• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.3-11
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• 2023

This research examines the need for a quantitative and objective method of calculating baseline productivity in the construction industry, which is known for its high volatility in performance and productivity. The existing literature's baseline productivity calculation methods rely heavily on subjective criteria, limiting their effectiveness. Additionally, data collection methods such as the "Five-minute Rating" are costly and time-consuming, making it challenging to collect detailed data at construction sites. To address these issues, this study proposes an objective baseline calculation method using unimpacted productivity BP, a work check sheet to systematically record detailed data, and a data collection and utilization process that minimizes cost and time requirements. This paper also suggests using unimpacted productivity BP and comparative analysis to address the objectivity and reliability issues of existing baseline productivity calculation methods.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.157-162
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• 2022

In a society full of knowledge and information, digital literacy and artificial intelligence (AI) education that can utilize AI technology is needed to solve numerous everyday problems based on computational thinking. In this study, data-centered AI education was conducted while teaching computer programming to non-computer programming students at universities, and the correlation between major factors related to academic performance was analyzed in addition to student satisfaction surveys. The results indicated that there was a strong correlation between grades and problem-solving ability-based tasks, and learning satisfaction. Multiple regression analysis also showed a significant effect on grades (F=225.859, p<0.001), and student satisfaction was high. The non-computer programming students were also able to understand the importance of data and the concept of AI models, focusing on specific examples of project types, and confirmed that they could use AI smoothly in their fields of interest. If further cases of AI education are explored and students' AI education is activated, it will be possible to suggest its direction that can collaborate with experts through interest in AI technology.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.14-23
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• 2017

In high-density high-rise cities such as Hong Kong, buildings account for nearly 90% of energy consumption and 61% of carbon emissions. Therefore, it is important to study the design of buildings, especially high-rise buildings, to achieve lower carbon emissions in the city. The carbon emissions of a building consist of embodied carbon from the production of construction materials and operational carbon from energy consumption during daily operation (e.g., air-conditioning and lighting). An integrated analysis of both types of carbon emissions can strengthen the design of low carbon buildings, but most of the previous studies concentrated mainly on either embodied or operational carbon. Therefore, the primary objective of this study is to develop a holistic methodology framework considering both embodied and operational carbon, in order to enhance the sustainable design of low carbon high-rise buildings. The framework will be based on the building information modeling (BIM) technology because BIM can be integrated with simulation systems and digital models of different disciplines, thereby enabling a holistic design and assessment of low carbon buildings. Structural analysis program is first coupled with BIM to validate the structural performance of a building design. The amounts of construction materials and embodied carbon are then quantified by a BIM-based program using the Dynamo programming interface. Operational carbon is quantified by energy simulation software based on the green building extensible Markup Language (gbXML) file from BIM. Computational fluid dynamics (CFD) will be applied to analyze the ambient wind effect on indoor temperature and operational carbon. The BIM-based framework serves as a decision support tool to compare and explore more environmentally-sustainable design options to help reduce the carbon emissions in buildings.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.39-52
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• 2023

In this study, project information of government-funded research institute in the food field was collected and analyzed to systematically identify the factors affecting the process of transferring technological achievements of public research institute to the private sector. This study hypothesized that human resources, financial resources, and technological characteristics as input factors of R&D projects affect output factors, such as research papers or patents produced by R&D projects. Moreover, these outputs would serve as drivers of the technology transfer as one of the R&D outcomes. Linear Regression Analysis and Poisson Regression Analysis were conducted to empirically and sequentially investigate the relationship between input factors and output and outcome of R&D projects and the results are as follows: First, the principle investigator's career and participating researcher's size as human resource factors have an influence on both the number of SCI (science citation index) papers and patent registration. Second, the research duration and research expenses for the current year have an influence on the number of SCI papers and patent registrations, which are the main outputs of R&D projects. Third, the technology life cycle affects the number of SCI papers and patent registrations. Lastly, the higher the number of SCI papers and patent registrations, the more it affected the number of technology transfers and the amount of technology transfer contract.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.213-220
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• 2011

In the current construction industry where various stakeholders take part, BIM Data exchange using standard format can provide a more efficient working environment for related staffs during the life-cycle of the building. Currently, the formats used to exchange the data from 3D-CAD application to structure energy analysis at the design stages are IFC, the international standard format provided by IAI, and gbXML, developed by Autodesk. However, because of insufficient data compatibility, the BIM data produced in the 3D-CAD application cannot be directly used in the energy analysis, thus there needs to be additional data entry. The reasons for this are as follows: First, an IFC file cannot contain all the data required for energy simulation. Second, architects sometimes write material names on the drawings that are not matching to those in the standard material library used in energy analysis tools. DOE-2.2 and Energy Plus are the most popular energy analysis engines. And both engines have their own material libraries. However, our investigation revealed that the two libraries are not compatible. First, the types and unit of properties were different. Second, material names used in the library and the codes of the materials were different. Furthermore, there is no material library in Korean language. Thus, by comparing the basic library of DOE-2, the most commonly used energy analysis engine worldwide, and EnergyPlus regarding construction materials; this study will analyze the material data required for energy analysis and propose a way to effectively enter these using semantic web's ontology. This study is meaningful as it enhances the objective credibility of the analysis result when analyzing the energy, and as a conceptual study on the usage of ontology in the construction industry.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.973-979
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• 2024

In the framework of the Generation IV research and development project, in which the French Commission of Alternative and Atomic Energies (CEA) is involved, a main objective for the design of Sodium-cooled Fast Reactor (SFR) is to meet the safety goals for severe accidents. Among the severe ones, the Unprotected Transient OverPower (UTOP) accidents can lead very quickly to a global melting of the core. UTOP accidents can be considered either as slow during a Control Rod Withdrawal (CRW) or as fast. The paper focuses on fast UTOP accidents, which occur in a few milliseconds, and three different scenarios are considered: rupture of the core support plate, uncontrolled passage of a gas bubble inside the core and core mechanical distortion such as a core flowering/compaction during an earthquake. Several levels and rates of reactivity insertions are also considered and the thermal-mechanical behavior of an ASTRID fuel pin from the ASTRID CFV core is simulated with the GERMINAL code. Two types of fuel pins are simulated, inner and outer core pins, and three different burn-up are considered. Moreover, the feedback from the CABRI programs on these type of transients is used in order to evaluate the failure mechanism in terms of kinetics of energy injection and fuel melting. The CABRI experiments complete the analysis made with GERMINAL calculations and have shown that three dominant mechanisms can be considered as responsible for pin failure or onset of pin degradation during ULOF/UTOP accident: molten cavity pressure loading, fuel-cladding mechanical interaction (FCMI) and fuel break-up. The study is one of the first step in fast UTOP accidents modelling with GERMINAL and it has shown that the code can already succeed in modelling these type of scenarios up to the sodium boiling point. The modeling of the radial propagation of the melting front, validated by comparison with CABRI tests, is already very efficient.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.83-90
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• 2024

In this paper, we propose a solution to apply a proximity voice dialog system to voice dialog technology, one of the interaction systems in multimedia environments. A voice dialog between multiple users in a multimedia space is designed by adjusting the volume of the voice according to the distance between the user avatars and muting the user who is beyond the audible distance. The main feature of this research is a reliable UDP-based active server system that delivers low-quality voice data to users who are far away based on distance and does not transmit voice data to users who enter the inaudible area for economic development. The performance of the proposed system was measured in a previously completed project based on the Unity game engine, and it is expected that the system proposed in this research will be actively used in environments that provide interaction between multiple users such as met averse content and real-time battle action games.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.143-151
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• 2024

This study proposes a guide to activate regional resource-based start-up projects by analyzing program planning, consultation, promotion, recruitment, operation, support, and performance measurement. It provides insights into effective methods for engaging prospective entrepreneurs interested in local brands.The study categorized 100 local start-up communities on platforms like YouTube and Instagram, identified suitable message characteristics for each channel, and measured conversion rates after distributing seven types of messages. Over four weeks, the messages received over 57,000 views, achieved a 13% conversion rate, and attracted about 100 applicants. This evaluation identified the most effective message types, offering key policy implications and insights into early-stage entrepreneurs' awareness and the entrepreneurial ecosystem. The researchers also revealed that cooperation with local communities and regional innovation centers is an important success of a local startup.