• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.1-10
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• 2014

A cloud computing is one of a parallel and distributed computing. The cloud computing provides some service for user with virtual resources. However, a user's service request does not show a time pattern. As a result, each resource also shows a different availability at the same time. This difference affects a quality of service (QoS) and a resource selection for users. Therefore, we propose the resource availability-based multi auction model for cloud service reservation and resource brokering system. The proposed system is to select the proper resource provider based on the users' request. The proposal adopts the multi phase of the auction to transact resources. The system evaluates the available factor of each resource on the auction phase, and finally reserves the service on the adaptive queue. The proposed model shows the better performance than other existing method.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.371-384
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• 2015

Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.1-8
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• 2020

In order to reach Ultra-Reliable Low-Latency communication, one of 5G aims, Multi-access Edge Computing paradigm was born. The idea of this paradigm is to bring cloud computing technologies closer to the network edge. User services are hosted in multiple Edge Clouds, deployed at the edge of the network distributedly, to reduce the service latency. For mobile users, migrating their services to the most proper Edge Clouds for maintaining a Quality of Service is a non-convex problem. The service migration problem becomes more complex in high mobility scenarios. The goal of the study is to observe how user mobility affects the selection of Edge Cloud during a fixed mobility path. Mobility-Aware Service Migration (MASM) is proposed to optimize service migration based on two main parameters: routing cost and service migration cost, during a high mobility scenario. The performance of the proposed algorithm is compared with an existing greedy algorithm.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.215-223
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• 2013

As cloud computing is activated, a variety of cloud services are being distributed. However, to use each different cloud service, you must perform a individual user authentication process to service. Therefore, not only the procedure is cumbersome but also due to repeated authentication process performance, it can cause password exposure or database overload that needs to have user's authentication information each cloud server. Moreover, there is high probability of security problem that being occurred by phishing attacks that result from different authentication schemes and input scheme for each service. Thus, when you want to use a variety of cloud service, we proposed OpenID based user authentication scheme that can be applied to a multi-cloud environment by the trusted user's verify ID provider.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.727-736
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• 2019

In this paper, we present how to create a point cloud for a target area using multiple unmanned aerial vehicle images and to remove the gaps and overlapping points between datasets. For this purpose, first, IBA (Incremental Bundle Adjustment) technique was applied to correct the position and attitude of UAV platform. We generate a point cloud by using MDR (Multi-Dimensional Relaxation) matching technique. Next, we register point clouds based on observation vectors between stereo images by doing this we remove gaps between point clouds which are generated from different stereo pairs. Finally, we applied an occupancy grids based integration algorithm to remove duplicated points to create an integrated point cloud. The experiments were performed using UAV images, and our experiments show that it is possible to remove gaps and duplicate points between point clouds generated from different stereo pairs.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.429-436
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• 2017

There is a growing tendency to outsource sensitive or important data in cloud computing recently. However, it is very important to protect the privacy of outsourced data. So far, a variety of secure and efficient multi-keyword search schemes have been proposed in cloud computing environment composed of a single data owner and multiple data users. Zhang et. al recently proposed a search protocol based on multi-keyword in cloud computing composed of multiple data owners and data users but their protocol has two problems. One is that the cloud server can illegally infer the relevance between data files by going through the keyword index and user's trapdoor, and the other is that the response for the user's request is delayed because the cloud server has to execute complicated operations as many times as the size of the keyword index. In this paper, we propose an improved multi-keyword based search protocol which protects the privacy of outsourced data under the assumption that the cloud server is completely unreliable. And our experiments show that the proposed protocol is more secure in terms of relevance inference between the data files and has higher efficiency in terms of processing time than Zhang's one.

• Journal of Convergence for Information Technology
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• v.8 no.2
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• pp.77-82
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• 2018

Recently, as cloud services become popular with general users, users' information is freely transmitted and received among the information used in the cloud environment, so security problems related to user information disclosure are occurring. we propose a method to secure personal information of multiple users by making personal information stored in the cloud server and a key for accessing the shared information so that the privacy information of the multi users using the cloud service can be prevented in advance do. The first key used in the proposed scheme is a key for accessing the user 's personal information, and is used to operate the information related to the personal information in the form of a multi - layer. The second key is the key to accessing information that is open to other users than to personal information, and is necessary to associate with other users of the cloud. The proposed scheme is constructed to anonymize personal information with multiple hash chains to process multiple kinds of information used in the cloud environment. As a result of the performance evaluation, the proposed method works by allowing third parties to safely access and process the personal information of multiple users processed by the multi - type structure, resulting in a reduction of the personal information management cost by 13.4%. The efficiency of the proposed method is 19.5% higher than that of the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2585-2598
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• 2015

Hand posture recognition has played a very important role in Human Computer Interaction (HCI) and Computer Vision (CV) for many years. The challenge arises mainly due to self-occlusions caused by the limited view of the camera. In this paper, a robust hand posture recognition approach based on 3D point cloud from two RGB-D sensors (Kinect) is proposed to make maximum use of 3D information from depth map. Through noise reduction and registering two point sets obtained satisfactory from two views as we designed, a multi-viewed hand posture point cloud with most 3D information can be acquired. Moreover, we utilize the accurate reconstruction and classify each point cloud by directly matching the normalized point set with the templates of different classes from dataset, which can reduce the training time and calculation. Experimental results based on posture dataset captured by Kinect sensors (from digit 1 to 10) demonstrate the effectiveness of the proposed method.

• International Journal of Contents
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• v.12 no.4
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• pp.23-30
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• 2016

For a library to be able provide information services and fulfill its function as a knowledge convergence center capable of responding to various information demands, the development of next-generation information systems based on the latest information and communication technology is needed. The development of mobile information services using portable devices such smart phones and tablet PCs and information systems which incorporate the concepts of cloud computing, SaaS (Software as a Service), annotation and Library2.0 is also required. This paper describes a library information system that utilizes collective intelligence and cloud computing. The information system developed for this study adopts the SaaS-based cloud computing service concept to cope with the shift in the mobile service paradigm in libraries and the explosion of electronic data. The strengths of such a conceptual model include the sharing of resources, support of multi-tenants, and the configuration and support of metadata. The user services are provided in the form of software on-demand. To test the performance of the developed system, the efficiency analysis and TTA certification test were conducted. The results of performance tests, It is encouraging that, at least up to 100MB, the job time is approximately linear and with only a moderate overhead of less than one second. The system also passed the level-3 or higher criteria in the certification test, which includes the SaaS maturity, performance and application program functions.