• Journal of information and communication convergence engineering
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• 제16권3호
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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.

• 한국CDE학회논문집
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• 제14권1호
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2011년도 춘계 학술논문 발표대회 1부
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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.

• 정보과학회 논문지
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• 제42권11호
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• pp.1361-1371
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• 2015

클라우드 서비스가 증가하고 다양해짐에 따라 클라우드 서비스 사용자들의 요구사항을 만족하는 가장 적절한 서비스를 찾아주는 클라우드 서비스 브로커(CSB) 기술이 나타났다. CSB를 통해 클라우드 서비스가 사용되는 것을 촉진시키기 위해서는 서비스 수준 협약(SLA) 기반의 협상 과정을 통해 사용자들의 요구사항에 맞는 품질 수준을 보장하는 것이 중요하다. 그러나 클라우드 서비스 유형 별로 품질 수준을 표현하는 측정 기준과 척도, 계산 방식이 서로 상이하다. 따라서 클라우드 서비스의 품질에 대한 가변성을 분석하고, 분석된 품질 수준을 보장 및 개선하기 위한 SLA 모델이 필요하다. 본 논문에서는 cross-cutting 개념을 적용하여 클라우드 서비스의 유형 별 품질 메트릭을 분석하고 사용자와 제공자 간의 품질 수준에 대한 협상을 담당하는 VSLA(Variable SLA) 메타모델을 제안한다.

• 한국콘텐츠학회논문지
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• 제19권4호
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• pp.79-90
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• 2019

최근 들어 다수의 클라우드 서비스 제공자가 클라우드 컴퓨팅 서비스를 제공함으로써 각 제공자는 더 많은 사용자를 확보하기 위해 치열한 경쟁을 벌이고 있다. 서비스 제공자별 컴퓨팅 자원의 구성 및 서비스 제공 부하가 다르기 때문에 사용자는 다양한 수준의 서비스 품질을 경험할 수 있다. 따라서 클라우드 서비스 시장에서 더 많은 사용자를 확보하여 수익을 최대화하기 위해서는 서비스 품질에 대한 가장 합리적인 가격을 결정하는 것이 매우 중요하다. 본 연구에서는 두 명의 서비스 제공자가 존재하는 멀티미디어 클라우드 서비스 시장에서 두 제공자 간 서비스 가격 경쟁에 대해 다룬다. 두 명의 클라우드 서비스 제공자가 최적의 가격을 결정하여 상호 경쟁하고 자신의 이익을 최대화할 수 있는 가격 산정 방법을 비협력 게임 이론으로 설명한다. 이를 위해 멀티미디어 클라우드 서비스의 제공 프로세스를 대기행렬 시스템으로 모형화하고, 분석 결과를 바탕으로 복점 멀티미디어 클라우드 서비스 시장에서 가격 경쟁 문제를 제안한다.

• Journal of Computing Science and Engineering
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• 제8권4호
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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.

• 한국CDE학회논문집
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• 제17권5호
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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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• 제11권4호
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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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• 제22권3호
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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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• 제19권4호
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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.