• Journal of Digital Convergence
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• v.19 no.3
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• pp.211-216
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• 2021

Information from various heterogeneous devices is steadily increasing in distributed cloud environments. This is because high-speed network speeds and high-capacity multimedia data are being used. However, research is still underway on how to minimize information errors in big data sent and received by heterogeneous devices. In this paper, we propose a deep learning-based asymmetric storage management technique for minimizing bandwidth and data errors in networks generated by information sent and received in cloud environments. The proposed technique applies deep learning techniques to optimize the load balance after asymmetric hash of the big data information generated by each device. The proposed technique is characterized by allowing errors in big data collected from each device, while also ensuring the connectivity of big data by grouping big data into groups of clusters of dogs. In particular, the proposed technique minimizes information errors when storing and managing big data asymmetrically because it used a loss function that extracted similar values between big data as seeds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2699-2707
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• 2013

We proposed the context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm at Mobile Ad-hoc Network(MANET). MANET has problem about dynamic topology, the lack of scalability of the network by mobile of node. By mobile of node, energy consumption rate is different. So it is important choosing routing algorithms for the minium of energy consumption rate. DDV-hop algorithms considers of the attribute of mobile node, create a cluster and maintain. And it provides a path by searching a route more energy efficient. We apply mobile of node by direction and time, the alogorighm of routning path and energy efficiency clustering is provided, it is shown the result of enery consumption that is optimized for the network.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.83-93
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• 2012

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.433-441
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• 2003

We propose a new resource reservation scheme called MC-HMRSVP(Mobile clustering based-Hierarchical mobile ReSource reserVation Protcol) that is an extension of HMRSVP[3,4]. MC-HMRSVP always establishes a virtual cluster called Mobile Cluster, which includes its immediately adjacent MA(Mobile Agent) for passive reservation as well as the current MA for active reservation to which MH(Mobile Host) belongs. Our scheme also establishes the MC regardless of intra/inter region movement by GMA(Gateway Mobile Agent) function when a MH moves. To provide a general formulation on analyzing the performance in terms of reservation cost, we also model the resource reservation cost by using a simple recursive equation. Then, we show that our scheme decreases the reservation cost in comparison with the existing HMRSVP extentions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8A
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• pp.791-800
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• 2010

Recently wireless sensor networks as a field of ubiquitous computing technology was in the limelight. To use and collect the necessary information, Sink node mobility is essential. TTDD(Two-Tier Data Dissemination) proposed most common technique associated with Mobile sink node in wireless sensor networks, but issues exist that the use of many control packet falls into the energy efficiency. The technique for solving problems is Cluster-Based Energy-efficient Routing protocol (CBPER). But CBPER does not transmit the data correctly to sink node or source node. In this paper, we propose An Energy-Efficient Routing Protocol based on Static Grid using mobile sink nodes in order to solve the data transmission failure and reduce the energy consumption in Wireless Sensor Networks. We have evaluated it with the NS-2 simulator. Our results show that the proposed protocol saves the energy consumption up to 34% in comparison with CBPER. We also prove that the proposed protocol can transmit more accurate data to the sink de than CBPER.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.74-84
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• 2009

GIS based new postal marketing method is presented in this paper with spatiotemporal mining to cope with domestic mail volume decline and to strengthening competitiveness of postal business. Market segmentation technique for socialogy of population and spatiotemporal prediction of consumer propensity to purchase through spatiotemporal multi-dimensional analysis are suggested to provide meaningful and accurate marketing information with customers. Internal postal acceptance & external statistical data of local districts in the Seoul Metropolis are used for the evaluation of geo-lifestyle clustering and spatiotemporal cube mining. Successfully optimal 14 maketing clusters and spatiotemporal patterns are extracted for the prediction of consumer propensity to purchase.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1146-1154
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• 2010

A virtual machine lease service in the cloud computing environment has been implemented using the open source cloud computing platform, OpenNebula. In addition, a web-based cloud user interface is developed for both convenient resource management and efficient service access. The present virtual machine allocation scheme adopted in OpenNebula has performance reduction problem because of not considering CPU allocation scheduler of the virtualization software. In order to address this problem we have considered both the priority of the idle CPU resources of the cluster and credit scheduler of Xen, which resulted in performance improvement of the OpenNebula virtual machine scheduler. The experimental results showed that the proposed allocation scheme provided more virtual machine creations and more CPU resource allocations for cloud service.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.27-31
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• 2013

In this paper, we propose a new quantization method using fuzzy C-means clustering(FCM) for lumbar ultrasound image recognition. Unlike usual histogram based quantization, our method first classifies regions into 10 clusters and sorts them by the central value of each cluster. Those clusters are represented with different colors. This method is efficient to handle lumbar ultrasound image since in this part of human body, the brightness values are distributed to doubly skewed histogram in general thus the usual histogram based quantization is not strong to extract different areas. Experiment conducted with 15 real lumbar images verified the efficacy of proposed method.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.41-47
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• 2010

It is very important to prolong the lifetime of wireless sensor networks by using their limited energy efficiently, since it is not possible to change or recharge the battery of sensor nodes after deployment. LEACH protocol is a typical routing protocol based on the clustering scheme for the efficient use of limited energy. It is composed of a few clusters, which consist of head nodes and member nodes. Though LEACH starts from the supposition that all nodes have data transferred to a head, there must be some nodes having useless data in actual state. In this paper we propose a power saving scheme by making a member node dormant if previous sensed data and current data is same. We evaluate the performance of the proposed scheme in comparison with original clustering algorithms. Simulation results validate our scheme has better performance in terms of the number of alive nodes as time evolves.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.609-616
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• 2023

In a wireless network environment, since sensors are not always connected to power, the life of a battery, which is an energy source supplied to sensors, is limited. Therefore, various studies have been conducted to extend the network life, and a layer-based routing protocol, LEACH(: Low-energy Adaptive Clustering Hierarchy), has emerged for efficient energy use. However, the LEACH protocol, which transmits fused data directly to the sink node, has a limitation in that it consumes as much energy as the square of the transmission distance when transmitting data. To improve these limitations, this paper proposes an algorithm that can minimize the transmission distance with multi-hop transmission where cluster heads are chained between cluster heads through relative distance calculation from sink nodes in every round.