• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.165-173
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• 2007

In this paper, we propose a new minimum resources allocation algorithm for optimal design automation. In the proposed algorithm, the operation are allocated to functional units so that the number of interconnection wires between functional units can be minimized. The registers are allocated to the maximal clusters generated by the minimal cluster partitioning algorithm. Finally, the interconnection is minimized by removing the duplicated inputs of multiplexers and exchanging the inputs across multiplexers. The efficiency of the proposed allocation algorithm is shown by experiments using benchmark examples.

• Journal of the Chungcheong Mathematical Society
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• v.33 no.1
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• pp.33-41
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• 2020

We introduced the concept of the 𝜖₀-density and the 𝜖₀-dense ace in [1]. This concept is related to the structure of employment. In addition to the double capacity theorem which was introduced in [1], we need the minimal dense subset. In this paper, we investigate a concept of the minimal 𝜖₀-dense subset in the Euclidean m dimensional space.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.79-86
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• 2009

In this paper, we propose a new clustering scheme that provides optimal data aggregation effect and reduces energy consumption of nodes by considering the density of nodes when forming clusters. Since the size of the cluster is determined to ensure optimal data aggregation rate, our scheme reduces transmission range and minimizes interference between clusters. Moreover, by clustering using locally adjacent nodes and aggregating data received from cluster members, we reduce energy consumption of nodes. Through simulation, we confirmed that energy consumption of the whole network is minimized and the sensor network life-time is extended. Moreover, we show that the proposed clustering scheme improves the performance of network compared to previous LEACH clustering scheme.

• ETRI Journal
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• v.39 no.3
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• pp.353-363
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• 2017

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.30.1-30.1
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• 2009

We present extra-tidal features of spatial configuration of stars around three metal-poor globular clusters (NGC 6273, NGC 6266, NGC 6681) located in the Galactic bulge. The accurate wide-field photometric data were obtained in BVI bands with the MOSAICII camera at CTIO Blanco 4m telescope. The derived color-magnitude diagrams (CMDs) covered a total $71'\times71'$ area including a cluster and its surrounding field outside of the tidal radius of the cluster. Applying the statistical technique of the CMD-mask algorithm, we minimized the field star contaminations on the obtained CMDs and chose properly the cluster's member stars. On the spatial stellar density maps around the target clusters, we found overdensity features beyond the tidal radii of the clusters. We also found that the radial density profiles of the clusters show departures from the best-fit King model for the outer region of clusters. The results add further observational evidence that the observed metal-poor bulge clusters would be originated from accreted satellite systems, indicative of the merging scenario of the formation of the Galaxy.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.728-737
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• 2019

In this study, the minimized on-board computer (OBC) module for integrated navigation is developed, which provides satellites' relative position information in formation flying and cluster operation situations. The scalability is considered to apply the user-selected wireless communication module and Global Positioning System (GPS) receiver for navigation, while considering to meet the structural design standard of nano-satellites. As a result of the product development and production, the processing speed of integrated navigation and real-time data synchronization is satisfied for cluster operation nano-satellites by using micro controller unit (MCU). From a heat/vacuum, vibration and radiation test, the OBC was confirmed to be operated in space environments. From these results, a mass production system of OBC was made which is a key part of development on satellite formation flying and cluster/constellation missions that the community demands are increasing.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2670-2685
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• 2020

Flying ad-hoc networks (FANETs) is a vibrant research area nowadays. This type of network ranges from various military and civilian applications. FANET is formed by micro and macro UAVs. Among many other problems, there are two main issues in FANET. Limited energy and high mobility of FANET nodes effect the flight time and routing directly. Clustering is a remedy to handle these types of problems. In this paper, an efficient clustering technique is proposed to handle routing and energy problems. Transmission range of FANET nodes is dynamically tuned accordingly as per their operational requirement. By optimizing the transmission range packet loss ratio (PLR) is minimized and link quality is improved which leads towards reduced energy consumption. To elect optimal cluster heads (CHs) based on their fitness we use k-means. Selection of optimal CHs reduce the routing overhead and improves energy consumption. Our proposed scheme outclasses the existing state-of-the-art techniques, ACO based CACONET and PSO based CLPSO, in terms of energy consumption and cluster building time.

• ETRI Journal
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• v.29 no.5
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• pp.545-558
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• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.