• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2146-2156
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• 2017

When a failure occurs in active distribution system, it will be isolated through the action of circuit breakers and sectionalizing switches. As a result, the network might be divided into several connected components, in which distributed generations could supply power for customers. Aimed at decreasing customer interruption cost, this paper proposes a theoretically optimal island partition model for such connected components, and a simplified but more practical model is also derived. The model aims to calculate a dynamic island partition schedule during the failure recovery time period, instead of a static islanding status. Fluctuation and stochastic characteristics of the renewable distributed generations and loads are considered, and the interruption cost functions of the loads are fitted. To solve the optimization model, a heuristic search algorithm based on the hill climbing method is proposed. The effectiveness of the proposed model and algorithm is evaluated by comparing with an existing static island partitioning model and intelligent algorithms, respectively.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.125-133
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• 2015

The Remote Device Management (RDM) protocol is used to manage the devices in the lighting control networks. RDM provides bi-directional communications between a controller and many lighting devices over the DMX512-A network. In RDM, using a simple binary search scheme, which is based on the 48-bit unique ID (UID) of each device, discovers the lighting devices. However, the existing binary search scheme tends to require a large delay in the device discovery process. In this paper, we propose a novel partition-based discovery scheme for fast device discovery in RDM. In the proposed scheme, all devices are divided into several partitions as per the device UID, and the controller performs device discovery for each partition by configuring a response timer that each device will use. From numerical simulations, we can see that there is an optimal number of partitions to minimize the device discovery time for a given number of devices in the proposed scheme, and also that the proposed partition-based scheme can reduce the device discovery time, as compared to the existing binary search scheme.

• 한국도로학회:학술대회논문집
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• 2010.09a
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• pp.229-234
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• 2010

• Journal of KIISE:Information Networking
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• v.31 no.5
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• pp.519-531
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• 2004

Ad-hoc networks are temporary wireless systems composed of mobile nodes without any fixed infrastructure. The life time of each node in the ad-hoc network significantly affects the life time of whole ad-hoc network. A node which drained out its battery may incur the partition of whole network in some network topology The life time of each node depends on the battery capacity of each node. Therefore if all mobile nodes in the network live evenly long, the life time of the network will be longer. In this paper, we propose Power-Aware Dynamic Source Routing (PADSR) which selects the best path to make the life time of the network be longer. In PADSR, when a source node finds a path to the destination node, it selects the best path that makes nodes in the network live evenly long. To find the best path, PADSR considers the consumption of transmission energy and residual battery capacity of nodes upon the path. Consequently the network lives longer if we use PADSR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.647-656
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• 2012

TDMA MAC protocol in MANET requires precise network synchronization between nodes though network topology changes continuously due to node mobility and the effect of propagation environment. In this paper we propose in-band cross-layer network synchronization architecture for TDMA-based MANETs. In the proposed architecture TDMA MAC protocol and proactive routing protocol cooperate closely to rapidly detect network partition and merge caused by node mobility and to precisely maintain network synchronization. We also implement the proposed synchronization architecture in OPNET simulator and evaluate the performance of it in various simulation scenarios. Simulation results show that our architecture stably maintains network time synchronization in both network partition and merge situations.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.57-65
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• 2007

In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Fan Ye et al. proposed that statistical en-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a partition value represents a trade off between resilience and energy where the partition value is the total number of partitions which global key pool is divided. If every partition are compromised by an adversary, SEF disables the filtering capability. Also, when an adversary has compromised a very small portion of keys in every partition, the remaining uncompromised keys which take a large portion of the total cannot be used to filter false reports. We propose a fuzzy-based adaptive partitioning method in which a global key pool is adaptively divided into multiple partitions by a fuzzy rule-based system. The fuzzy logic determines a partition value by considering the number of compromised partitions, the energy and density of all nodes. The fuzzy based partition value can conserve energy, while it provides sufficient resilience.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.46-53
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• 2016

In mobile communication systems, location management deals with the location determination of users in a network. One of the strategies used in location management is to partition the network into location areas. Each location area consists of a group of cells. The goal of location management is to partition the network into a number of location areas such that the total paging cost and handoff (or update) cost is a minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is a difficult combinatorial optimization problem. This cell grouping problem is to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking is a minimum in location area wireless network. In fact, this is shown to be an NP-complete problem in an earlier study. In this paper, artificial bee colony (ABC) is developed and proposed to obtain the best/optimal group of cells for location area planning for location management system. The performance of the artificial bee colony (ABC) is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. The important control parameter of ABC is only 'Limit' which is the number of trials after which a food source is assumed to be abandoned. Simulation results for 16, 36, and 64 cell grouping problems in wireless network show that the performance of our ABC is better than those alternatives such as ant colony optimization (ACO) and particle swarm optimization (PSO).

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3262-3271
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• 2000

Continuous media objects, due to large volume and real-time consiraints in their delivery,are likely to consume much network andwidth Generally, proxy servers are used to hold the fiequently requested objects so as to reduce the network traffic to the central server but most of them are designed for text and image dae that they do not go well with continuous media data. So, in this paper, we propose a two-layered network cache management policy for continuous media object delivery on the wide area networks. With the proposed cache management scheme,in cach LAN, there exists one LAN cache and each LAN is further devided into a group of sub-LANs, each of which also has its own sub-LAN eache. Further, each object is also partitioned into two parts the front-end and rear-end partition. they can be loaded in the same cache or separately in different network caches according to their access frequencics. By doing so, cache replacement overhead could be educed as compared to the case of the full size daa allocation and replacement , this eventually reduces the backbone network traffic to the origin server.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.