• 한국지능시스템학회논문지
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• 제10권4호
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• pp.310-314
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• 2000

본 논문에서는 선형 이진 신경회로망 (Linear Binary neural Network)을 이용하여 이진 영상으로부터 골격(skeleton)을 추출하는 병렬 구조를 제안하였다. 기존의 골격 추출 알고리즘으로부터 이진함수를 추출하고 이를 MSP Term Grouping Algorithm을 이용하여 학습시겼다. 결과에서는 기존의 역전과 (Back-propagation) 학습알고리즘을 사용한 신경회로망보다 더 쉽게 하드웨어로 구현할 수 있음을 보여준다.

• Journal of Power Electronics
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• 제13권1호
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• pp.139-160
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• 2013

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve $L_2$ tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

• International Journal of Ocean System Engineering
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• 제3권3호
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• 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.

• 한국통신학회논문지
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• 제25권3A호
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• pp.375-382
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• 2000

Equalization of satellite communication using Complex-Bilinear Recurrent Neural Network(C-BLRNN) is proposed in this pater. Since the BLRNN is based on the bilinear polynomial and it has been more effectively used in modeling highly nonlinear systems with time-series characteristics than multi-layer perception type neural networks(MLPNN) , it can be applied to satellite equalizer. the proposed C-BLRNN based equalizer for M-PSK with a channel model is compared with Volterra filter Equalizer, DFE, and conventional Complex MLPNN Equlizer. The results show that the proposed C-BLRNN based equalizer gives very favorable results in both of MSE and BER criteria over other equalizers.

• 유공압시스템학회논문집
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• 제1권1호
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• pp.1-9
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• 2004

This paper addresses an approximation and tracking control of recurrent neural networks(RNN) using two-dimensional iterative learning algorithm for an electro-hydraulic servo system. And two dimensional learning rule is driven in the discrete system which consists of nonlinear output function and linear input. In order to control the trajectory of position, two RNN's with the same network architecture were used. Simulation results show that two RNN's using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RNN was very effective to control trajectory tracking of electro-hydraulic servo system.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권7호
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• pp.440-445
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• 2017

전자상거래 발전에 따라 온라인 쇼핑을 이용하는 사람들이 증가하였고 제품 또한 다양해지고 있다. 이러한 추세로 구매자가 만족할 수 있는 정확한 추천시스템의 중요성이 증대되었으며 정확도를 높이기 위한 새로운 방법의 연구가 계속되고 있다. 순환신경망은 시퀀스 학습에 적합한 딥 러닝 방법 중 하나이며 본 연구에서는 추천시스템의 정확도를 높이는 방법으로 구매자의 제품 접근순서를 순환신경망에 적용하여 알고리즘 성능평가를 하였다. 알고리즘 성능평가에는 대표적인 순환신경망 알고리즘과 최적화 알고리즘으로 진행하였다. 순환신경망 알고리즘으로는 RNN, LSTM, GRU 그리고 최적화 알고리즘으로는 Adagrad, RMSProp, Adam optimizer를 사용하였다. 실험 도구로는 구글의 오픈소스 라이브러리인 텐서플로우를 사용하였고 데이터는 RecSys Challenge 2015에서 제공하는 e-commerce session 데이터를 활용하였다. 실험 결과 실험 데이터에 적합한 최적의 하이퍼파라미터를 발굴하고 적용하여 RecSys Challenge 2015 참가자들의 결과와 비교하였다. 상품 접근 순서만을 학습시킨 결과이기 때문에 등수가 높지는 않았지만 기존 추천시스템에 접목한다면 정확도 향상에 기여할 수 있을 것으로 보인다.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.6-10
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• 2021

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.436-442
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• 2017

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.