• 정보처리학회지
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• 제11권6호
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1369-1389
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• 2024

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dual-module design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies.

• 한국통신학회논문지
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• 제37B권9호
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• pp.806-813
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• 2012

클라우드 컴퓨팅 환경에서 Infrastructure as a Services (IaaS), Platform as a Services (PaaS), Software as a Services (SaaS), Desktop as a Services (DaaS) 등 다양한 솔루션들이 지속적으로 제공되고 있다. 현재는 사용자 단말이 클라우드 서비스를 제공받으면서 이동성을 보장하기 위한 솔루션으로 Mobile as a Services(MaaS)가 가장 많은 주목을 받고 있다. 사용자는 이동을 하면서도 클라우드에 있는 데이터 및 어플리케이션에 대한 접근과 이용이 가능해야 한다. 다시 말해 모바일 Thin-Client 환경에서 클라우드와 통신, 이동성 지원은 필수 요소이다. 모바일 단말의 이동성을 지원하기 위해 MobileIPv6 (MIPv6) 및 Proxy Mobile IPv6 (PMIPv6)가 소개되면서 많은 연구가 진행되고 있다. 또한, PMIPv6에 대한 연구는 도메인 내에 패킷 손실을 우려한 최적 경로 설정, 빠른 핸드 오버 등의 개선방안이 많이 제시된 바 있다. 본 논문은 모바일 Thin-Client 지원을 위해 PMIPv6 및 클라우드 연동 시스템을 제안한다. 제안한 시스템에서 모바일 Thin-Client가 서비스를 지원받기 위해 Replica서버를 이용하여 원활한 서비스를 제공하는 기법을 제안하며 성능평가를 통하여 기존 PMIPv6에서 핸드오버에 대한 데이터 비용을 비교 분석할 것이다.

• Journal of Platform Technology
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• 제9권1호
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• pp.15-22
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• 2021

In a smart manufacturing environment, more and more devices are connected to the Internet so that a large volume of data can be obtained during all phases of the product life cycle. The large-scale industries, companies and organizations that have more operational units scattered among the various geographical locations face a huge resource consumption because of their unorganized structure of sharing resources among themselves that directly affects the supply chain of the corresponding concerns. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. The manufacturing units include machinery that may be situated in different geological areas and process instances that are executed from different machinery data should be constantly managed by the super admin to coordinate the manufacturing process in the large-scale industries these environments make the manufacturing process a tedious work to maintain the efficiency of the production unit. The data from all these instances should be monitored to maintain the integrity of the manufacturing service system, all these data are computed in the cloud environment which leads to the latency in the performance of the smart manufacturing service system. Instead, validating data from the external device, we propose to validate the data at the front-end of each device. The validation process can be automated by script validation and then the processed data will be sent to the cloud processing and storing unit. Along with the end-device data validation we will implement the APM(Asset Performance Management) to enhance the productive functionality of the manufacturers. The manufacturing service system will be chunked into modules based on the functionalities of the machines and process instances corresponding to the time schedules of the respective machines. On breaking the whole system into chunks of modules and further divisions as required we can reduce the data loss or data mismatch due to the processing of data from the instances that may be down for maintenance or malfunction ties of the machinery. This will help the admin to trace the individual domains of the smart manufacturing service system that needs attention for error recovery among the various process instances from different machines that operate on the various conditions. This helps in reducing the latency, which in turn increases the efficiency of the whole system

• 대한원격탐사학회지
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• 제38권6_3호
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• pp.1761-1775
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• 2022

예측하기 힘든 기후변화로 인해 물 관련 재해의 발생 빈도와 피해 규모도 지속적으로 증가하는 추세이다. 재난관리의 측면에서 광범위한 지역의 피해면적을 파악하고, 중·장기적 예측을 위한 모니터링이 필수적이다. 수재해 분야에서 광역적 모니터링을 위해 Synthetic Aperture Radar (SAR) 위성영상을 활용한 원격탐사 기술 연구가 활발히 진행되고 있다. 수재해 모니터링을 위한 시계열 분석에는 방대한 양의 영상수집과 잡음이 많은 레이더 산란 특성을 고려한 복잡한 전처리과정이 필요하며, 이를 위해 상당한 시간이 소요되는 한계가 있다. 최근 클라우드 컴퓨팅 기술의 발전과 함께 위성 빅데이터를 활용한 시·공간 분석이 가능한 많은 플랫폼들이 제안되고 있다. 구글어스엔진(Google Earth Engine, GEE)은 대표적인 플랫폼으로, 600여개의 위성 자료를 무료로 제공하고 있으며 위성영상의 분석준비데이터를 기반으로 준-실시간 시·공간 분석이 가능하다. 이에 본 연구에서는 구글어스엔진을 활용한 즉각적인 수재해 피해 탐지와 중·장기적 시계열 관측 연구를 수행하였다. 변화탐지에 주로 활용되는 Otsu 기법을 통해 '20년 발생한 집중호우를 중심으로 하천 범람으로 인한 하폭의 변화와 피해 면적을 확인하였다. 또한 재난관리 측면에서 모니터링의 중요성이 요구되는 만큼 상습침수지역으로 선정된 연구대상 지역을 중심으로 '18년부터 '22년까지의 시계열 수체의 변화 경향을 확인하였다. 구글어스엔진은 자바스크립트 기반 코딩을 통한 짧은 처리시간, 시공간 분석과 표출의 강점으로 수재해 분야 활용이 가능할 것으로 판단된다. 더불어 향후 다양한 위성 빅데이터와의 연계를 통해 활용 분야가 확대될 것으로 기대된다.

• 한국빅데이터학회지
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• 제1권2호
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• pp.9-19
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• 2016

ICT 융합에 대한 관심이 높아진 가운데 독일의 Industry 4.0을 시작으로 제조업과 ICT 융합에 대한 연구가 활발하게 진행되고 있다. 이를 통해 전통적인 제조업의 제조단가를 낮추고 극적인 품질향상을 기대할 수 있게 되었다. 최근 정부의 제조업 3.0 전략 등에 힘입어 국내에서도 제조업에 대한 고도화가 진행되고 있으며, 이러한 추세에 발맞추어 제조업 운영에서 발생하는 빅데이터에 대한 주문맞춤형 분석 플랫폼을 개발하고 이를 통해 제조 현장의 경쟁력을 높이고자 한다. 주문맞춤형 분석 플랫폼은 확장성을 고려하여 스프링 프레임워크를 기반으로 웹에서 실행되도록 설계되었으며, 제조업 현장에서 발생하는 다량의 데이터를 빠르게 처리하기 위하여 스파크와 하둡 파일 시스템을 이용한다. 실시간으로 스트리밍 된 데이터를 프로세스 마이닝 기반 알고리즘을 통해 처리하고 공장의 현황을 분석하여 제조업 현장의 문제를 파악하고 신속한 의사결정을 지원할 수 있다.