• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.32.3-32
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• 2001

This paper proposes a layered protocol to interpret voice commands of the user´s own language to a machine, to control it in real time. The layers consist of speech signal capturing layer, lexical analysis layer, interpretation layer and finally activation layer, where each layer tries to mimic the human counterparts in command following. The contents of a continuous voice command are captured by using Hidden Markov Model based speech recognizer. Then the concepts of Artificial Neural Network are devised to classify the contents of the recognized voice command ...

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3000-3022
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• 2019

Full-duplex (FD) technologies enable wireless nodes to simultaneously transmit and receive signal using the same frequency-band. The FD modes could improve their physical layer throughputs. However, in the wireless ad hoc networks, the FD communications also produce new interference risks. On the one hand, the interference ranges (IRs) of the nodes are enlarged when they work in the FD mode. On the other hand, for each FD pair, the FD communication may cause the potential hidden terminal problems to appear around the both sides. In this paper, to avoid the interference risks, we first model the IR of each node when it works in the FD mode, and then analyze the conditions to be satisfied among the transmission ranges (TRs), carrier-sensing ranges (CSRs), and IRs of the FD pair. Furthermore, in the media access control (MAC) layer, we propose a specific method and protocol for collision avoidance. Based on the modified Omnet++ simulator, we conduct the simulations to validate and evaluate the proposed FD MAC protocol, showing that it can reduce the collisions effectively. When the hidden terminal problem is serious, compared with the existing typical FD MAC protocol, our protocol can increase the system throughput by 80%~90%.

• 대한전자공학회논문지TC
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• 제43권10호
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• pp.79-89
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• 2006

TCP는 신뢰성을 보장하는 전송 프로토콜로서 인터넷 등에서 가장 널리 사용되고 있는 전송 방식이다. 하지만 TCP는 유선망에 적합하도록 설계되었기 때문에 무선망에서 TCP를 사용할 경우 성능 저하가 발생된다. TCP의 성능 저하 원인으로는 MAC 계층에서의 무선 매체 경쟁, hidden-terminal 문제와 exposed terminal 문제, 링크 계층에서의 패킷 손실, 불공정성의 문제들과 노드의 이동에 의한 경로 단절시 발생되는 패킷 순서 바뀜 문제와 경로의 단절로 인한 재전송 타이머의 exponential backoff에 의한 대역폭의 낭비 등이 있다. 특히 이동 ad-hoc 망에서는 전송 범위(transmission range)와 간섭범위(interference range)의 불일치로 인해 발생되는 hidden terminal 문제로 인해 동시에 전송할 수 있는 노드의 수가 제한되며 이로 인해 성능저하가 크게 발생된다. 본 논문에서는 IEEE 802.11 기반 이동 ad-hoc 망에서 발생되는 hidden terminal 문제로 인해 노드가 전송을 하지 못하고 CW(contention window)만 크게 증가되는 문제를 해결하기 위한 MAC 알고리즘을 제안한다. 기존의 802.11 MAC의 DCF(distributed coordination function)에서는 전송에 실패할 경우 CW를 지수적으로 증가시키지만 본 논문에서 제안하는 기법은 노드가 전송 실패를 하였을 경우 그 원인에 따라 CW를 적절하게 변화시킴으로 성능 향상을 얻을 수 있다. 이 기법을 사용하면 hidden terminal에 의해 전송을 실패하는 노드에게 공정한 전송 기회를 부여함으로써 TCP 성능 향상을 얻을 수 있음을 시뮬레이션을 통해 보였다.

• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• 전자공학회논문지B
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• 제29B권4호
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• pp.58-65
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• 1992

In this paper new back propagation algorithm which adds neurons to hidden layer is proposed. this proposed algorithm is applied to the pattern recognition of written number coupled with back propagation algorithm through omitting redundant learning. Learning rate and recognition rate of the proposed algorithm are compared with those of the conventional back propagation algorithm and the back propagation through omitting redundant learning. The learning rate of proposed algorithm is 4 times as fast as the conventional back propagation algorithm and 2 times as fast as the back propagation through omitting redundant learning. The recognition rate is 96.2% in case of the conventional back propagation algorithm, 96.5% in case of the back propagation through omitting redundant learning and 97.4% in the proposed algorithm.

• International Journal of Contents
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• 제9권4호
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• pp.11-15
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• 2013

"Protein Folding Problem" is considered to be one of the "Great Challenges of Computer Science" and prediction of disordered protein is an important part of the protein folding problem. Machine learning models can predict the disordered structure of protein based on its characteristic of "learning from examples". Among many machine learning models, we investigate the possibility of multilayer perceptron (MLP) as the predictor of protein disorder. The investigation includes a single hidden layer MLP, multi hidden layer MLP and the hierarchical structure of MLP. Also, the target node cost function which deals with imbalanced data is used as training criteria of MLPs. Based on the investigation results, we insist that MLP should have deep architectures for performance improvement of protein disorder prediction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.281-286
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• 2005

The main objective of the research work presented in this article is to present a systematic approach for designing a multilayer feed-forward artificial neural network based self-tuning power system stabilizer (ST-ANNPSS). In order to suggest an approach for selecting the number of neurons in the hidden layer, the dynamic performance of the system with ST-ANNPSS is studied and hence compared with that of conventional PSS. Finally the effect of variation of loading condition and equivalent reactance, Xe is investigated on dynamic performance of the system with ST-ANNPSS. Investigations reveal that ANN with one hidden layer comprising nine neurons is adequate and sufficient for ST-ANNPSS. Studies show that the dynamic performance of STANNPSS is quite superior to that of conventional PSS for the loading condition different from the nominal. Also it is revealed that the performance of ST-ANNPSS is quite robust to a wide variation in loading condition.

• 한국환경보건학회지
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• 제28권3호
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• pp.19-25
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• 2002

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.