• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.491-502
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• 2022

Voice technology allows consumers to make purchases through smart devices, and the interest in voice-driven conversational commerce has significantly expanded. In this study, we explored the continuance use intention of voice commerce, and the adoption of a value-attitude-behavior model. An online survey was conducted on 360 individuals who used an artificial intelligence assistant device in a voice commerce environment. We used Amos 23.0 and SPSS 25.0 for descriptive, confirmatory, and structural equation modeling analyses. These results indicated that functional value was the highest influencing variable on satisfaction of voice commerce, while social, emotional, and epistemic values significantly influenced it as well. Additionally, satisfaction of voice commerce significantly influenced the continuance use intention of voice commerce. These findings could help us understand the characteristics of voice commerce users and the diversity value in voice commerce environment.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.1027-1037
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• 2021

This study aims to evaluate the performance of an electric multi-purpose cultivator through a simulation analysis. The simulation model was developed using commercial software, Simulation X, by applying the specifications of certain parts, such as an electric motor, a battery, and so on. The input parameter of the simulation was the engine load data according to the rotary tillage level using a conventional multi-purpose cultivator. The data were collected by configuring a load measurement system, and the load cycle was developed by repeating the data collection process under the most severe conditions. The average output engine torque values of conventional multi-purpose cultivator were 10.7, 13.0, 9.4, and 11.2 Nm in the D1P1, D1P2, D2P1, and D2P2 conditions, respectively. As a result of the simulation, the maximum values of the motor torque, rotational speed, and power of the electric multi-purpose cultivator were 16.8 Nm, 2,033.3 rpm, and 3.3 kW, respectively, and the motor was driven in sections within 70, 68, and 45% of the maximum output range. The rate of decrease of the battery state of charge (SOC) level per minute was approximately 0.6%, and it was possible to supply electric power to the motor for 9,550 sec. In the future study, research to verify and improve simulation models of electric multi-purpose cultivators should be conducted.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.603-617
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• 2023

This study aims to provide a better understanding of the turbulent flow characteristics in swash zone. A double dam-break method is employed to generate the swash zone flow. Comparing with the conventional single dam-break method, a delay between two gate opening can be controlled to reproduce various interactions between uprush and backwash. For numerical simulations, overInterDyMFoam based on OpenFOAM is adopted. Using overInterDyMFoam, interface between two immiscible fluids having different densities (i.e., air and water phases) can be tracked in a moving mesh with multiple layers. Two-dimensional Reynolds-Averaged Navier-Stokes equations are solved with a standard 𝜅-𝜖 turbulence model for momentum and continuity. Numerical model results are validated with laboratory experiment data for the time series of water depth and streamwise velocity. Turbulent kinetic energy distribution is further investigated to identify the turbulence evolution for each flow regime (i.e., uprush, backwash, and swash-swash interaction).

• Journal of Engineering Education Research
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• v.27 no.2
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• pp.35-50
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• 2024

The purpose of this study is to identify the core competencies of graduate students at A research university in the context of graduate education in science and engineering, and to develop and validate a diagnostic tool to measure them. To achieve the research objectives, first, 6 factors and 18 sub-competencies of core competencies were derived based on a review of domestic and foreign studies, cases of excellent research-centered overseas universities, and interviews with members of A University. Second, a theoretical model was constructed by deriving behavioral indicators based on the core competencies and sub-competencies, and a preliminary survey was conducted on 188 graduate students of University A to verify the statistical validity of the theoretical model. Results of exploratory and confirmatory factor analysis, the core competencies of graduate students at A research university consisted of 6 factors, 16 sub-competencies, and 77 items. Specifically, it included "Independent research capability(13 items)", "Social Entrepreneurship(10 items)", "Academic agility(15 items)", "Ingenious Challenges(15 items)", "Collegial Collaboration(9 items)", and "Mueunjae leadership(15 items)". This study contributes to the development of theories related to core competencies of graduate students in science and engineering, and has practical significance as a basis for a data-driven competency-based graduate education system.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1310-1319
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• 2024

The reactor cavity cooling system (RCCS) is a passive reactor safety system commonly present in the designs of High-Temperature Gas-cooled Reactors (HTGR) that removes heat from the reactor pressure vessel by means of natural convection and radiation. It is one of the factors responsible for ensuring that the reactor does not melt down under any plausible accident scenario. For the simulation of accident scenarios, which are transient phenomena unfolding over a span of up to several days, intermediate fidelity methods and system codes must be employed to limit the models' execution time. These models can quantify radiation heat transfer well, but heat transfer caused by natural convection must be quantified with the use of correlations for the heat transfer coefficient. It is difficult to obtain reliable correlations for HTGR RCCS heat transfer coefficients experimentally due to such a system's size. They could, however, be obtained from high-fidelity steady-state simulations of RCCSs. The Rayleigh number in RCCSs is too high for using a Direct Numerical Simulation (DNS) technique; thus, a Reynolds-Averaged Navier-Stokes (RANS) approach must be employed. There are many RANS models, each performing best under different geometry and fluid flow conditions. To find the most suitable one for simulating an RCCS, the RANS models need to be validated. This work benchmarks various RANS models against three experiments performed on the HTTR RCCS Mockup by the Japanese Atomic Energy Agency (JAEA) in 1993. This facility is a 1/6 scale model of a vessel cooling system (VCS) for the High Temperature Engineering Test Reactor (HTTR), which is operated by JAEA. Multiple RANS models were evaluated on a simplified 2d-axisymmetric geometry. They were found to reproduce the experimental temperature profiles with errors of up to 22% for the lowest temperature benchmark and 15% for the higher temperature benchmarks. The results highlight that the pragmatic turbulence models need to be validated for high Rayleigh natural convection-driven flows and improved accordingly, more publicly available experimental data of RCCS resembling experiments is needed and indicate that a 2d-axisymmetric geometry approximation is likely insufficient to capture all the relevant phenomena in RCCS simulations.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.49-65
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• 2023

Purpose - In the Korean and Chinese social landscape, it is vital to appreciate the significance of the Japanese history problem. The current study investigated whether the perception of the Japanese history problem affects decisions regarding technology adoption in organizations by comparing South Korea and China. Design/methodology/approach - The study involved 305 Korean and 379 Chinese participants who responded to scenarios and surveys regarding the adoption of workplace surveillance cameras supplied by a Japanese company. Findings - Using a moderated mediation model based on protection motivation theory (PMT), we found that past experiences of privacy invasion significantly reduced trust in the adoption of surveillance cameras at work. This relationship was mediated by respondents' perceptions of security vulnerability. The current study, however, did not confirm any significant moderating effect of the Japanese history problem priming on trust in the adoption of workplace surveillance cameras. Research implications - This suggests that the Japanese history problem may have a limited impact on organizational technology adoption decisions, different from the political consumerism behavior driven by public anti-Japanese affectivity. The current study reaffirms the validity and applicability of PMT and provides both theoretical insights and practical recommendations.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1238-1245
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• 2024

The construction industry faces the challenge of providing effective, engaging, and rule-specific safety learning. Traditional methodologies exhibit limited adaptability to technological advancement and struggle to deliver optimal learning experiences. Recently, there has been widespread adoption of information retrieval and ontology-based chatbots, as well as content delivery methods, for safety learning and education. However, existing information and content retrieval methods often struggle with accessing and presenting relevant safety learning materials efficiently. Additionally, the rigid and complex structures of ontology-based approaches pose obstacles in accommodating dynamic content and scaling for large datasets. They require more computational resources for ontology management. To address these limitations, this paper introduces iSafe Chatbot, a novel framework for construction safety learning. Leveraging Natural Language Processing (NLP) and Large Language Model (LLM), iSafe Chatbot aids safety learning by dynamically retrieving and interpreting relevant Occupational Safety and Health Administration (OSHA) rules from the comprehensive safety regulation database. When a user submits a query, iSafe Chatbot identifies relevant regulations and employs LLM techniques to provide clear explanations with practical examples. Furthermore, based on the user's query and context, iSafe Chatbot recommends training video content from video database, enhancing comprehension and engagement. Through advanced NLP, LLM, and video content delivery, iSafe Chatbot promises to revolutionize safety learning in construction, providing an effective, engaging, and rule-specific experience. Preliminary tests have demonstrated the potential of the iSafe Chatbot. This framework addresses challenges in accessing safety materials and aims to enhance knowledge and adherence to safety protocols within the industry.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.89-100
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• 2016

This article analyzes two different strategies that both aim at creating innovative design or problem solving: design thinking and action learning. User-driven innovation strategy that has become more and more popular during the last decades is "design thinking". Based on designerly methods and principles, this strategy was developed by the design consultancy IDEO in the late 90s. Action learning is a pragmatic and moral philosophy based on a deeply humanistic view of human potential that commits us, via experiential learning, to address the intractable problems of organizations and societies. This paper provides a structured analysis and comparison of the two innovation strategies-design thinking and action learning-with the goal to identify potentials to enrich either of the two by merging or adapting specific parts or aspects. Although there are significant differences in both strategies, there are also several similarities in methodology and process design. This article compares process models for action learning and design thinking and highlights the specific differences and similarities. As a result, we suggested a union model of action learning and design thinking, and verified a this model through a case study. We complemented the process of team building and reflection of action learning for union model. Also, we statistically verified through a case study to validate the superiority of the design thinking model which complemented action learning. This article contributes to a better understanding of both-design thinking and action learning, and it may help to improve either of the two strategies to foster social innovation.