• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.299-304
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• 2017

A wireless passive sensor network is a network consisting of sink nodes, sensor nodes, and radio frequency (RF) sources, where an RF source transfers energy to sensor nodes by radiating RF waves, and a sensor node transmits data by consuming the received energy. Against theoretical expectations, a wireless passive sensor network suffers from many practical difficulties: scarcity of energy, non-simultaneity of energy reception and data transmission, and inefficiency in allocating time resources. Perceiving such difficulties, we propose a simple contending-type medium access control (MAC) scheme for many sensor nodes to deliver packets to a sink node. Then, we derive an approximate expression for the network-wide throughput attained by the proposed MAC scheme. Also, we present an approximate expression for the optimal partition, which maximizes the saturated network-wide throughput. Numerical examples confirm that each of the approximate expressions yields a highly precise value for network-wide throughput and finds an exactly optimal partition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.465-470
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• 2011

Recently fault currents are increasing in a network. It is caused by increase in electric demand and high penetration of distributed generation with renewable energy sources. Moreover, distribution network has become more and more complex as mesh network to improve the distribution system reliability and increase the flexibility and agility of network operation. Accordingly, the fault current will exceed capacity of circuit breakers soon and all the various rational solutions to solve this problem are taken into account. Under these circumstances, superconducting fault current limiter(SFCL) is a new alternative in the viewpoint of technical and economic aspects. This study presents operation processes for a resistive-type of SFCL, and it proposes reliability model for the SFCL. When a SFCL is installed into a network, the contribution of decreased fault currents to failure for distribution equipments can be quantified. As a result, it is expected that a SFCL makes the reliability of adjacent equipments on existing network improve and these changes are analyzed. We propose a methodology to evaluate the reliability in the distribution network where a SFCL is installed considering a reliability model for resistive-type of SFCL and reliability changes for adjacent equipments which are proposed in this paper.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.3
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• pp.561-573
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• 2018

In order to design the ventilation in the road tunnel, it is necessary to know the ratio of average annual daily traffic by vehicle type. In general, the road tunnels are onedirectional tunnel, so the traffic of each vehicle type does not change along the tunnel. On the other hand, in the case of network road tunnels, since the connections in the tunnels are complex, the traffic of vehicle-type varies depending on the network composition of tunnels. In the studying the easy method for calculating the ratio of vehicle type for the network road tunnel are proposed with using incidence matrix.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.3
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• pp.95-100
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• 2004

A control alogorithm using sliding mode and neural network for Buck type DC-DC converter is presented. Also, we conform a rightness the proposal method by comparing a theoretical values and experimental values obtained from experiment using DSP(digital signal processor). Performance comparisons made with the general hysteresis controller clearly bring out the superior performance of the proposal neural network controller. This paper will be applied to other power conversion system using the proposal neural network controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.223-226
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• 2003

Because UMLS semantic network is bulky and complex, user hard to understand and has shortcoming that can not express all semantic network on screen. To solve this problem, rules to dismember semantic network efficiently are introduction. but there is shortcoming that this should classifies manually applying rule whenever UMLS semantic network is modified. Suggest automatic clustering method of UMLS semantic network that use genetic algorithm to solve this problem. Proposed method uses Linked semantic relationship between each semantic type and semantic network does clustering by structurally similar semantic type linkages. To estimate the performance of suggested method, we compared it with result of clustering method by rule.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.885-896
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• 2013

MTC(Machine Type Communications), providing a variety of services anytime and anywhere by connecting the cellular network to the machine and things without human intervention, is being considered as a major challenge of the next-generation communications. Currently, When a massive MTC devices simultaneously connect to the network, each MTC device needs an independent access authentication process. Because of this process, authentication signaling congestion and overload problems will cause in LTE-Advanced. In this paper, we propose a group-based authentication protocol and a key management protocol. For managing the MTC devices as group units, the proposed protocol elects a group leader and authentications only once with the core network. After the authentication is completed, a group leader manages the rest members and MME(Mobility Management Entity) by constructing a binary tree. Finally, the propose protocol analysis show that the proposed protocol not only can reduces the authentication signaling which generated in between the MTC devices and the core network but also can manages the MTC devices, efficiently.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.137-141
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• 2002

Among the members of the inhibitor of apoptosis (IAP) protein family, only Livin and survivin have been reported to have variant forms. We have found a variant form of c-IAP2 through the interaction with the X protein of HBV using the yeast two-hybrid system. In contrast to the wild-type c-IAP2, the variant form has two stretches of sequence in the RING domain that are repeated in the C-terminus that would disrupt the RING domain. We demonstrate that the variant form has an inhibitory effect on TNF-mediated $NF-{\kappa}B$ activation unlike the wild-type c-IAP2, which increases TNFmediated $NF-{\kappa}B$ activation. These results suggest that this variant form has different activities from the wild-type and the RING domain may be involved in the regulation of TNF-induced $NF-{\kappa}B$ activation.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.363-369
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• 2002

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.