• 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.

• Information and Communications Magazine
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• v.15 no.7
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• pp.75-94
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• 1998

In this paper, HFC CATV networks are introduced. HFC CATV networks are currently considered as a most effective and plausible solution for subscriber access networks in the information superhighway, among others such as ADSL and B-WLL. The advantages of HFC CATV networks include its broad upstream and downstream channel bandwidths, much wider than PSTN's and its readiness. The HFC CATV networks are already well spread among the general subscribers in Korea. This fact will shorten the installation period and significantly reduce its cost. In addition, The structure and channel environment of the HFC CATV networks are introduced. At the same time, in this paper included are major problems in the HFC CATV networks and the solutions for these problems. Also discussed are data transmission, medium access control, IEEE802.14, and MCNS for standardizations. Moreover, additional telecommunications service activities in both Korea and other countries, using HFC CATV networks are also included. The topics which are covered in detail are the ones such as interoperation between HFC CATV networks and trunk line and the digital transmission performance test for the CATV networks. In the performance test, it is concluded that HFC CATV networks can satisfy the QoS for various additional services if the number of the subscribers in a cell is limited to less than 500 and other minor requirements are satisfied. Based on all these discussions and conclusions, HFC CATV networks are suggested in this paper for the subscriber access networks of the so called information superhighway.

• Journal of IKEEE
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• v.11 no.4
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• pp.322-330
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• 2007

Recent advances in wireless networks and low-cost, low-power design have led to active research in large-scale networks of small, wireless, low power sensors and actuators, In large-scale networks, lots of timing-synchronization protocols already exist (such as NTP, GPS), In ad-hoc networks, especially wireless sensor networks, it is hard to synchronize all nodes in networks because it has no infrastructure. In addition, sensor nodes have low-power CPU (it cannot perform the complex computation), low batteries, and even they have to have active and inactive section by periods. Therefore, new approach to time synchronization is needed for wireless sensor networks, In this paper, I propose Energy-Efficient Time Synchronization (EETS) protocol providing network-wide time synchronization in wireless sensor networks, The algorithm is organized two phase, In first phase, I make a hierarchical tree with sensor nodes by broadcasting "Level Discovery" packet. In second phase, I synchronize them by exchanging time stamp packets, And I also consider send time, access time and propagation time. I have shown the performance of EETS comparing Timing-sync Protocol for Sensor Networks (TPSN) and Reference Broadcast Synchronization (RBS) about energy efficiency and time synchronization accuracy using NESLsim.

• Interdisciplinary Bio Central
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• v.3 no.2
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• pp.8.1-8.6
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• 2011

Background: Thanks to the development of the mathematical/statistical reverse engineering and the high-throughput measuring biotechnology, lots of biologically meaningful genegene interaction networks have been revealed. Steady-state analysis of these systems provides an important clue to understand and to predict the systematic behaviours of the biological system. However, modeling such a complex and large-scale system is one of the challenging difficulties in systems biology. Results: We introduce a new stochastic modeling approach that can describe gene regulatory mechanisms by dividing two (DNA and protein) layers. Simple queuing system is employed to explain the DNA layer and the protein layer is modeled using G-networks which enable us to account for the post-translational protein interactions. Our method is applied to a transcription repression system and an active protein degradation system. The steady-state results suggest that the active protein degradation system is more sensitive but the transcription repression system might be more reliable than the transcription repression system. Conclusions: Our two layer stochastic model successfully describes the long-run behaviour of gene regulatory networks which consist of various mRNA/protein processes. The analytic solution of the G-networks enables us to extend our model to a large-scale system. A more reliable modeling approach could be achieved by cooperating with a real experimental study in synthetic biology.

• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. 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. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• 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).

• Journal of Communications and Networks
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• v.6 no.4
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• pp.397-402
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• 2004

Location management is difficult in ad hoc networks due to many features such as the lack of a wired infrastructure, the scarce energy, memory and processing capabilities of nodes, and nodes’ movement which leads to a dynamic topology. These characteristics make the location management schemes designed for mobile cellular networks inefficient for ad hoc networks. New solutions for location management have therefore been proposed in the literature in the recent past. In this paper, a taxonomy of location management strategies is presented; some of the more interesting approaches proposed in the literature are critically discussed, highlighting their advantages and disadvantages.

• 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.