• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.1059-1066
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• 2012

With the development of Smartphone, Smartphone contains diverse functions including many sensors that can describe users' state. So there has been increased studies rapidly about activity recognition and life pattern recognition with Smartphone sensors. This research suggest modeling of the activity data to classify extracted data in existing activity recognition study. Activity data is divided into two parts: Physical activity and Logical Activity. In this paper, activity data modeling is theoretical analysis. We classified the basic activity(walking, standing, sitting, lying) as physical activity and the other activities including object, target and place as logical activity. After that we suggested a method of visualizing modeling data for users. Our approach will contribute to generalize human's life by modeling activity data. Also it can contribute to visualize user's activity data for existing activity recognition study.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1085-1092
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• 2003

Proposed in this paper is a standard-compliant business process modeling tool which is based on the UML(Unified Modeling Language) activity diagram and produces an XPDL(XML Process Definition Language) file as an output. The XPDL is a standard process definition exchange format by WfMC(Workflow Management Coalition). To develop an UML/XPDL-based modeling tool, the mapping of modeling elements between activity diagram and XPDL format is conducted after the detailed analysis of each modeling specification. As a result of this mapping, it is revealed that modeling elements of each activity diagram and XPDL must be expanded. So new modeling elements are identified and added to each specification. Based on this mapping, the prototype system is developed, and the usefulness of the developed system is shown through the case study.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.361-368
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• 2022

This study seeks to determine the research trend of late adolescents by utilizing big data. Also, seek for research trends related to activity participation, treatment, and mediation to provide academic implications. For this process, gathered 1.000 academic papers and used TF-IDF analysis method, and the topic modeling based on co-occurrence word network analysis method LDA (Latent Dirichlet Allocation) to analyze. In conclusion this study conducted analysis of activity participation, treatment, and mediation of late adolescents by TF-IDF analysis method, co-occurrence word network analysis method, and topic modeling analysis based on LDA(Latent Dirichlet Allocation). The results were proposed through visualization, and carries significance as this study analyzed activity, treatment, mediation factors of late adolescents, and provides new analysis methods to figure out the basic materials of activity participation trends, treatment, and mediation of late adolescents.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.207-213
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• 1999

When function modeling with IDEF0 which is called as a standard methodology for CALS (FIPS 183), the initial work is to determine the purpose and viewpoint, and the next work is to generate a data list and an activity list. This work requires participant's discussion and agreements. The main problems for these working are a communication between participants and efficient discussion processing. Brainstorming is the most useful method for this work. The paper is for development for the modeling assistant tool to promote the business modeling. In this paper, we will introduce the tool which name is 'MindMapper' and 'Dyna-IDEF'. Mind-mapper is software for mind-map method that is used for creative thinking. Using this tool, we refine the result of the brainstorming about activity list and translate the result into the functional model of IDEF0 using Dyna-IDEF. The translated functional model is prototype model without ICOM arrows.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.645-654
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• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

• Proceedings of the KOR-KST Conference
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• 1994.10a
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• pp.26-60
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• 1994

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3543-3548
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• 2023

Shutdown chemistry evolution is performed in nuclear power plants at each refueling outage (RFO) to establish safe conditions to open system and minimize inventory of corrosion products in the reactor coolant system (RCS). After hydrogen peroxide is added to RCS during shutdown chemistry evolution, corrosion products are released and are removed by filters and ion exchange resins in the chemical volume control system (CVCS). Shutdown chemistry evolution including RCS clean-up time to remove released corrosion products impacts the critical path schedule during RFOs. The estimation of clean-up time prior to RFO can provide more reliable actions for RCS clean-up operations and transients to operators during shutdown chemistry. Electric Power Research Institute (EPRI) shutdown calculator (SDC) enables to provide clean-up time by Co-58 peak activity through operational data from nuclear power plants (NPPs). In this study, we have investigated the results of EPRI SDC by shutdown chemistry data of Co-58 activity using NPP data from previous cycles and modeled the estimated clean-up time by EPRI SDC using average Co-58 activity of the NPP. We selected two RFO data from the NPP to evaluate EPRI SDC results using the purification time to reach to 1.3 mCi/cc of Co-58 after hydrogen peroxide addition. Comparing two RFO data, the similar purification time between actual and computed data by EPRI SDC, 0.92 and 1.74 h respectively, was observed with the deviation of 3.7-7.2%. As the modeling the estimated clean-up time, we calculated average Co-58 peak concentration for normal cycles after cycle 10 and applied two-sigma (2σ, 95.4%) for predicted Co-58 peak concentration as upper and lower values compared to the average data. For the verification of modeling, shutdown chemistry data for RFO 17 was used. Predicted RCS clean-up time with lower and upper values was between 21.05 and 27.58 h, and clean-up time for RFO 17 was 24.75 h, within the predicted time band. Therefore, our calculated modeling band was validated. This approach can be identified that the advantage of the modeling for clean-up time with SDC is that the primary prediction of shutdown chemistry plans can be performed more reliably during shutdown chemistry. This research can contribute to improving the efficiency and safety of shutdown chemistry evolution in nuclear power plants.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.571-578
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• 2010

Recently, many social networking services using mobile devices are spread. Also, many studies based on location and time are increasing. However, existing studies have been difficult to resolve queries by place, time, and events. In the paper, we propose time and space modeling method for social services. We propose Human Activity Graph and Quad Relation Factors through time, place, event, and social activity of users, and we design the database scheme for data collect and analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.119-131
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• 1986

Described in this paper is a modelling methodology for mass production system simulation. The mass production system under consideration consists of various types of flow lines, special purpose production facilities, conveyor lines, palletized carts, and storage facilities. This type of production system is typical in home appliance industry, automobile industry, footwear industry, etc. where a variety of product mix are mass-produced. The modelling methodology is based on the "discrete-event formalism", and an "activity-oriented world view" is adopted to formalize the system description. A distinctive feature of the modelling methodology is that only the static structure (ie, system components) is included in the fixed model. The dynamic structure of the system is specified through a "data-driven" mechanism, which is an extension of the "experimental frame" concept. Each type of system components (ie, flow line, conveyors, carts, etc.) is formally modeled by using Activity Cycle Diagrams. The issue of "model structuring" is also addressed. The modeling methodology has been successfully applied in a real simulation study of a mass production system.