• International Journal of Advanced Culture Technology
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• 제3권2호
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• pp.77-86
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• 2015

In this paper there is an overview of some standards and security models which are implemented in such an IP-based and heterogeneous networks and we also present some security models in an open environment and finally we obtain that as a result of the nature of 4G networks there are still more security holes and open issues for expert to notice. Our survey shows that a number of new security threats to cause unexpected service interruption and disclosure of information will be possible in 4G due mainly to the fact that 4G is an IP-based, heterogeneous network. Other than that, it tells about the security issues and vulnerabilities present in the above 4G standards are discussed. Finally, we point to potential areas for future vulnerabilities and evaluate areas in 4G security which warrant attention and future work by the research and advanced technology industry.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1145-1148
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• 2005

The sensor network that may be deemed to fall in the field of ubiquitous computing performs the basic function of transmitting sensing data through the autonomous sensing and the Ad hoc network. In order to collect and treat various sensing data at the time of application and manage extremely limited system resources, the sensor network requires the embedded operating system that uses low power, a small cord size and the least hardware resources. In this paper, The operating system having a new structure for constructing the IEEE 802. 15.4 MAC and Zigbee sensor network is suggested and can be formed by reviewing the characteristics and the core structural requirements of the operating system for the sensor network based on operating systems, which have been formed under existing similar conditions, and applying such features and core structural requirements to the design of the operating system for achieving the features and the requirements.

• 한국전자통신학회논문지
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• 제10권10호
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• pp.1131-1138
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• 2015

인터넷의 규모가 급성장하면서 IPv4 주소는 고갈되었고 IPv6로의 전이는 지연되고 있다. 차선책으로 IPv4 주소공간의 낭비를 줄이기 위하여 공중망과 사설망을 연결하는 네트워크 주소변환(NAT : Network Address Translation) 방안이 사용되고 있다. 본 논문에서는 주소공간을 효율적으로 사용할 수 있는 동적 NAT과 PAT 기반의 네트워크 설계를 기존의 이론중심에서 탈피하기 위해 토폴로지 설계 후 패킷 트레이서를 이용하여 공통 가상 망을 구현하고 시뮬레이션을 통해 결과의 검증이 가능한 효과적인 구현사례를 제시하였다.

• Structural Engineering and Mechanics
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• 제18권4호
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• pp.441-459
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• 2004

This paper explores the potential use of neural networks (NNs) in the field of contact mechanics. A neural network model is developed for predicting, with sufficient approximation, the contact lengths between the elastic layer and two elastic circular punches. A backpropagation neural network of three layers is employed. First contact problem is solved according to the theory of elasticity with integral transformation technique, and then the results are used to train the neural network. The effectiveness of different neural network configurations is investigated. Effect of parameters such as load factor, elastic punch radii and flexibilities that influence the contact lengths is also explored. The results of the theoretical solution and the outputs generated from the neural network are compared. Results indicate that NN predicted the contact length with high accuracy. It is also demonstrated that NN is an excellent method that can reduce time consumed.

• 산업경영시스템학회지
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• 제36권4호
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.169-178
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• 2019

Due to the characteristics of an ad hoc network without a control center, self-organization, and flexible topology, the trust evaluation of the nodes in the network is extremely difficult. Based on the analysis of ad hoc networks and the blockchain technology, a blockchain-based node-level trust evaluation model is proposed. The concepts of the node trust degree of the HASH list on the blockchain and the perfect reward and punishment mechanism are adopted to construct the node trust evaluation model of the ad hoc network. According to the needs of different applications the network security level can be dynamically adjusted through changes in the trust threshold. The simulation experiments demonstrate that ad-hoc on-demand distance vector(AODV) Routing protocol based on this model of multicast-AODV(MAODV) routing protocol shows a significant improvement in security compared with the traditional AODV and on-demand multipath distance vector(AOMDV) routing protocols.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.338-344
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• 2002

In this paper, neural network force tracking control is proposed. The conventional impedance function is reformulated to have direct farce tracking capability. Neural network is used to compensate for all the uncertainties such as unknown robot dynamics, unknown environment stiffness, and unknown environment position. On line training signal of farce error for neural network is formulated. A large x-y table is built as a test-bed and neural network loaming algorithm is implemented on a DSP board mounted in a PC. Experimental studies of farce tracking on unknown environment for x-y table robot are presented to confirm the performance of the proposed technique.

• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 수산해양교육연구
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• 제26권4호
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• pp.760-768
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• 2014

Yet logistics base in China has a refrigerated storage facilities installed areas, the number of those is very limited and is generally insufficient. According to these especial points, a new construction cold chain logistics network design strategy is required from how to use the existing refrigerated warehouses to new issue. For example, however refrigerated storage facility is supplied, can it satisfy all demand of this area? Then does it have optimized location of this area? If future demand expansion, adding that already other refrigerated storage facilities matter? Or, add another refrigerated facilities, optimum cold chain established a network matter? So on. Above problems can be occurred. In order to solve facing many of these issues of distribution network, northeast area in China has been selected as a subject, and we designed a new cold chain distribution network.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.471-477
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• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.