• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.147-148
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• 2006

Describe about CAN-LIN integrated network in this treatise. Describe an search their advantage about CAN communication and LIN communication protocol. Introduce CAN-LIN integrated network system and decide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3602-3620
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• 2016

Dynamic topology is one of the main influence factors on network performability. However, it was always ignored by the traditional network performability assessment methods when analyzing large-scale mobile ad hoc networks (MANETs) because of the state explosion problem. In this paper, we address this problem from the perspective of complex network. A two-layer hierarchical modeling approach is proposed for MANETs performability assessment, which can take both the dynamic topology and multi-state nodes into consideration. The lower level is described by Markov reward chains (MRC) to capture the multiple states of the nodes. The upper level is modeled as a small-world network to capture the characteristic path length based on different mobility and propagation models. The hierarchical model can promote the MRC of nodes into a state matrix of the whole network, which can avoid the state explosion in large-scale networks assessment from the perspective of complex network. Through the contrast experiments with OPNET simulation based on specific cases, the method proposed in this paper shows satisfactory performance on accuracy and efficiency.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.713-724
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• 2016

Energy consumption has become a main problem of sustainable development in communication networks and how to communicate with high energy efficiency is a significant topic that researchers and network operators commonly concern. In this paper, an energy-efficient multicast algorithm in multi-hop wireless networks is proposed aiming at new generation wireless communications. Traditional multi-hop wireless network design only considers either network efficiency or minimum energy consumption of networks, but rarely the maximum energy efficiency of networks. Different from previous methods, the paper targets maximizing energy efficiency of networks. In order to get optimal energy efficiency to build network multicast, our proposed method tries to maximize network throughput and minimize networks' energy consumption by exploiting network coding and sleeping scheme. Simulation results show that the proposed algorithm has better energy efficiency and performance improvements compared with existing methods.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.77-78
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• 2008

With advent of 3G mobile communication services, video telephony becomes one of the major services. However, due to a narrow channel bandwidth, the current video telephony services have not yet reached a satisfied level. In this paper, we propose an ROI (Region-Of-Interest) based improvement of visual quality for video telephony services with the H.264|MPEG-4 Part 10 (AVC: Advanced Video Coding) codec. To this end, we propose a face detection and tracking method to define ROI for the AVC codec based video telephony. Experiment results show that our proposed ROI based method allowed for improved visual quality in both objective and subjective perspectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3071-3095
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• 2013

Configuring optimization of wireless sensor networks, which can improve the network performance such as utilization efficiency and network lifetime with minimal energy, has received considerable attention in recent years. In this paper, a cross layer optimal approach is proposed for multi-source linear network and grid network under Rayleigh block-fading channels, which not only achieves an optimal utility but also guarantees the end-to-end reliability. Specifically, in this paper, we first strictly present the optimization method for optimal nodal number $N^*$, nodal placement $d^*$ and nodal transmission structure $p^*$ under constraints of minimum total energy consumption and minimum unit data transmitting energy consumption. Then, based on the facts that nodal energy consumption is higher for those nodes near the sink and those nodes far from the sink may have remaining energy, a cross layer optimal design is proposed to achieve balanced network energy consumption. The design adopts lower reliability requirement and shorter transmission distance for nodes near the sink, and adopts higher reliability requirement and farther transmission distance for nodes far from the sink, the solvability conditions is given as well. In the end, both the theoretical analysis and experimental results for performance evaluation show that the optimal design indeed can improve the network lifetime by 20-50%, network utility by 20% and guarantee desire level of reliability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.542-545
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• 2005

본 연구에서는 Home Network System에서 가전기기들을 제어하고 집안의 상황을 원격지에 있는 사용자에게 전달해 줄 수 있는 Home Network Mobile Robot을 제작하여 보다 더 지능적이고 사용자에게 편리한 Home Network System을 구축한다. 이를 위해 본 논문에서는 실제 Home Network 시스템 하에서의 자율이동 로봇을 고안하였으며 이의 구동을 위해 OS로는 Linux Kernel 2.4를 Porting 하였고, Vision 및 Ethernet 통신이 용이하도록 회로를 설계, 제작하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.704-710
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• 2007

This paper examines the operations of private housing developers (PHDs) based on Social Network Theory. PHDs need to choose the best consultants, contractors and suppliers (CCS) to make sure the project run and complete successfully. PHDs gather the scarce resources from the external environment through personal network. This research used the social network method which relies on alliances based on network, social, tie and trust. The more people/firm PHDs network with, the better chances of finding the right CCS.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.194-198
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• 2023

The current electricity networks will undergo profound changes in the years to come to be able to meet the growing demand for electricity, while minimizing the costs of consumers and producers, etc. The electricity network of tomorrow or even the intelligent « Smart Grids » network will be the convergence of two networks: the electricity network and the telecommunications network. In this context falls our work which aims to study the impact of the integration of energy decentralization into the electricity network. In this sense, we have implemented a new smart grid model where several coexisting suppliers can exchange information with consumers in real time. In addition, a new approach to energy distribution optimization has been developed. The simulation results prove the effectiveness of this approach in improving energy exchange and minimizing consumer purchase costs and line losses.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.