• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.325-330
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• 2014

The era of convergence is being applied to all areas of Information and Communication Technology (ICT). The convergence of broadcasting service and communication service almost occurs on smart devices including smartphone. The smart hybrid Digital Multimedia Broadcasting (DMB) is a typical example of the convergence of broadcasting and wireless communication service. The hybrid mobile broadcasting service can support seamless video, 3D, high quality, and additional data services based on network connection between the broadcasting and wireless network. The gateway and terminal (including apps on the smartphone) take the role of the main components on the hybrid service. This paper presents the service concept, main components structure, the implementation of gateway and terminals, and field test to the urban areas for the mobile hybrid system.

• 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 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.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1626-1636
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• 2013

Nowadays, a rapid development in wind power technologies is occurring compared with other renewable energies. This advance in technology has facilitated a new generation of wind turbines with larger capacity and higher efficiency. As the height of the turbines and the distance between turbines increases, the monitoring and control of this new generation wind turbines presents new challenges. This paper presents the architectural design, simulation, and evaluation of hybrid communication networks for a large-scale wind turbine (WT). The communication network of WT is designed based on logical node (LN) concepts of the IEC 61400-25 standard. The proposed hybrid network architectures are modeled and evaluated by OPNET. We also investigate network performance using three different technologies: Ethernet-based, WiFi-based, and ZigBee-based. Our network model is validated by analyzing the simulation results. This work contributes to the design of a reliable communication network for monitoring and controlling a wind power farms (WPF).

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• ETRI Journal
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• v.34 no.6
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• pp.838-846
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• 2012

This paper proposes a cost-effective hybrid-type power budget extender (PBEx) that can provide a high power budget of over 45 dB in an asymmetric 10-Gb/s Ethernet passive optical network (10/1G-EPON). The hybrid-type 10/1G-EPON PBEx comprises a central office terminal (COT) and remote terminal (RT) module supporting four channels and uses a coarse wavelength division multiplexing (CWDM) technology between the COT and RT for a reduction of fiber cost and efficient access network design. The proposed 10/1G-EPON PBEx can provide over a 40-km reach and 128-way split per CWDM wavelength with no modification of a legacy 10/1G-EPON system and can satisfy the error-free service in $10^{10}$ packet transmission.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.1-9
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• 2017

With the Development of Wireless Network Technology and Wireless Link Technology, Wireless Mesh Network (WMN) is Attracting Attention as a Key Technology to Construct the Wireless Transit Network. The WMN has been Studied for a Long Time in Various Fields, however there are still many Problems that have not been solved yet. One of them is the Routing Problem to find an Optimal path in a Multi-hop Network Composed of Wireless Links. In the Hybrid-WMN, which is one of the Three Types of WMN, Optimal Path Selection Requires Research on Path Search Protocols that Effectively use the Infrastructure Mesh as a Transit Network, Together with Research for a Routing Metric with Excellent Performance. Therefore, this Paper Proposes a Dual Mode-AODV(Ad hoc On-demand Distance Vector) for Hybrid-WMN. Simulation result shows that the Path Selection Delay was Reduced by 52% than AODV when the Proposed Dual Mode-AODV was applied.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.687-698
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• 2014

ATSC is currently working on standardization of hybrid 3DTV broadcasting service in heterogenous network environment after completion of service-compatible 3DTV broadcasting service standard based on broadcasting channel. This paper proposes a convergence 3D video broadcasting method on broadcasting and IP network while guaranteeing a Full-HD 3D quality without degrading the image quality of legacy DTV. Specifically, this paper describes transmission of the 3D additional video using the ISO/IEC 23009-1 DASH, robust synchronization method under heterogenous network environments and system target decoder model for hybrid 3DTV receiver. Based on experimental results, we confirm that proposed technologies can be used as a core technology in the hybrid 3DTV standardization and a reference model for a development of hybrid 3DTV encoder and receiver.