• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.103-110
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• 2000

Shaft encoder which encodes the rotational angle of a shaft becomes more important recently due to factory automation and office automation. Although an absolute type encoder is more dsirable due to its convenience an incremental encoder is commonly used because of its cost and technical difficulties Fabricating a high resolution absolute encoder is very diff-cult because the physical size is limited by currently available technology. In order to overcome this difficulty Moire fringe can be used incorporated with gray code. In order to measure the position of fringes which move as the code disk rotates a neural network was developed in this paper. Formerly fringe position is usually measured by a sophisticated software which needs a little long calculation time. However using nerual network method can eliminate such calculation time even though it needs learning job The pro-posed method is verified through several experiments.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.201-209
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• 2017

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.36-43
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• 1992

In this paper, a robot assembly wrist, which is able to assemble chamferless parts, has been developed. The RCC (Remote Center Compliance) structure is used as a basic structure. 5 position sensors and 4 pneumatic actuators are installed additionally to measure the deformation of RCC structure and correct the errors actively. Due to the restricted direction of actuation, a decision rule which selects the suitable actuator according to the position sensor signals is needed. For this purpose, a neural network is used and it is experimentally shown that the nerual network overcomes system's nonlinearity. This paper presents fundamental experiment results for the insertion of parts with several clearance.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.11
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• pp.95-103
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• 1997

A robust center estimation tecnique of n-fold engineering parts is presented, which use self-organizing neural networks with generating and merging learning for training neural units. To estimate the center of the n-fold engineering parts using neural networks, the segmented boundaries of the interested part are approximated to strainght lines, and the temporal estimated centers by thecosine theorem which formed between the approximaged straight line and the reference point, , are indexed as (.sigma.-.theta.) parameteric vecstors. Then the entries of parametric vectors are fed into self-organizing nerual network. Finally, the center of the n-fold part is extracted by mean of generating and merging learning of the neurons. To accelerate the learning process, neural network uses an adaptive learning rate function to the merging process and a self-adjusting activation to generating process. Simulation results show that the centers of n-fold engineering parts are effectively estimated by proposed technique, though not knowing the error distribution of estimated centers and having less information of boundaries.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.95-110
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• 1995

n this research neural -based model was developed to forecast link travel times , And it is also compared wiht other time series forecasting models such as Box-Jenkins model, Kalman filter model. These models are validated to evaluate the accuracy of models with real time series data gathered by the license plate method. Neural network's convergency and generalization were investigated by modifying learning rate, momentum term and the number of hidden layer units. Through this experiment, the optimum configuration of the nerual network architecture was determined. Optimumlearining rate, momentum term and the number of hidden layer units hsow 0.3, 0.5, 13 respectively. It may be applied to DRGS(dynamic route guidance system) with a minor modification. The methods are suggested at the condlusion of this paper, And there is no doubt that this neural -based model can be applied to many other itme series forecating problem such as populationforecasting vehicel volume forecasting et .

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.671-672
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• 2020

VVC(Versertile Video Codec)의 화면 내 예측은 인코더에서 영상을 적절하게 사각형 블록으로 분할하고, 블록 주변의 먼저 재구성된 참조샘플들을 이용하여 예측블록을 형성한다. 인코더는 화면 내 예측 모드에서 각 PU(Prediction Unit)에 대하여 MIP(Matrix-based weighted Intra Prediction) 적용 여부, MIP에서 matrix의 인덱스, MRL(Multi Reference Line)의 인덱스, DC/Planar/Angular 모드에 대한 최적모드를 고려하여 각 정보를 디코더로 전송하며 각 후보모드들의 압축효율을 비교하는 과정에서 높은 연산량을 요구한다. 본 논문에서는 이러한 모드 결정은 원본영상으로도 대략적인 결정이 가능하다는 전제를 가지고 NN(Nueral Netwrok)의 일종인 CNN(Convolutional Nerual Network)를 이용하여 복잡한 모드 결정 방법을 생략하는 방법을 제안한다.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.419-427
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• 1999

We propose an algorithm for obtaining the optimal node number of hidden units in dynamic neural networks. The dynamic nerual networks comprise of dynamic neural units and neural processor consisting of two dynamic neural units; one functioning as an excitatory neuron and the other as an inhibitory neuron. Starting out with basic network structure to solve the problem of control, we find optimal neural structure by multiplication and combining dynamic neural unit. Numerical examples are presented for nonlinear systems. Those case studies showed that the proposed is useful is practical sense.