• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.3-11
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• 2023

According to the post evaluation scheme in Korea of a public construction project which is more than 30 Billion KRW, project performance is evaluated by investigating outcomes and effects of the construction after the completion of the project. The current post evaluation results can be used for planning and estimating a construction project in the future. However, it is not easy to utilized for an on-going project because the system does not provide the phase-based performance of a project. Although project planning performance is important for project initiation, few attempt has been made to evaluate planning performance in Korea. The purpose of this study is to provide a conceptual performance evaluation of planning performance using preliminary feasibility study conducted by Korea Development Institute. This study developed a planning performance database using data extracted from preliminary feasibility study reports of the completed 354 road construction projects. This study analyzed the performance of the planning stage of road projects by developing absolute metrics such as standard construction cost and standard construction schedule based on a Lane-Km. Using the standard construction cost and schedule metrics, the planning performance was analyzed by project characteristics. The results of this study can be used for phase-based performance evaluation from planning phase to construction phase.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.49-67
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• 2023

In recent years, the film industry in South Korea has experienced a period of prosperity, evidenced by the numerous awards won at major international film festivals. Furthermore, growing global interest in K-content and the expansion of the OTT industry following the COVID-19 pandemic are providing favorable conditions for the development of the domestic film industry. Sound effects play a crucial role in conveying the atmosphere and emotions of a film, making them an essential element of film production. In response, the Jeonju IT & CT Industry Promotion Agency has been promoting the development of Korean-style sound effects since 2013. Furthermore, the agency launched an online service called the "K-Sound Library," a sound effect archive, in 2021. However, the service has not been widely utilized because of issues with the database's construction and the system's problems. Therefore, this study aims to identify the K-Sound Library's problems through interviews with sound effects specialists about the online service of the first sound effect archive in South Korea. Based on the interviews and analyses of foreign cases, the study suggests ways to improve the search services' usability and the sound effects classification system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.551-560
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• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.381-389
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• 2023

One of the crucial components in building information modeling (BIM) is data. To systematically manage these data, various research studies have focused on the creation of object breakdown structures and property sets. Specifically, crucial data for managing programs and payments involves work breakdown structures (WBSs) and cost breakdown structures (CBSs), which are indispensable for mapping BIM objects. Achieving this requires disassembling CBS quantities based on 3D objects and WBS. However, this task is highly tedious owing to the large volume of CBS and divergent coding practices employed by different organizations. Manual processes, such as those based on Excel, become nearly impossible for such extensive tasks. In response to the challenge of computing quantities that are difficult to derive from BIM objects, this study presents methods for disassembling length-based quantities, incorporating significant portions of the bill of quantities (BOQs). The proposed approach recommends suitable CBS by leveraging the accumulated history of WBS-CBS mapping databases. Additionally, it establishes a unified CBS code, facilitating the effective operation of CBS databases.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.794-801
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• 2023

The high seas, covering the majority of the world's oceans, hold invaluable marine resources crucial for the growth of the marine bio-industry. The High seas bioresources program of South Korea is at the forefront of these global efforts. This study aims to gauge public awareness and quantify the project benefits, offering insights for future policy decisions. The results revealed that the estimated annual average willingness to pay (WTP) was 3,778.8 KRW, equating to approximately 81.54 billion KRW when extrapolated to the entire national population. The implications of the study are twofold: The project benefits, based on WTP, are substantial, amounting to approximately 81.5 billion KRW annually. This provides critical reference material for future policy formulation, given the considerable WTP in comparison to the current investment. Although interest in international sea marine biological resources is growing, public awareness remains relatively low. However, the project plays a crucial role in building essential databases for the marine bio-industry and securing international sea marine biological resources. Public interest and sustained support are pivotal, not only for this project but also for future policy implementation. Strategies to enhance public awareness are essential, and the study results offer valuable input for future policy decisions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1001-1006
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• 2023

Globally, rising energy costs and increased energy demand are important issues for the food processing and manufacturing industries, which consume significant amounts of energy throughout the supply chain. Accordingly, there is a need for the development of a real-time energy monitoring and analysis system that can optimize energy use. In this study, a food factory energy monitoring system was proposed based on IoT installed in a food factory, including monitoring of each facility, energy supply and usage monitoring for the heat treatment process, and search functions. The system is based on the IoT sensor of the food processing plant and consists of PLC, database server, OPC-UA server, UI server, API server, and CIMON's HMI. The proposed system builds big data for food factories and provides facility-specific monitoring through collection functions, as well as energy supply and usage monitoring and search service functions for the heat treatment process. This data collection-based energy monitoring system will serve as a guide for the development of a small and medium-sized factory energy monitoring and management system for energy savings. In the future, this system can be used to identify and analyze energy usage to create quantitative energy saving measures that optimize process work.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Information Systems Review
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• v.18 no.3
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• pp.155-183
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• 2016

Research on personalized recommender service that uses big data has gained considerable attention given the increasing volume of contents being created. This development indicates the need for service providers to collect personal information and content rating data to personalize content recommendations. Previous studies on this topic proposed algorithms to offer improved recommendations using minimal rating data or service designs and increase the number of ratings. However, limited studies have been conducted on the factors that motivate the ratings input of users, as well as the factors that influence their continuous usage of recommender service. The present study explored the factors that motivate users to enter ratings by conducting in-depth interviews with users who use recommender services. The meanings of these ratings were also explored. Results show that the meaning and usage range of ratings differed based on the stage of a user's with utilization of the service. When users input an initial rating, they treat such a rating as a database to save the impression of a past experience. Such a rating is then used as a tool to reflect the current feeling and thoughts of a user. In the end, users were not only interested in their own rating system, but they also actively sought out the meaning of the rating systems of others and utilized them. Users also expressed mistrust in the recommendations of the service because they were aware of the limitation of the algorithms. This study identified a number of practical implications regarding recommender services.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.