• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.05a
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• pp.145-148
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• 2011

As a futuristic construction model, building information model(BIM) based project management system(PMIS) and mobile PMIS have been showing visible sign. However, researches on the 3D BIM based PMIS using mobile device are hard to find, result from limitation of mobile device application(slow speed at huge BIM file, display size, and etc.) and undefined standard of business processes. Therefore, this research aims at studying feasibility of mobile BIM PMIS based on cloud computing as a business model. In case of applying mobile BIM PMIS, 3D drawings and integrated building informations are possible on mobile devices in real time. it would support increasing the productivity of project participants as designer, engineer, supervisor, and etc. Globally, BIM based PMIS and Mobile BIM system, cloud computing based mobile BIM simulator are in the concept or experimental phase, therefore it is possible to secure global leading technology of IT and construction merger in the mobile BIM.

• Journal of KIISE
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• v.42 no.11
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• pp.1361-1371
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• 2015

Depending on the increase amongst various cloud services, the technology of the Cloud Service Broker (CSB) to find the most appropriate services to meet the needs of cloud service consumers has emerged. In order to advance for cloud services to be used through the CSB, it is important to ensure the quality level that meets the demands of consumers through a negotiation process based on the Service Level Agreement (SLA). However, quality metrics of cloud services are different from each other based on the measurement scale, which represents the quality level, and the calculation for each type of cloud services. Therefore, it is necessary to analyze the variability of the quality of cloud services and establish a SLA model for ensuring and improving the level of quality. In this paper, we analyze the quality metrics for the specific type of cloud services by applying the cross-cutting concept and propose a Virtual SLA (VSLA) meta-model.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.79-90
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• 2019

As an increasing number of cloud service providers begin to provide cloud computing services, they form a competitive market to compete for users. Due to different resource configurations and service workloads, users may observe different response times for their service requests and experience different levels of service quality. To compete for cloud users, it is crucial for each cloud service provider to determine an optimal price that best corresponds to their service qualities while also guaranteeing maximum profit. To achieve this goal, the underlying rationale and characteristics in this competitive market must be clarified. In this paper, we analyze price competition in the multimedia cloud service market with two service providers. We characterize the nature of non-cooperative games in a duopoly multimedia cloud service market with the goal of capturing how each cloud service provider determines its optimal price to compete with the other and maximize its own profit. To do this, we introduce a queueing model to characterize the service process in a multimedia cloud data center. Based on performance measures of the proposed queueing model, we suggest a price competition problem in a duopoly multimedia cloud service market. By solving this problem, we can obtain the optimal equilibrium prices.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.181-186
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• 2014

Web data mining aims at discovering useful knowledge from various Web resources. There is a growing trend among companies, organizations, and individuals alike of gathering information through Web data mining to utilize that information in their best interest. In science, cloud computing is a synonym for distributed computing over a network; cloud computing relies on the sharing of resources to achieve coherence and economies of scale, similar to a utility over a network, and means the ability to run a program or application on many connected computers at the same time. In this paper, we propose a new system framework based on the Hadoop platform to realize the collection of useful information of Web resources. The system framework is based on the Map/Reduce programming model of cloud computing. We propose a new data mining algorithm to be used in this system framework. Finally, we prove the feasibility of this approach by simulation experiment.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.342-352
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• 2012

As a futuristic construction model, building information model (BIM) based project management system (PMIS) and mobile BIM simulator apps have been showing visible sign. However, researches on the BIM based 3D simulator using mobile device are hard to find result from limitation of mobile device (slow speed at huge 3D file, display size, and etc.) and undefined standard of business processes. Therefore, this research aims at studying application of mobile BIM apps based on cloud computing. Total 8 BIM cloud apps were selected and analyzed in the 5 application feasibility characteristics (speed, view, inquiry, markup, and usability). This research would be essential phase to construct BIM based mobile project management system using cloud computing in the future.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.95-100
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• 2022

Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.