• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.720-722
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• 2015

In this paper, we propose a new type of integrated wireless control system using a cloud-based AP Controller. With this system, network administrator can control wireless network in head office and branch hierarchically. In head office, they have an AP Controller Manager, it can control all Access Points, Access point Controllers in their networks. In addition, if we need to install new Access point Controller because of the increasing number of Access Point, the process of making virtual Access point Controller can be automated. This paper presents an architecture of the integrated wireless control system, as well as describes its components and protocols.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.78-88
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• 2016

Recently BIM technology has been expanded for using in construction project. However its spread has been delayed than the initial expectations, due to the high-cost of BIM infrastructure development, the lack of regulations, the lack of process and so forth. In design phase, especially, collaboration based on BIM system has being a key factor for successful next generation building project. Through the analysis of current research trend about IT technologies, virtualization and BIM service, data exchange such as drawing, 3D model, object data, properties using cloud computing and virtual server system is defined as a most successful solution. In various industrial fields, cloud computing technology is utilized as a promising solution which can reduce time and cost of hardware infrastructure. Among the cloud computing technology, VDI is receiving a great deal of attention from it market as an essential part cloud computing. VDI enables to host multiple individual virtual machines by using hypervisor. It has an advantage to easy main device management. Therefore, this study implements a step-by-step user's DaaS by analyzing the desktop resource data of the workers from Pre-design phase to Schematic design, Design develop and Construction design phase. It also develops BIM environment based on test of BIM modeler and designers in architectural design firm. The goal of the study is to enable the cloud computing BIM server. It provides cost saving, high-performance quality of working environment and cooperation's convenience and high security when doing BIM work in small and medium sized architectural design firm.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.89-112
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• 2015

Recently, the business environment of healthcare has changed rapidly due to the entering the mobile era, the intensifying global competition, and the explosion of healthcare needs. Despite of necessity in expanding new IT-based medical services and investing IT resources to respond environmental changes, the small and medium sized hospitals could not realize these requirements due to the limited management resources. CHISSMH is designed and presented in this research to provide high valued clouding medical services with reasonable price. CHISMH is designed and presented in this research to provide high valued medical services with reasonable price through cloud computing. CHISME is designed to maximize resource pooling and sharing through the visualization. By doing so, Cloud Service provider could minimize maintenance cost of cloud data center, provide high level services with reasonable pay-per-use price. By doing so, Cloud Service provider could minimize maintenance cost of cloud data center, and could provide high level services with reasonable pay-per-use price. CHISME is expected to be base framework of cloud HIS services and be diffusion factor of cloud HIS services Operational experience in CHISSMH with 15 hospitals is analyzed and presented as well.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1498-1508
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• 2011

A cloud service customer has no other way but to wait for his lost data to be recovered by the cloud service provider when the data was lost or not accessible for a while due to the provider's system failure, cracking attempt, malfunction, or outage. We consider a solution to address this problem that can be implemented in the cloud client's domain, rather than in the cloud service provider's domain. We propose a high level architecture and scheme for successfully retrieving data units even when several cloud storages are not accessible at the same time. The scheme is based on a clever way of partitioning and partial overlapping of data for being stored on multiple cloud storages. In addition to providing a high level of data availability, the scheme makes it possible to re-encrypt data units with new keys in a user transparent way, and can produce the complete log of every user's data units accessed, for assessing data disclosure, if needed.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.13-21
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• 2023

Recently, due to the collapse of boundaries between fields caused by COVID-19, the government's goal of connecting all data and making it accessible to people, businesses, and governments has garnered attention. To achieve this goal, cloud technology is consistently mentioned, and since the use of cloud technology inevitably raises security concerns, various studies are being conducted on the topic. This paper analyzes the use of cloud technology in public and administrative institutions in Korea and presents a model that applies the U.S. government's TIC 3.0 concept to mitigate potential security issues. The objective is to provide a secure cloud service utilization model for public and administrative institutions, with reference to TIC 3.0.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.85-92
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• 2022

Demand for big data analysis and AI developers is increasing, but there is a lack of an education base to supply them. In this paper, by developing a cloud-based artificial intelligence education platform, the goal was to establish an environment in which practical practical training can be efficiently learned at low cost at educational institutions and IT companies. The development of the education platform was carried out by planning scenarios for each user, architecture design, screen design, implementation of development functions, and hardware construction. This training platform consists of a containerized workload, service management platform, lecture and development platform for instructors and students, and secured cloud stability through real-time alarm system and age test, CI/CD development environment, and reliability through docker image distribution. The development of this education platform is expected to expand opportunities to enter new businesses in the education field and contribute to fostering working-level human resources in the AI and big data fields.