• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1144-1158
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• 2016

In this paper, delay-constrained data transmission is considered over error-prone networks. Network coding is deployed for efficient information exchange, and an approximate decoding approach is deployed to overcome potential all-or-nothing problems. Our focus is on determining the cluster size and its impact on approximate decoding performance. Decoding performance is quantified, and we show that performance is determined only by the number of packets. Moreover, the fundamental tradeoff between approximate decoding performance and data transfer rate improvement is analyzed; as the cluster size increases, the data transfer rate improves and decoding performance is degraded. This tradeoff can lead to an optimal cluster size of network coding-based networks that achieves the target decoding performance of applications. A set of experiment results confirms the analysis.

• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.291-298
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• 2010

In wireless sensor networks, energy is the most important consideration because the lifetime of the sensor node is limited by battery. The clustering is the one of methods used to manage network energy consumption efficiently and LEACH(Low-Energy Adaptive Clustering Hierarchy) is one of the most famous clustering algorithms. LEACH utilizes randomized rotation of cluster-head to evenly distribute the energy load among the sensor nodes in the network. The random selection method of cluster-head does not guarantee the number of cluster-heads produced in each round to be equal to expected optimal value. And, the cluster head in a high-density cluster has an overload condition. In this paper, we proposed both a token based cluster-head selection algorithm for guarantee the number of cluster-heads and a cluster selection algorithm for uniform-density cluster. Through simulation, it is shown that the proposed algorithm improve the network lifetime about 9.3% better than LEACH.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.10-14
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• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.61.1-61.1
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• 2020

We examine the HI gas kinematics and distribution of galaxy pairs in group or cluster environment from high-resolution Australian Square Kilometre Array Pathfinder (ASKAP) WALLABY pilot observations. We use 22 well-resolved galaxies in the Hydra cluster of which 4 galaxies are visually identified as pairs and others are isolated ones. We perform profile decomposition of HI velocity profiles of the galaxies using a new tool, BAYGAUD which enables us to separate a line-of-sight velocity profile into an optimal number of Gaussian components based on Bayesian MCMC techniques. All the HI velocity profiles of the galaxies are decomposed into kinematically cold or warm gas components with their velocity dispersion, 4~8 km/s or > 8 km/s, respectively. We derive the mass fraction of the kinematically cold gas with respect to the total HI gas mass, f = log10(M_cold / M_HI), of the galaxies and correlate them with their dynamical mass. The cold gas reservoir of the paired galaxies in the Hydra cluster is found to be relatively higher than that of the isolated ones which show a negative correlation with the dynamical mass in general.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1240-1245
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• 2009

The throughput characteristics of the cluster tool with dual blade robot are analyzed. Using equipment's cycle time chart of the equipment, simple analytic form of the throughput is derived. Then, several important throughput characteristics are analyzed by the throughput formula. First, utilization of the process chamber and the robot are maximized by assigning the equipment to the process whose processing time is near the critical process time. Second, rule for selecting optimal number of process chambers is suggested. It is desirable to select a single process chamber plus a single robot structure for relatively short time process and multi process chambers plus a single robot, namely cluster tool for relatively long time process. Third, throughput variation between equipments due to the wafer transfer time variation is analyzed, especially for the process whose processing time is less than critical process time. And the throughput and the wafer transfer time of the equipments in our fabrication line are measured and compared to the analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.138-143
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• 2006

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.218-223
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• 1995

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature or feature subset among all the available features is selected based on fitness function in genetic algorithm which is inversely proportional to classification error, balance between cluster, number of feature used. The proposed design scheme is applied to the handwtitten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.393-400
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• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• International Journal of Highway Engineering
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• v.19 no.3
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• pp.65-70
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• 2017

PURPOSES : Pavement Management System contains the data that describe the condition of the road. Under limited budget, the data can be utilized for efficient plans. The objective of this research is to develop a mixed integer program model that maximizes remaining durable years (or Lane-Kilometer-Years) in road maintenance planning. METHODS : An optimization model based on a mixed integer program is developed. The model selects a cluster of sectors that are adjacent to each other according to the road condition. The model also considers constraints required by the Seoul Metropolitan Facilities Management Corporation. They select two lanes at most not to block the traffic and limit the number of sectors for one-time construction to finish the work in given time. We incorporate variable cost constraints. As the model selects more sectors, the unit cost of the construction becomes smaller. The optimal choice of the number of sectors is implemented using piecewise linear constraints. RESULTS : Data (SPI) collected from Pavement Management System managed by Seoul Metropolitan City are fed into the model. Based on the data and the model, the optimal maintenance plans are established. Some of the optimal plans cannot be generated directly in existing heuristic approach or by human intuition. CONCLUSIONS:The mathematical model using actual data generates the optimal maintenance plans.