• 디지털콘텐츠학회 논문지
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• 제8권3호
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• pp.393-401
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• 2007

P2P systems have gained a lot of research interests and popularity over the years and have the capability to unleash and distribute awesome amounts of computing power, storage and bandwidths currently languishing - often underutilized - within corporate enterprises and every Internet connected home in the world. Since there is no central control over resources or devices and no before hand information about the resources or devices, device discovery remains a substantial problem in P2P environment. In this paper, we cover some of the current solutions to this problem and then propose our feed forward neural network (FFNN) based solution for device discovery in mobile P2P environment. We implements feed forward neural network (FFNN) trained with back propagation (BP) algorithm for device discovery and show, how large computation task can be distributed among such devices using agent technology. It also shows the possibility to use our architecture in home networking where devices have less storage capacity.

• 대한전자공학회논문지SD
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• 제45권1호
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• pp.20-27
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• 2008

본 논문에서는 비선형 패턴 분류를 위해 FPGA 칩에 신경회로망을 구현하였다. 병렬처리 연산을 위해 순방향 신경회로망이 구현 되었다. 신경망의 학습을 off-line으로 한 다음에 가중치 값들을 저장하여 사용한다. 예로서, AND와 XOR 논리의 패턴 구분이 수행된다. 실험결과를 통해 FPGA에 구현된 신경회로망이 잘 작동하는 것을 검증하였다.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.225-232
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• 2017

Understanding actions in videos is an important task. It helps in finding the anomalies present in videos such as fights. Detection of fights becomes more crucial when it comes to sports. This paper focuses on finding fight scenes in Hockey sport videos using blur & radon transform and convolutional neural networks (CNNs). First, the local motion within the video frames has been extracted using blur information. Next, fast fourier and radon transform have been applied on the local motion. The video frames with fight scene have been identified using transfer learning with the help of pre-trained deep learning model VGG-Net. Finally, a comparison of the methodology has been performed using feed forward neural networks. Accuracies of 56.00% and 75.00% have been achieved using feed forward neural network and VGG16-Net, respectively.

• 대한기계학회논문집A
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• 제33권8호
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• pp.753-759
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• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.

• 디지털콘텐츠학회 논문지
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• 제18권1호
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• pp.183-190
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• 2017

본 논문에서는 깊은 신경망을 이용한 오디오 이벤트 검출 방법을 제안한다. 오디오 입력의 매 프레임에 대한 오디오 이벤트 확률을 feed-forward 신경망을 적용하여 생성한다. 매 프레임에 대하여 멜 스케일 필터 뱅크 특징을 추출한 후, 해당 프레임의 전후 프레임으로부터의 특징벡터들을 하나의 특징벡터로 결합하고 이를 feed-forward 신경망의 입력으로 사용한다. 깊은 신경망의 출력층은 입력 프레임 특징값에 대한 오디오 이벤트 확률값을 나타낸다. 연속된 5개 이상의 프레임에서의 이벤트 확률값이 임계값을 넘을 경우 해당 구간이 오디오 이벤트로 검출된다. 검출된 오디오 이벤트는 1초 이내에 동일 이벤트로 검출되는 동안 하나의 오디오 이벤트로 유지된다. 제안된 방법으로 구현된 오디오 이벤트 검출기는 UrbanSound8K와 BBC Sound FX자료에서의 20개 오디오 이벤트에 대하여 71.8%의 검출 정확도를 보였다.

• Journal of Magnetics
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• 제22권1호
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권5호
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• pp.289-294
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• 2002

다층신경회로망 구조의 재구성은 회로망의 일반화 능력이나 효율성의 관점에서 중요한 문제로 연구되어왔다. 본 논문에서는 신경회로망에 학습된 은닉 지식들을 추출하여 조합함으로써 신경회로망의 구조를 재구성하는 새로운 방법을 제안한다. 먼저, 각 노드별로 학습된 대표적인 지역 규칙을 추출하여 각 노드의 불필요한 연결구조들을 제거한 후, 이들의 논리적인 조합을 통하여 중복 또는 상충되는 노드와 연결구조를 제거한다. 이렇게 학습된 지식을 분석하여 노드와 연결구조를 재구성한 신경회로망은 처음의 신경회로망에 비하여 월등히 감소된 구조 복잡도를 가지며 일반적으로 더 우수한 일반화 능력을 가지게 됨을 실험결과로서 제시하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제20권3호
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• 제20권3호
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.