• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.47-55
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• 2011

The performance of data transmission from mobile devices to cloud storages is limited by the amount of data being transferred, communication speed and battery consumption of mobile devices. Especially, when the large-scale data communication takes place using mobile devices, such as smart phones, the performance turbulence and power consumption become an obstacle to establish the reliable communication environment. In this paper, we present an efficient data transmission method using USB Hijacking. In our approach, the synchronization to transfer a large amount of data between mobile devices and user PC is executed by using USB Hijacking. Also, there is no need to concern about data capacity and battery consumption in the data communication. We presented several experimental results to verify the effectiveness and suitability of our approach.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1518-1525
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• 2021

Due to the growth of VR industry and rise of digital twin industry, the importance of implementing 3D data same as real space is increasing. However, the fact that it requires expertise personnel and huge amount of time is a problem. In this paper, we propose a system that generates point cloud data with same shape and color as a real space, just by scanning the space. The proposed system integrates 3D geometric information from lidar and color information from stereo camera into one point cloud. Since the number of 3D points generated by lidar is not enough to express a real space with good quality, some of the pixels of 2D image generated by camera are mapped to the correct 3D coordinate to increase the number of points. Additionally, to minimize the capacity, overlapping points are filtered out so that only one point exists in the same 3D coordinates. Finally, 6DoF pose information generated from lidar point cloud is replaced with the one generated from camera image to position the points to a more accurate place. Experimental results show that the proposed system easily and quickly generates point clouds very similar to the scanned space.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.272-276
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• 2000

Reverse engineering refers to the process that creates a physical part from acquiring the surface data of an existing part using a scanning device. In recent years, as the non-contact type scanning devices become more popular, the huge amount of point data can be obtained with high speed. The point data handling process, therefore, becomes more important since the scan data need to be refined for the efficiency of subsequent tasks such as mesh generation and surface fitting. As one of point handling functions, the cross-sectioning function is still frequently used for extracting the necessary data from the point cloud. The commercial reverse engineering software supports cross-sectioning functions, however, these are only for cross-sectioning the point cloud with the constant spacing and direction. In this paper, adaptive cross-sectioning point cloud which allow the changes of the spacing and directions of cross-sections according to the constant spacing and direction. In this paper, adaptive cross-sectioning algorithms which allow the changes of the spacing and directions of cross-sections according to the curvature difference of the point cloud data are proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2717-2738
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• 2024

Cloud computing has extensively grown in recent years. A large amount of data is stored in cloud servers. To ensure confidentiality, these data is often encrypted and then stored in cloud servers. However, encryption makes range queries difficult to perform. To solve this issue, we present a scheme that facilitates fast range queries on encrypted multi-dimensional data in scenarios involving multiple users. In our scheme, we construct a tree index on encrypted multi-dimensional data, and each node is linked to a secure enhanced multi-dimensional range (MDR). To support efficient range query on the tree index, we adopt bloom filter technique. Additionally, users' privileges are designed in a one-way calculation manner to support that different users can only perform range queries within their own privileges. Finally, we conduct extensive experiments which show the efficiency of our scheme, and also conduct a thorough analysis of its security.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.105-112
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• 2020

In the fourth industrial revolution represented by hyper-connected and intelligence, cloud computing is drawing attention as a technology to realize big data and artificial intelligence technologies. The proliferation of cloud computing has also increased the number of threats. In this paper, we propose one way to effectively monitor to the resources assigned to clients by the IaaS service provider. The method we propose in this paper is to model the use of resources allocated to cloud systems using ARIMA algorithm, and it identifies abnormal situations through the use and trend analysis. Through experiments, we have verified that the client service provider can effectively monitor using the proposed method within the minimum amount of access to the client systems.

• ETRI Journal
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• v.46 no.4
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• pp.683-696
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• 2024

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video-based point-cloud compression in the range of 300-700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real-time point-cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal-to-noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7-5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low-complexity requirements are required in lightweight device environments.

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.147-156
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• 2010

In this paper, we propose 3D cloud animation by cloud modeling method of 2D images retrieved from a meteorological satellite. First, on the satellite images, we locate numerous control points to perform thin-plate spline warping analysis between consecutive frames for the modeling of cloud motion. In addition, the spectrum channels of visible and infrared wavelengths are used to determine the amount and altitude of clouds for 3D cloud image reconstruction. Pre-integrated volume rendering method is used to achieve seamless inter-laminar shades in real-time using small number of slices of the volume data. The proposed method could successfully construct continuously moving 3D clouds from 2D satellite images at an acceptable speed and image quality.