• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.367-372
• /
• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.3
• /
• pp.375-386
• /
• 2024

Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

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

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.12
• /
• pp.81-87
• /
• 2018

Cloud computing is a computing environment in which users borrow as many IT resources as they need to, and use them over the network at any point in time. This is the concept of leasing and using as many IT resources as needed to lower IT resource usage costs and increase efficiency. Recently, cloud computing is emerging to provide stable service and volume of data along with major technological developments such as the Internet of Things, artificial intelligence and big data. However, for a more secure cloud environment, the importance of perimeter security such as shared resources and resulting secure data storage and access control is growing. This paper analyzes security threats in cloud computing environments and proposes a security architecture for effective response.

• Asian Journal of Atmospheric Environment
• /
• v.8 no.4
• /
• pp.175-183
• /
• 2014

To visually and chemically verify the rainout of soot particles, a model experiment was carried out with the cylindrical chamber (0.2 m (D) and 4 m (H)) installing a cloud drop generator, a hydrotherometer, a particle counter, a drop collector, a diffusing drier, and an artificial soot particle distributer. The processes of the model experiment were as follows; generating artificial cloud droplets (major drop size : $12-14{\mu}m$) until supersaturation reach at 0.52%-nebulizing of soot particles (JIS Z 8901) with an average size of $0.5{\mu}m$-counting cloud condensation nuclei (CCN) particles and droplets by OPC and the fixation method (Ma et al., 2011; Carter and Hasegawa, 1975), respectively - collecting of individual cloud drops - observation of individual cloud drops by SEM - chemical identifying of residual particle in each individual droplet by SEM-EDX. After 10 minutes of the completion of soot particle inject, the number concentrations of PM of all sizes (> $0.3{\mu}m$) dramatically decreased. The time required to return to the initial conditions, i.e., the time needed to CCN activation for the fed soot particles was about 40 minutes for the PM sized from $0.3-2.0{\mu}m$. The EDX spectra of residual particles left at the center of individual droplet after evaporation suggest that the soot particles seeded into our experimental chamber obviously acted as CCN. The coexistence of soot and mineral particle in single droplet was probably due to the coalescence of droplets (i.e., two droplets embodying different particles (in here, soot and background mineral particles) were coalesced) or the particle capture by a droplet in our CCN chamber.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.833-849
• /
• 2021

Cloud accounting represents a new area of accounting information systems. Past research has often focused on accounting information systems and its antecedents, rather than factors that adopt cloud accounting system. The purpose of this paper is to explain the factors that influence the adoption of cloud accounting in the financial sectors. This paper applied the technology acceptance model (TAM), technology-organization-environment, and the De Lone and Mc Lean model, coupled with proposed factors relevant to cloud accounting. The proposed model was empirically evaluated using survey data from 187 managers (financial managers, IT department managers, audit managers, heads of accounting departments, and head of internal control departments) in Jordanian bank branches. Based on the SEM results, top management support, organizational competency, service quality, system quality, perceived usefulness, and perceived ease of use had a positive relationship with the intention of using cloud accounting. Cloud accounting adoption positively affected cloud accounting usage. This paper contributes to a theoretical understanding of factors that activate the adoption of cloud accounting. For financial firms in general the results enable them to better develop cloud accounting framework. The paper verifies the factors that affect the adoption of cloud accounting and the proposed cloud accounting model.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.34-37
• /
• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Journal of Information Technology Applications and Management
• /
• v.30 no.6
• /
• pp.91-111
• /
• 2023

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R² and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R² and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R² by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R² by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.3
• /
• pp.63-67
• /
• 2024

Recently, artificial intelligence (AI) workloads encompassing various industries such as smart logistics, FinTech, and entertainment are being executed on the cloud. In this paper, we address the scheduling issues of various AI workloads on a multi-tenant cloud system composed of heterogeneous GPU clusters. Traditional scheduling decreases GPU utilization in such environments, degrading system performance significantly. To resolve these issues, we present a new scheduling approach utilizing genetic algorithm-based optimization techniques, implemented within a process-based event simulation framework. Trace driven simulations with diverse AI workload traces collected from Alibaba's MLaaS cluster demonstrate that the proposed scheduling improves GPU utilization compared to conventional scheduling significantly.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.2
• /
• pp.115-120
• /
• 2021

In Korea, where the world's first cloud computing development law was created, cloud service technology has been developing so far, and the industries to which artificial intelligence and big data technologies can be applied based on this are increasing. It is important for domestic and overseas cloud operators to secure many partners in order to provide optimal services to users. It is also important to systematically develop the partner's technology and discover new partners. In particular, the public, medical, and financial sectors are industrial fields that are difficult for domestic as well as global cloud service providers to expand without the help of partners. This study analyzes partner policies for industries caused by domestic regulations through domestic and foreign cases, and aims to establish partner management policies optimized for the domestic environment.