4권6호
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• pp.795-801
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• 1998

In this paper, we propose a machine vision algorithm for inspecting inserts which are used for milling and turning operations. Major defects of the inserts are breakage and crack on insert surfaces. Among the defects, breakages on the face of the inserts can be detected through three stages of the algorithm developed in this paper. In the first stage, a multi-layer perceptron is used to recognize the inserts being inspected. Edge detection of the insert image is performed in the second stage. Finally, in the third stage breakages on the insert face are identified using Hough transform. The overall algorithm is tested on real specimens and the results show that the algorithm works fairly well.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.345-351
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• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• Journal of Electrical Engineering and Technology
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• 제5권3호
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3658-3679
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• 2022

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.