• Smart Structures and Systems
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• v.11 no.5
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• pp.497-510
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• 2013

Quick, accurate damage monitoring is strongly required for damage assessment in the aftermath of a large natural disaster. Wireless sensor networks are promising technologies to acquire damage information in a citywide area. The wireless sensor networks, however, would be faced with difficulty to collect data in real-time and to expand the scalability of the networks. This paper discusses a scheme of network architecture to cove a whole city in multi-tier heterogeneous networks, which consist of wireless sensor networks, access networks and a backbone network. We first review previous studies for citywide damage monitoring, and then discuss the feature of multi-tier heterogeneous networks to cover a citywide area.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.151-162
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• 1994

A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.

• The Korean Journal of Rehabilitation Nursing
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• v.13 no.1
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• pp.53-61
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• 2010

Purpose: The purpose of this study was to investigate the relationships of social networks to health status among the urban low income elderly. Method: Using a sample of 598 elderly aged 65 years and higher, social networks, health status were measured by the Social Networks Scale (LSNS), Perceived Health Status, GDSSF-K, K-ADL respectively. The t-test, ANOVA and Tukey-test and Pearson's correlation analyses were performed using SPSS 18.0. Results: 41% of subjects didn't contact with relatives at least once a month. 56% of subjects saw or heard less than monthly from relative with whom they have the most contact. 47% didn't have relatives who one can rely on private matters. Social networks among the low income elderly significantly differed by marital status, health insurance type, economic status, regular exercise, living with family. Social networks were significantly correlated with perceived health status (r=.201), cognitive function (r=-.154) and depressive symptoms (r=-.301). Conclusion: Poor social networks were found in urban low income elderly. Poorer social networks were related to worse health status and more depressive symptoms. Interventions targeting at increasing social networks are urgently needed for low income elderly.

• 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 KIEE Conference
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• 2002.07d
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.2061-2080
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• 2013

Roaming authentication protocol is widely used in wireless network which can enable a seamless service for the mobile users. However, the classical approach requires the home server's participation during the authentication between the mobile user and the foreign server. So the more the roaming requests are performed the heavier burden will be on the home server. In this paper, we propose a new roaming authentication framework for wireless communication without the home server's participation. The new roaming authentication protocol in the new framework takes advantage of the ID-based cryptography and provides user anonymity. It has good performance compared with the roaming authentication protocols whose authentication do not need the home server's participation in terms of security and computation costs. Moreover, a new User-to-User authentication protocol in the new framework is also present. All the authentications proposed in this paper can be regarded as a common construction and can be applied to various kinds of wireless networks such as Cellular Networks, Wireless Mesh Networks and Vehicle Networks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.404-409
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• 1998

In this paper, we propose a learning algorithm of fuzzy neural networks with trapezoidal fuzzy weights. This fuzzy neural networks can use fuzzy numbers as well as real numbers, and represent linguistic information better than standard neural networks. We construct trapezodal fuzzy weights by the composition of two triangles, and devise a learning algorithm using the two triangular membership functions, The results of computer simulations on numerical data show that the fuzzy neural networks have high fitting ability for target output.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1456-1463
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• 2012

In this paper, we study the ON/OFF control policy of base stations in two-tier heterogeneous cellular networks to minimize the total power consumption of the system. Using heterogeneous cellular networks is a potential approach of providing higher throughput and coverage compared to conventional networks with only macrocell deployment, but in fact heterogeneous cellular networks often operates regardless of total power consumption, which is a very important issue of modern cellular networks. We propose a policy that controls the activation/deactivation of base stations in heterogeneous cellular networks to minimize total power consumption. Under this policy, the total power consumed can be significantly reduced when the traffic is low while the QoS requirement is satisfied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3907-3923
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• 2020

An enhanced irregular repetition slotted ALOHA (IRSA) protocol is proposed by using polarization characteristic of satellite link and MIMO detection in low earth orbit (LEO) satellite networks, which is dubbed polarized MIMO IRSA (PM-IRSA). In the proposed scheme, one or two packets in one slot can be decoded by employing polarized MIMO detection, and more than two collided packets in multiple slots which can construct the virtual MIMO model can be decoded by the MIMO detection algorithm. The performance of the proposed scheme is analyzed with the density evolution (DE) approach and the degree distribution is optimized to maximize the system throughput by using a differential evolution. Numerical results certify our analysis and show that the normalized throughput of the proposed PM-IRSA can achieve 1.89 bits/symbol.