• 한국융합학회논문지
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• 제11권9호
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• pp.297-303
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• 2020

본 연구의 목적은 청소년기에서 성인기에 해당하는 암컷 생쥐를 대상으로 유산소 운동과 유산균 섭취가 운동 학습능력과 체중에 미치는 영향을 파악하는 것이다. 실험대상을 비운동, 중강도, 고강도 운동과 유산균 섭취, 비섭취 변인의 6집단으로 나누고 4주간 운동강도별 트레드밀과 유산균으로 처치하였다. 처치 전 후로 버티컬그리드 테스트를 수행하여 운동학습능력과 체중을 평가하였다. 버티컬그리드 테스트에서는 유산균을 섭취하고, 고강도 운동을 수행한 집단의 상행·회전·하행 속도가 가장 빨랐으며 운동을 하지 않은 비유산균집단과 유의한 차이를 보였다(p<.001). 운동을 하지 않은 비유산균집단이 가장 느린 수행 속도를 기록했다. 또한, 운동 수행과 유산균 섭취를 함께한 집단이 운동만 수행한 집단에 비해 빠른 수행 속도를 기록하는 경향을 보였다. 체중 변화를 비교한 결과 중강도 운동만 수행한 집단의 체중 증가는 운동을 수행하지 않은 비유산균집단의 체중 증가에 비해 유의하게 높았다(p=.032). 종합하면, 성장기의 유산소 운동은 운동학습 향상에 도움을 줄 수 있으며, 유산균 섭취와 병행하면 보다 효율적인 운동학습이 이루어질 수 있다.

• 한국컴퓨터산업학회논문지
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• 제8권3호
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• pp.147-154
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• 2007

각 급 학교에서는 학습이 끝난 후에 실시하는 총괄평가의 결과만으로 학습평가를 하고 있는데 이러한 평가 방식은 학습자의 학습능력의 형성과정을 고려하지 않는 결과위주의 학습평가로 볼 수 있다. 또 기존의 학습평가는 학습 수행능력을 판정하기 위한 진단평가와 학습능력의 향상 정도를 측정하기 위한 형성평가를 각기 개별적으로 수행하여 평가하기 때문에 학습 수행능력을 보다 명확하게 처리하기 곤란한 점이 있다. 따라서 본 논문에서는 학습자의 능력을 보다 객관적으로 평가하기 위한 방안으로 퍼지 추론을 이용하여 진단평가와 형성평가를 통합 평가할 수 있는 학습평가 방법을 제안한다.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• Journal of Welding and Joining
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• 제17권5호
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• pp.98-106
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• 1999

In GMAW, the spatters are generated according to the variation of the arc. Of the arc is stable, Few spatters are generated. But if unstable, too many spatters are generated. So, this means the spatters are dependent on the arc state. The aim of this study is to accurately estimate the arc state. To do this, the generated spatters were captured under the some welding conditions, and the waveforms of the arc voltage and welding current were collected. From the collected signals, the waveform factors and their standard deviations were extracted. Using these factors as input parameters of multi-layer artificial neural network, the learning for the weight of the generated spatters is performed and the estimation results to the real spatter are assessed. Obtained results are as follow: the linear correlation coefficient between the estimated result and the real spatters was 0.9986. And although the average convergence error was set 0.002, the estimated error to the real spatter was within 0.1 gr/min at each welding condition. In the estimation for the weight generated spatters, the result with multi-layer neural network was far better than with multiple regression analysis. Especially, even though under the welding condition which the arc state is unstable (the spatter is generated much more), very excellent estimation performance was shown.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• 한국컴퓨터정보학회논문지
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• 제14권7호
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• pp.9-15
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• 2009

퍼지 가중치 평균 분류기는 가중치를 적절히 설정함으로써 뛰어난 분류 성능을 얻을 수 있다는 장점이 있다. 그러나 일반적으로 가중치는 인식 문제 분야의 특성이나 해당 전문가의 지식과 주관적 경험을 기반으로 설정되므로 설정된 가중치의 일관성과 객관성을 보장하기가 어려운 문제점을 갖고 있다. 따라서 이 논문에서는 퍼지 가중치 평균 분류기의 가중치를 설정하기 위한 객관적 기준을 제시하기 위하여 특정값들 간의 통계적 정보를 이용한 가중치 설정 기법들을 제안하였다. 제안한 기법들의 효과를 조사하기 위하여 UCI machine learning repository 사이트에서 제공되는 표준 데이터들 중의 하나인 Iris 데이터 세트를 이용하여 실험하였으며, 그 결과 우수한 성능을 확인 할 수 있었다.

• 전자공학회논문지B
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• 제33B권11호
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• pp.159-166
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• 1996

In this paper, the method for improving the speed of convergence and learning rate of back propagation algorithms is proposed which update the learning rate parameter and momentum term for each weight by generated error, changely the output layer of neural network generates a high value in the case that output value is far from the desired values, and genrates a low value in the opposite case this method decreases the iteration number and is able to learning effectively. The effectiveness of proposed method is verified through the simulation of X-OR and 3-parity problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.480-483
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• 1992

Although the back-propagation(BP) algorithm is widely used for its simple structure and easy learning method, it has a drawback of slow convergence rate. In this paper, we propose an algorithm to improve this problem by manipulating the slope parameter of the activation function. The steepest descent method is used in learning the slope parameter, as in the case of weight. The simulation shows that the learning rates of the proposed algorithm is faster than the conventional BP algorithm.

• 제어로봇시스템학회논문지
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• 제21권5호
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• pp.477-481
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• 2015

Implementation of memristor-based multilayer neural networks and their hardware-based learning architecture is investigated in this paper. Two major functions of neural networks which should be embedded in synapses are programmable memory and analog multiplication. "Memristor", which is a newly developed device, has two such major functions in it. In this paper, multilayer neural networks are implemented with memristors. A Random Weight Change algorithm is adopted and implemented in circuits for its learning. Its hardware-based learning on neural networks is two orders faster than its software counterpart.