• 전자공학회논문지B
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• 제32B권12호
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• 융합보안논문지
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• 제12권3호
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• pp.67-73
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• 2012

본 논문에서는 지그비를 이용한 무선 기반 센서 네트워크 단점을 극복하기 위하여 유무선 네트워크 연동 기술을 이용한 하이브리드 센서 네트워크 구현 방안을 제시하였다. 제안된 유무선 연동 센서 네트워크는 CATV망에서의 저손실 전송 특성을 바탕으로 실내 무선 환경의 열악한 전송 특성을 보완할 수 있으며, 마찬가지로 동일 주파수 대역의 무선랜과 블루투스의 간섭에서도 자유로울 수 있다. 또한 이미 충분한 인프라가 구축되어 있는 CATV망을 변경없이 사용하고, 기존 무선망의 전송 부정확성에 대한 안정성과 예측 가능한 전송 링크를 제공함으로써 네트워크 설계가 보다 효율적이었으며, 센서 네트워크의 안정성과 높은 신뢰성을 보장하였다.

• Journal of Communications and Networks
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• 제4권3호
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• pp.161-169
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• 2002

Ad hoc wireless networks involving large populations of scattered communication nodes will play a key role in the development of low power, high capacity, interactive, multimedia communication networks. Such networks must support arbitrary network connections and provide coverage anywhere and anytime. This paper partitions such arbitrarily connected network architectures into three distinct groups, identifies the associated dual network architectures and counts the number of network architectures assuming there exist N network nodes. Connectivity between network nodes is characterized as a random event. Defining the link availability P as the probability that two arbitrary network nodes in an ad hoc network are directly connected, the network connection probability $ \integral_n$(p) that any two network nodes will be directly or indirectly connected is derived. The network connection probability $ \integral_n$(p) is evaluated and graphically demonstrated as a function of p and N. It is shown that ad hoc wireless networks containing a large number of network nodes possesses the same network connectivity performance as does a fixed network, i.e., for p>0, $lim_{N\to\infty} Integral_n(p)$ = 1. Furthermore, by cooperating with fixed networks, the ad hoc network connection probability is used to derive the global network connection probability for hybrid networks. These probabilities serve to characterize network connectivity performance for users of wireless ad hoc and hybrid networks, e.g., IEEE 802.11, IEEE 802.15, IEEE 1394-95, ETSI BRAN HIPERLAN, Bluetooth, wireless ATM and the world wide web (WWW).

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2015년도 제52차 하계학술대회논문집 23권2호
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• pp.250-251
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• 2015

본 논문에서는 자기 조직화 기능을 갖는 Kohonen의 SOM(Self organization map) 신경회로망과 주어지는 데이터에 따라 초기의 클러스터 개수를 설정하여 처리하는 수정된 K-Means 알고리즘을 결합한 Hybrid Kohonen Network 를 제안한다. 또한, 실제의 항공영상에 적용하여 고전적인 K-Means 알고리즘 및 고전적인 SOM 알고리즘보다 우수함을 보인다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권7_8호
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• pp.744-751
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• 2003

본 논문에서는 최근 뛰어난 예측력으로 각광받는 최소제곱 Support Vector Machine(Least Square Support Vector Machine: LS-SVM)과 First Principle(FP)을 결합한 하이브리드 최소제곱ㆍSupport Vector Machine 모델, HLS-SVM(Hybrid Least Square-Super Vector Machine)을 제안한다. 제안한 모델인 하이브리드 최소제곱 Support Vector Machine을 기존의 방법인 하이브리드 신경망(Hybrid Neural Network:HNN), 비선형 칼만필터와 하이브리드 신경망을 결합한 HNN-EKF (Hybrid Neural Network with Extended Kalman Filter) 모델과 비교해 보았다. HLS-SVM 모델은 학습 및 validation 과정에서는 HNN-EKF와 근사한 성능을 보였고, HNN 보다는 우수한 결과를 보였고, 일반화 성능에서는 HNN-EKF에 비해 3배, HNN보다 100배정도 우수한 결과를 보였다.

• 한국콘텐츠학회논문지
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• 제12권10호
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• pp.11-21
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• 2012

하이브리드 네트워크 인프라는 Internet2, SURFNet 등의 선도 연구망 커뮤니티에게 가장 우선적인 기술로 대두되고 있다. 그러나, 첨단(high-end) 응용의 종단 간 협업 연구를 위하여 필수적인 하이브리드 연구망 간의 인터도메인 협업 인프라는 실질적인 아키텍쳐의 설계와 구현에 있어서 아직도 많은 연구를 필요로 하고 있다. 따라서 본 논문에서는 하이브리드 연구망 기반의 분산 가상형 네트워크 운영과 리소스 정보 관리를 위한 프레임워크를 제안하고, 이를 기반으로 코어 시스템을 구현하였다. 제안된 프레임워크는 멀티도메인 하이브리드 연구망 운영과 관리를 위하여 분산형 아키텍쳐로 설계되었다. 분산 가상형 네트워크 운영 프레임워크는 네트워크 도메인 내에서 자치성과 독립적인 제어를 유지하면서 인터도메인 네트워크 간의 협업을 가능케 함으로써, 연구자 및 실험자가 스스로 생성한 가상 네트워크를 운영 관리 할 수 있는 환경을 제공할 수 있다. 본 논문에서는 제안된 프레임워크를 위한 세부적인 구조와 기술을 다루며, 이러한 환경이 어떻게 고성능 첨단(high-end) 응용을 위하여 활용될 수 있는지에 대하여 고찰한다.

• International Journal of Aeronautical and Space Sciences
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• 제16권4호
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.637-640
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• 2011

The hybrid neural networks have characteristics such as fast learning times, generality, and simplicity, and are mainly used to classify learning data and to model non-linear systems. The middle layer of a hybrid neural network clusters the learning vectors by grouping homogenous vectors in the same cluster. In the clustering procedure, the homogeneity between learning vectors is represented as the distance between the vectors. Therefore, if the distances between a learning vector and all vectors in a cluster are smaller than a given constant radius, the learning vector is added to the cluster. However, the usage of a constant radius in clustering is the primary source of errors and therefore decreases the recognition success rate. To improve the recognition success rate, we proposed the enhanced hybrid network that organizes the middle layer effectively by using the enhanced ART1 network adjusting the vigilance parameter dynamically according to the similarity between patterns. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with conventional recognition algorithms.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.