• ETRI Journal
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• 제30권5호
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• pp.663-673
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• 2008

The multi-scenario topology of multi-channel hybrid ubiquitous sensor networks (USNs) is studied and a novel link auto-diversity cross-layer cooperative scheduling scheme is proposed in this paper. The proposed scheme integrates the attributes of the new performance evaluation link auto-diversity air-time metric and the topology space in the given multi-scenario. The proposed scheme is compared with other schemes, and its superiority is demonstrated through simulations. The simulation results show that relative energy consumption, link reception probability, and end-to-end blocking probability are improved. The addressing ratio of success with unchanged parameters and external information can be increased. The network can tolerate more hops to support reliable transportation when the proposed scheme is implemented. Moreover, the scheme can make the network stable. Therefore, the proposed scheme can enhance the average rate performance of the hybrid USN and stabilize the outage probability.

• 한국통신학회논문지
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• 제16권2호
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• pp.128-136
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• 1991

A neural network of multi layer perception type, learned by error back propagation learning rule, is generally used for the verification or clustering of similar type of patterns. When learning is completed, the network has a constant value of output depending on a pattern. This paper shows that the intensity of neuron's out put can be controlled by a function which intensifies the excitatory interconnection coefficients or the inhibitory one between neurons in output layer and those in hidden layer. In this paper the value of factor in the function to control the output is derived from the know values of the neural network after learning is completed And also this paper show that the amount of an increased neuron's output in output layer by arbitary value of the factor is derived. For the applications increased recognition performance of a pattern than has distortion is introduced and the output of partially damaged neurons are first managed and this paper shows that the reduced recognition performance can be recovered.

• 대한임베디드공학회논문지
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• 제7권5호
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• pp.209-217
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• 2012

In the present, Wireless Sensor Network(WSN) has been used for the purpose of the military operation with surveillance systems and for collecting useful information from the natural environment. Basically, low-power, easy deployment and low cost are the most important factors to be deployed for WSNs. Lots of researches have been studied to meet those requirements, especially on the node capacity and battery lifetime improvements. Recently, the study of wireless mesh networks applied into the surveillance systems has been proceeded as a solution of easy deployment. In this paper, we proposed large-scale intelligent multi-layer surveillance systems based on QoS assuring Wireless Mesh Networks and implemented them in the real testbed environment. The proposed system explains functions and operations for each subsystem as well as S/W and H/W architectures. Experimental results are shown for the implemented subsystems and the performance is satisfactory for the surveillance system. We can identify the possibility of the implemented multi-layer surveillance system to be used in practice.

• International Journal of Advanced Culture Technology
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• 제6권3호
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• 전자공학회논문지B
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• 제28B권11호
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• pp.934-940
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• 1991

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergencs speed. In this paper, an approach to improve the convergence speed is proposed using fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.491-496
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• 2004

The oscillation of the fluid caused by external forces is called sloshing, which occurs in moving vehicles with contained liquid masses, such as aircraft. cars and liquid rocket and so on. This sloshing effect could be a severe problem in vehicle stability and control. So, various baffles are used in order to reduce the sloshing. The Lagrangian, Eulerian and ALE numerical method is widely used on the analysis of sloshing presently. But, these numerical methods are needed so many CPU time. In this study, for the reduction of the sloshing analysis time, me multi.layer perceptron artificial neural network is introduced and analysis results are presented.

• 전자공학회논문지C
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• 제35C권3호
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.176-190
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• 2018

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.89.4-89
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• 2002

$\textbullet$ Field Programmable Gate Array $\textbullet$ flexible hardware $\textbullet$ neural network $\textbullet$ determinism learning $\textbullet$ multi-valued logic $\textbullet$ disjunctive normal form $\textbullet$ multi-dimensional exclusive OR

• 한국항해항만학회지
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• 제32권8호
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• pp.589-596
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• 2008

선박의 접안운동을 자동화하기 위하여 인공신경망(Artificial Neural Network, 이하 ANN)에 의한 제어를 수행하였다. ANN은 시스템의 비선형성이 표현 가능하므로 접안운동과 같은 비선형성이 강한 조종운동에 적합하다. 입력층과 출력층 사이에 하나 이상의 중간층이 존재하는 다층 인식자(Multi-layer perceptron)를 사용하였고, 교사 데이터(Teaching data)와 역전파(Back-Propagation) 알고리즘을 사용하여 신경망의 출력값과 목표 출력값 사이의 오차가 최소가 되도록 신경망 학습을 수행하였다. 접안 시 저속조종 수학모델을 사용하여 접안 시뮬레이션을 수행하였으며, ANN의 입력층 성분(unit)이 8개인 구조와 6개인 구조의 접안 제어를 비교하였다. 시뮬레이션 결과, 두 ANN에 의하여 접안 경로 선택에 차이가 나타났으나 접안 조건은 모두 만족하였다.