• 한국안전학회지
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• 제28권1호
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• 한국정보과학회논문지:시스템및이론
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• 제28권11호
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• pp.581-591
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• 2001

본 논문은 이동 컴퓨팅 환경에서 다중 이동 클라이언트를 위한 원격 파일 접근 문제를 다루었다. 이러한 환경에서 사용자의 이동성은 파일 접근에 영향을 미치며 이동 호스트는 제한된 용량의 비휘발성 저장 장치를 가진다는 점에서 자원에 대한 심각한 제약이 따른다. 따라서, 통신 및 연산 부하가 파일접근 지연을 야기할 수 있다. 본 논문은 이러한 문제를 해결하기 위하여 파일 이주 기법을 이용하여 접근 지연을 줄일 수 있는 기법을 제안한다. 이 기법의 목표는 모든 이동 호스트들에게 가능하면 신속하게 요청한 파일을 전달하여 접근지연을 최소화하는데 있다. 우리는 이동 호스트의 파일 요청이 있을 때 서버가 파일 이주를 결정하는 주문형 기법을 개발하였다. 시뮬레이션을 통하여 파일 접근 빈도, 파일 크기, 이동성 등이 파일시스템 접근 지연에 미치는 영향을 실험하였다. 시뮬레이션 결과에서 제안된 이주 기법은 파일 접근 빈도가 높고 이동성이 작은 이동 호스트가 요청한 파일에 대한 접근 지연을 줄이는데 효율적이라는 것을 보였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권2호
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• pp.81-86
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• 2014

본 논문에서는 스마트폰 사용자의 실시간 상황 인식을 위한 효과적인 사운드 분류 시스템을 제안한다. 이 시스템은 전처리 과정에서 입력 사운드 데이터로부터 고요한 사운드와 화이트 노이즈를 미리 여과해버림으로써, 제한적인 계산 자원의 불필요한 소모를 막을 수 있다. 또한 에너지 레벨이 낮은 사운드 데이터들은 사전에 증폭시킴으로써, 이들에 대한 분류 성능을 향상시킬 수 있다. 또, 제안하는 사운드 분류 시스템은 HMM 분류 모델의 효율적인 학습과 적용을 위해 k-평균 군집화를 이용하여 특징 벡터들에 대한 차원 축소와 이산화를 수행한다. 한 대학 연구동내 일상생활로부터 수집한 8가지 유형의 사운드 데이터 집합을 이용하여 제안한 시스템의 성능 분석 실험을 수행하였다. 이 실험을 통해 본 논문에서 제안한 사운드 분류 시스템은 높은 분류 성능을 보여 주었다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.248-256
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• 2021

Now that we're in the big data era, data has taken on a new significance as the storage capacity has exploded from trillion bytes to petabytes at breakneck pace. As the use of cloud computing expands and becomes more commonly accepted, several businesses and institutions are opting to store their requests and data there. Cloud storage's concept of a nearly infinite storage resource pool makes data storage and access scalable and readily available. The majority of them, on the other hand, favour a single cloud because of the simplicity and inexpensive storage costs it offers in the near run. Cloud-based data storage, on the other hand, has concerns such as vendor lock-in, privacy leakage and unavailability. With geographically dispersed cloud storage providers, multicloud storage can alleviate these dangers. One of the key challenges in this storage system is to arrange user data in a cost-effective and high-availability manner. A multicloud storage architecture is given in this study. Next, a multi-objective optimization problem is defined to minimise total costs and maximise data availability at the same time, which can be solved using a technique based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions known as the Pareto-optimal set.. When consumers can't pick from the Pareto-optimal set directly, a method based on Principal Component Analysis (PCA) is presented to find the best answer. To sum it all up, thorough tests based on a variety of real-world cloud storage scenarios have proven that the proposed method performs as expected.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.74-78
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• 2019

As software becomes larger and network technology develops, the management of distributed data becomes more popular. Therefore, it is becoming increasingly important to use blockchain technology that can guarantee the integrity of data in various fields by utilizing existing infrastructure. Blockchain is a distributed computing technology that ensures that servers participating in a network maintain and manage data according to specific agreement algorithms and rules to ensure integrity. As smart contracts are applied, not only passwords but also various services to be applied to the code. In order to reinforce existing research on smart contract applied to the blockchain, we proposed a dynamic conditional rule of smart contract that can formalize rules of smart contract by introducing ontology and SWRL and manage rules dynamically in various situations. In the previous research, there is a module that receives the upper rule in the blockchain network, and the rule layer is formed according to this module. However, for every transaction request, it is a lot of resources to check the top rule in a blockchain network, or to provide it to every blockchain network by a reputable organization every time the rule is updated. To solve this problem, we propose to separate the module responsible for the upper rule into an independent server. Since the module responsible for the above rules is separated into servers, the rules underlying the service may be transformed or attacked in the middleware. Therefore, the security mechanism using TLS and PKI is added as an agent in consideration of the security factor. In this way, the benefits of computing resource management and security can be achieved at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• 한국산업정보학회논문지
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• 제14권5호
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• pp.113-123
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• 2009

환경에 대한 문제가 대두됨에 따라, 에너지와 자원의 소비를 억제하고 탄소 배출을 줄이기 위한 노력들이 진행되고 있다. 그 중 다양한 형태의 문서들을 디지털로 변환하여 컴퓨터상에 파일로 보관하고, 이를 전자 문서 또는 전자 이미지 형태로 제공하여 에너지와 자원을 줄이기 위한 노력들이 진행되고 있다. 하지만, 디지털화 된 이미지 데이터들은 인터넷상에 표준화되지 못한 채 산재해 있어 이를 활용하기 위해서는 많은 시간과 노력이 필요하게 된다. 이에 따라 전력 소비 및 자원의 소비가 다시 증가하게 된다. 본 논문에서는 인터넷 상에 존재하는 디지털화된 이미지 데이터의 효율적인 관리와 제공을 통해 에너지와 자원의 소비를 줄이기 위한 시스템을 설계하고 구현한다. 웹상에 존재하는 대용량의 이미지 콘텐츠들을 보다 정확하게 분류하고 사용자가 원하는 이미지 콘텐츠를 정확하고 빠르게 제공해 줌으로써 이미지 데이터에 대한 에너지와 자원의 소비를 줄일 수 있다.

• 한국전자통신학회논문지
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• 제18권3호
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• pp.427-432
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• 2023

본 연구에서, MEC(: Multi-access Edge Computing)가 Wave, Lte, 5G 망에서 V2X(: Vehicle to Everything) 를 적용한 자율 자동차의 다양한 시험을 위해서 Cloud 서비스망 구성이 제안되고, MEC App(:Application)은 특정 지역에서 두 가지 도메인(사업자(KT, SKT, LG U+), 망 형태(Wave, LTE(3G 포함), 5G))의 V2X 서비스 기능 시험 검증을 적용하였다. 국내 운영업체(SKT, KT, LG U+ 그리고 Wave)의 4G 망에서, MEC는 독립적인 망 기능을 가져가기 위한 목적으로 V2X 기능 블록과 Traffic Offloading을 통한 개선 효과를 정리하였다. 그리고 5G 망의 V2X VNF에서 높은 수준의 QoS로 값으로, Traffic Steering기능의 시나리오가 목적지별 트래픽 경로상에서 입증되었다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.408-417
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• 2022

Although traditionally perceived as being a visualization and asset management resource, the relatively rapid rate of improvement of computing power, coupled with the proliferation of cloud and edge computing and the IoT has seen the expanded functionality of modern Digital Twins (DTs). These technologies, when applied to buildings, are now providing users with the ability to analyse and predict their energy consumption, implement building controls and identify faults quickly and efficiently, while preserving acceptable comfort and well-being levels. Furthermore, when these building DTs are linked together to form a community DT, entirely new and novel energy management techniques, such as demand side management, demand response, flexibility and local energy markets can be unlocked and analysed in detail, creating circularity in the economy and making ordinary building occupants active participants in the energy market. Through the EU Horizon 2020 funded TwinERGY project, three different levels of DT (consumer - building - community) are being created to support the creation of local energy markets while optimising building performance for real-time occupant preferences and requirements for their building and community. The aim of this research work is to demonstrate the development of this new, interrelated, multi-level DT that can be used as a decision-making tool, helping to determine optimal scenarios simultaneously at consumer, building and community level, while enhancing and successfully supporting the community's management plan implementation.