• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.1-16
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• 1995

Group collaborative systems are recently emerging to support a group of users engaged in common tasks such as group decision making, engineering design, group scheduling, or collaborative writing. This paper provides an change management framework for the group collaborative system to facilitate managing dependency relationship between shared objects and dependent user views, and coordinating change and propagation activities between the two in distributed computing environments. Specifically, the framework adopts an object-oriented database paradigm and presents several object constructs capturing dependency management and change notification mechanisms. First, it introduces change management mechanisms with transient shared objects and secondly, it extends them into presistent object computing environment by integrating transaction management mechanisms and change notification mechanisms. A prototype change management framework is developed on a commercial object-oriented database management system.

• Korean Management Science Review
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• v.10 no.1
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• pp.107-125
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• 1993

Modern telecommunication switching systems are SPC(Stored Program Control) machines handling voice, data and other kinds of traffic, in an environment which tends to be fully digital switching and transmission. The throughput of such systems is determined by the real time capacity of its centralized or distributed control processors and by the traffic capacity of the switching network. Designers must verify the traffic and call processing capacity of the switching system and check its performance under traffic load before it is put into service. Verification of traffic and call processing capacity of switching systems is one of the problems treated by teletraffic studies ; teletraffic studies are based on stochastic process, queueing theory, simulations and other quantitative methods of decision making. This study suggests the general methodology to evaluate the throughput and performance of the ISDN switching system. TDX-10 ISDN switching system are employed to give illustrative examples of the methodologies discussed in this study.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.2
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• pp.17-34
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• 1987

Modern telecommunication switching systems are SPC (Stored Program Control) machines handling voice, data and other kinds of traffic, in an environment which tends to be fully digital switching and transmission. The throughput of such systems is determined by the real time capacity of its centralized or distributed control processors and by the traffic capacity of the switching network. Designers must verify the traffic and call processing capacity of the switching system and check its performance under traffic load before it is put into service. Verification of traffic and call processing capacity of switching systems is one of the problems treated by teletraffic studies; teletraffic studies are based on stochastic process, queueing theory, simulations and other quantitative methods of decision making. This paper reviews the general methodologies to evaluate the throughput and performance of the digital switching system. TDX-10, which is a fully digital switching system under development in ETRI, is employed to give illustrative examples of the methodologies discussed in this paper.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.137-152
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• 1998

One of the components that constitute the simulation models is the state variables whose values are determined by the time related simulation process. Embedding rule-based expert systems into the simulation models should provide a systematic way of handling these time-dependent variables without distracting the essential problem solving capabilities of the expert systems which are well suited for expressing the decision making function of complex cases. The expert system, however, is inefficient in dealing with the time elapsing characteristics of target system compare to the simulation models. To solve the problem, this paper provides an interruptible inference engine whose inferencing process can be interrupted when the variables' value, which are used as the parameters of the rules, are not yet determined due to the time dependent nature of the state variables. The process is resumed when the variables are ready. The elapse of time is calculated by time-advance function of the simulation model to which the expert system has been embedded. The example modeling shown exploits the embedded interruptible inferencing capability for the controlling and monitoring of metal grating process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.503-508
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• 2005

In the modern, distributed and dynamic construction environment it is important to exchange information from different sources and in different data formats in order to improve the processes supported by these systems. Previous research has demonstrated that (i) a significant percentage of construction data is stored in semi-structured or unstructured data formats (ii) locating and identifying such data that are needed for the important decision making processes is a very hard and time-consuming task. In this paper, an automated methodology for the classification and retrieval of construction images in AEC/FM model based systems will be presented. Specifically, a combination of techniques from the areas of image processing, computer vision, and content-based image retrieval have been deployed to develop a method that can retrieve related construction site image data from components of a project model.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.578-582
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• 2015

Lack of client involvement in construction of public projects has been identified as the main cause of many operational problems. Clients need to perform their roles and responsibilities effectively and efficiently at the right time and through the use of correct methods in order to have the optimum involvement required during all the construction project phases, namely, the planning, design, construction, handover, and operation and maintenance phase. This paper investigates the influence of clients' individual factors on their involvement in construction project delivery. A questionnaire survey was distributed to various government agencies of the Kingdom of Saudi Arabia (KSA). Explanatory Factory Analysis was performed to group nineteen individual factors. The factor analysis result suggests that the individual factors that can involve clients' involvement in construction projects can be grouped into five, which are: adequacy of knowledge and experience for decision making in the early stage of design; ability to communicate clearly; job satisfaction; personal skills; and expertise in design and construction.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.422-432
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• 2018

This study is designed to find out the causal relation between social support, career capability, career decision-making self-efficacy and career adaptability for the career interrupted women. For this purposes the scales of social support, career capability, career decision related self efficacy and career adaptability were used. For this study, the questionnaires were distributed from Mar. 2, 2018 to Apr. 6, 2018 to 960 to the career interrupted women who had the experience of getting training at the vocational school, beauty school and women's centers located in Seoul and Gyeonggi area (composed of married women in their 30s and 50s who were receiving the education of beauty care for more than 1-9 months). 920 questionnaires got responded from the women and they were used for the final analysis with Cronbach's ${\alpha}$, exploratory factor analysis, factor analysis, frequency analysis and correlation analysis performed with SPSS program and the structural equation performed with AMOS program. The findings from the analysis are as follows; First, it was found that the structural model between career capability, social support, career decision related self efficacy and career adaptability are proper. Second, the path coefficient of the structural model was found to be statistically significant with respect to all of career capability, social support, career decision related self efficacy and career adaptability. Third, it was found that in the relation between career capability, social support and career adaptability, the career decision related self efficacy has the mediating effect. These results show that if the level of career adaptability is to be enhanced for the caber interrupted women, the career decision related self efficacy can make the critical role in addition to the career capability and social support. So, this study tries to offer the basic data required for the preparation of career and the development of future career for the success of career interrupted women going back to the workplace.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1504-1511
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• 2009

By development of renewable energies and high-efficient facilities and deregulated electricity market, the operation cost of distributed generation(DG) becomes more competitive. The amount of distributed resource is considerably increasing in the distribution network consequently. Also, international environmental regulations of the leaking carbon become effective to keep pace with the global efforts for low-carbon paradigm. It contributes to spread out the business of DG. Therefore, the operator of DG is able to supply electric power to customers who are connected directly to DG as well as loads that are connected to entire network. In this situation, community energy system(CES) having DGs is recently a new participant in the energy market. DG's purchase price from the market is different from the DG's sales price to the market due to the transmission service charges and etc. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize the profit. If there is no regulation for carbon emission(CE), the generators which get higher production than generation cost will hold a prominent position in a competitive price. However, considering the international environment regulation, CE newly will be an important element to decide the marginal cost of generators as well as the classified fuel unit cost and unit's efficiency. This paper will introduce the optimal operation of CES's DG connected to the distribution network considering CE. The purpose of optimization is to maximize the profit of CES and Particle Swarm Optimization (PSO) will be used to solve this problem. The optimal operation of DG represented in this paper is to be resource to CES and system operator for determining the decision making criteria.