• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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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.

• Convergence Security Journal
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• v.12 no.3
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• pp.67-73
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• 2012

In this paper, in order to overcome the disadvantage of wireless-based sensor network, a hybrid sensor network using wired and wireless linkage is proposed. Proposed a wired and wireless linkage hybrid sensor network can compensate the defect of poor transmission at the indoor wireless environment, and can be free from interference between a wireless LAN and Bluetooth of the same frequency bandwidth due to an attribute of low-loss transmission at the CATV network. Also, proposed a wired and wireless linkage hybrid sensor network make use of CATV network which is well-built infrastructure, is more efficient to design network, assure a stability and high reliability of the sensor network as providing a stability for an inaccuracy and a predictable transmission link for the existing wireless network.

• Journal of Communications and Networks
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• v.4 no.3
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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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• v.22 no.6
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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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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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.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.744-751
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• 2003

A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.11-21
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• 2012

Hybrid network infrastructure has been deployed as the most important technology for the advanced research networking community such as Internet2, SURFnet, etc. However, further research needs to be performed in terms of feasible design and implementation of architecture for inter-domain collaborative network infrastructure, which is essential to end-to-end collaborative research based on high-end applications. In this paper, we suggest a framework for distributed and virtual network operations based on hybrid research networks and efficient cooperation between multi-domain hybrid networks, which aims to provide collaborative network environment for high-end applications. Suggested framework is designed to adopt decentralized model of multi-domain hybrid research network management. A collaborative and distributed virtual model that is characterized by cooperation among hybrid research networks that insist on maintaining their autonomy and control, can also contribute for researchers and other end-users to manage and operate their own virtual networks.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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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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• v.9 no.6
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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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.