• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.315-323
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• 2016

This study examined the durability and method for making a mockup of bobsled tracks for constructing a bobsled stadium, which is a sport in the Winter Olympics. As bobsleigh games are very fast and dangerous, a safety design for players and a precise construction using highly efficient shotcrete is necessary. Moreover, a general molding construction is difficult because bobsleigh tracks are composed of various curves and slopes, and it is necessary to construct them using high-strength and high durability materials. The developed method for making a mockup and performing durability evaluation of bobsleigh tracks through this research will be applied in the construction of the 2018 Pyeongchang Winter Olympics Sliding centre and bobsleigh tracks using domestic techniques.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.35-46
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• 2006

In this study, we proposed a layout of the integrated decision support system in order to prevent the contamination and to manage risk in water supply networks for safe and smooth water supply. We evaluated the priority of risk factors to detect anomaly in water supply networks using PROMETHEE and ANP techniques, which are applied to various Multi-Criteria Decision Making area in Europe and America. To develop the model, we selected pH, residual chlorine concentration, discharge, hydraulic pressure, electrical conductivity, turbidity, block leakage and water temperature as the key data item. We also chose pipe corrosion, pipe burst and water pollution in pipe as the criteria and then we present the results of PROMETHEE and ANP analysis. The evaluation results of the priority of risk factors in water supply networks will provide basic data to establish a contingency plan for accidents so that we can establish the specific emergency response procedures.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1055-1065
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• 2009

High-dynamic-range (HDR) rendering technology changes the range from the broad dynamic range (up to 9 log units) of a luminance, in a real-world scene, to the 8-bit dynamic range which is the common output of a display's dynamic range. One of the techniques, iCAM06 has a superior capacity for making HDR images. iCAM06 is capable of making color appearance predictions of HDR images based on CIECAM02 and incorporating spatial process models in the human visual system (HVS) for contrast enhancement. However there are several problems in the iCAM06, including obscure user controllable factors to be decided. These factors have a serious effect on the output image but users get into difficulty in that they can't find an adequate solution on how to adjust. So a suggested model gives a quantitative formulation for user controllable factors of iCAM06 to find suitable values which corresponds with different viewing conditions, and improves subjective visuality of displayed images for varying illuminations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.276-287
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• 2021

Project Management Information System (PMIS) is a special purpose information system that is created to provide useful information for project managers and participants to make effective and efficient decision making during projects. The use of PMIS is increasing in project based industries such as construction, defense, manufacturing, software development, telecommunication, etc. It is generally known that PMIS helps to improve the quality of decision making in project management, and consequently improves the project management performance. However, it is unclear what are the difference of PMIS impacts between industries, and still need to be studied further. The purpose of this study is to compare the impact of PMIS on project management performance between industries. We assume that the effects of PMIS will be different depending on the industry types. Five hypotheses are established and tested by using statistical methods. Data were collected by using a survey questionnaire from those people who had experience of using PMIS in various project related industries such as construction, defense, manufacturing, software development and telecommunication. The survey questionnaire consists of 5 point scale items and were distributed through e-mails and google drive network. A total of 181 responses were collected, and 137 were used for analysis after excluding those responses with missing items. Statistical techniques such as factor analysis and multiple regression are used to analyze the data. Summarizing the results, it is found that the impacts of PMIS quality on the PM performance are different depending on the industry types where PMIS is used. System quality seems to be more important for improving the PM performance in construction industry while information quality seems more important for manufacturing industry. As for the ICT and R&D industries, PMIS seems to have relatively lesser impact compared to construction and manufacturing industries.

• Journal of Korea Trade
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• v.24 no.3
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• pp.20-36
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• 2020

Purpose - As the global product network expands through both internationalization and diversification of the multimodal transportation system, corporate strategies have shifted to emphasize the importance of a high value-added international logistics system. To guide policies and strategies to attract relevant industries, this study aims to analyze the location competitiveness of regional logistics distribution center to serve Northeast Asia. Design/methodology - Multi-criteria techniques are considered to offer a promising framework for evaluating decision-making factors. This paper employed an analytic hierarchy process to analyze the hierarchal structure of determinants for selecting the location of a regional logistics distribution center. Adopting both qualitative and quantitative evaluations, this study suggest political implications for a regional logistics distribution center development, such as the direction of political support, service differentiation and infrastructure development. Findings - This study developed a location competitiveness evaluation model, based on the case study of the major port-cities in Northeast Asia. Evaluation model incorporates five factors underpinning 17 components extracted using factor analysis. The results revealed that the logistics factor is the most significant factor for evaluating the competitiveness of a regional logistics distribution center. The remaining factors were market, costs, and services environment. Comparing qualitative and quantitative evaluations, results provide useful insights for a regional logistics distribution center development in Northeast Asia. Originality/value - This study revealed differences between qualitative and quantitative evaluations. The finding implies that prior works on evaluation models of competitiveness has not successfully measured the gap between quantitative data and expert' evaluations. To overcome this limitation, this paper considered both actual data such as actual distance, cost, the number of companies located, and expert opinions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.3-27
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• 2022

Cardiac CT has been proven to provide diagnostic and prognostic evaluation of coronary artery disease for cardiovascular risk stratification and treatment decision-making based on rapid technological development and various research evidence. Coronary CT angiography has emerged as a gateway test for coronary artery disease that can reduce invasive angiography due to its high negative predictive value, but the diagnostic specificity is relatively low. However, coronary CT angiography is likely to overcome its limitations through functional evaluation to identify the hemodynamic significance of coronary artery disease by analyzing myocardial perfusion and fractional flow reserve through cardiac CT. Recently, studies have been actively conducted to incorporate artificial intelligence to make this more objective and reproducible. In this review, functional imaging techniques of cardiac computerized tomography are explored.

• Journal of Distribution Science
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• v.22 no.4
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• pp.11-22
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• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.