• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.757-764
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• 2013

The estimation of maintenance cost of information system so far has been conducted centered on the ordering body, so the problem of provider's having to cover the cost due to small cost compared to the amount of work is not solved. This study is a base study for estimating the maintenance cost of information system centered on provider, and it deduces cost items of maintenance and suggests adjustment factors for adjusting the gap between the ordering body and provider regarding the maintenance cost. In order to deduce the cost items of maintenance, this study adds the activities of the provider for maintenance to the base study of cost factors regarding the existing maintenance activity, divides, and classifies them into the fixed cost and variable cost. In order to adjust the gap between the ordering body and provider regarding the maintenance cost, this study found the adjustment factors such as the code, utility, and components created by the automatic tool that was not included when estimating the maintenance cost centered on the ordering body. After examining and analyzing K Company's data of maintenance performance for three years, it confirmed that the gap regarding the adjustment factors was about 13% in case of K Company.

• Clean Technology
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• v.20 no.1
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• pp.1-6
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• 2014

$CO_2$ is generated by the combustion reaction, when getting the energy from fossil fuel. If the carbon dioxide emissions increases more, the global warming problem will become more serious. CCS (carbon capture storage) needs to be developed for the prevention of this. When liquefied $CO_2$ is transported, BOG (boil-off gas) is generated because of several problems. In the study, by injecting liquefied $CO_2$ in two tanks which contains $40m^3$each, the amount of BOG and its composition were measured during 30 days when generating pressure changes and external heat, loading, unloading. In result, 16,040 kg of BOG was generated and the composition has been found out to be 99.95% $CO_2$ and 0.05 % $N_2$. Also, we conducted simulation process for reliquefaction of generated BOG with vapor compression cycle using the PRO/II with PROVISION version 9.2. As a result, the refrigeration cycle of the total circulation flow rate was 42.07 kg/h and the condenser utility consumption was 48.85 kg/h.

• Cartoon and Animation Studies
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• s.15
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• pp.251-265
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• 2009

Motion Graphics are a great effective vehicle for precise communication between customers in various media and formats. The important thing in the expression of Motion Graphics is to deliver messages clearly. Some current Motion Graphics which are focused on only attracting attention or sensational expressions more than narrative are evaluated lower. This study aims to utilize easy and positive Motion Graphics to deliver messages by applying their utility to production of Motion Graphics, omitting time spent on delivering effective messages and analyzing their suggestive expression methods because of the nature of producing Motion Graphics. This thesis is to study several theoretical backgrounds of omission and implicated expressions mentioned in the similar studies from the view of Motion Graphics, and to search applied examples and functional things using the expression methods in some film title sequence. Excellent Motion Graphics use planned omission and implicated methods rather than to use entire narratives or complicated descriptions. Especially, a film title sequence should focus on symbolic visual expressions. They are necessary to attract the audience's interest. To overcome the limitation of time and space deliver a huge amount of information quickly and powerfully, Motion Graphics should properly use omission of image and time and suggestive expressions through symbols and metaphors. Then they will have a role to level up their current values and discussions.

• Journal of Distribution Science
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• v.5 no.1
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• pp.57-73
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• 2007

There are more problems in distributing agricultural and special products than industrial products because they have such restrictions as seasonal fluctuation in the quantity, indeterminate and diverse shapes, many farm households, a small amount of shipment, and mandatory guide and maintenance. However, with social atmosphere called well-being, there are increasing concerns about inorganic agricultural products and direct trade with farmers. It is therefore urgent to stabilize the price of agricultural and special products and to correctly grasp and improve logistics activities for those dealing with agricultural and special products in order to supply fresh ones. This study aimed at examining logistics activities and the information technology level for agricultural and special products, determining how these factors affect logistics performance, and eventually suggesting a scheme to improve the logistics achievements for agricultural and special products. For this purpose, it selected information technology and logistics activity levels as independent variables and logistics performance as a dependent variable. The information technology level was measured for utility, holding level, and concern, respectively, and the logistics activity level was tested for the level of using the logistics information system in those companies concerned. From the above-mentioned findings, it is urgent to enhance distribution technology and its level immediately since distribution of agricultural and special products is poor in Korea and good distribution can contribute to a reduction of logistics costs or improvement of customer service.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.172-189
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• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Applied Biological Chemistry
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• v.21 no.2
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• pp.123-130
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• 1978

Asp. oryzae D and H strains were cultured in the wheat bran and Czapek-Dox liquid media containing garlic powder in order to investigate the effect of garlic on the enzyme production and growth of Asp. oryzae. The results obtained were as follows; 1. Wheat bran media containing 0.5 to 2% garlic powder yielded increased in acid protease activity of the Asp. oryzae D strain while the best activity appeared at 2 to 6% in alkaline protease and 0.5% in neutral protease. 2. The protease activities of Asp. oryzae H strain was similar to that of the control in wheat bran media containing 0.5 to 8% garlic powder, but the peak appeared at the garlic powder concentration of 30%. 3. Garlic powder increased the $\alpha$-and Glucoamylase activities of Asp. oryzae H strain. 4. Garlic powder inhibited the both Asp. oryzae strains from the cellulase production. 5. Czapek-Dex liquid media containing 0.5 to 6% garlic powder yielded increase in dry mycerial weight in comparison with the control, and the increament was much more in case of the Asp. oryzae H strain. 6. As the amount of the garlic powder added to the Czapek-Dox liquid media increased, the pH of the cultured media of Asp. oryzae D strains was on the decrease while the media of H strain increased. 7. As the amount of the garlic powder added increased, the acidity of the cultured Czapek-Dex media increased. 8. The growth rate of the two Asp. oryzae strains were remarkably inhibited and no growth appeared in wheat bran and Czapek-Dox media containing garlic powder more over 10%. 9. The utility rate of reducing sugar was the highest in the Czapek-Dox liquid media containing 1 to 2% garlic powder.

• Journal of the Korean Orthopaedic Association
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• v.53 no.6
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• pp.490-497
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• 2018

Purpose: To evaluate the utility of ceramic-on-polyethylene articular bearing surface when cementless total hip arthroplasty is performed in patients older than 65 years through an analysis of the minimum five-year follow-up results using the ceramic femoral head and cross-linked polyethylene liner. Materials and Methods: From March 2010 to September 2012, 51 patients (56 hips) who were older than 65 years were enrolled in this retrospective study. The mean age at surgery was $70.9{\pm}5.1years$ old. A clinical assessment was analyzed using the Harris hip score. For the radiographic assessment, the cup inclination and anteversion, stem alignment, and wear amount were measured. The postoperative complications were also determined. Results: The mean Harris hip score was improved from preoperative 48 points to postoperative 87 points (p<0.05). The mean cup inclination was $40.9^{\circ}{\pm}6.4^{\circ}$ and the mean cup anteversion was $20.3^{\circ}{\pm}8.1^{\circ}$. The mean cup anteversion of the elevated liner-used group (16 cases) was $14.3^{\circ}{\pm}7.9^{\circ}$ and the mean cup anteversion of the neutral liner used group (40 cases) was $22.4^{\circ}{\pm}9.1^{\circ}$ (p<0.05). The mean stem alignment angle was $0^{\circ}$ (range, varus $4^{\circ}$-valgus $4^{\circ}$). The mean linear wear amount was $0.458{\pm}0.041mm$ and the average annual linear wear rate was $0.079{\pm}0.032mm/yr$. Six cases (10.7%) of intraoperative periprosthetic femoral fractures were encountered. Conclusion: Based on these results, the use of a ceramic-on-polyethylene articular bearing surface in elderly patients with cementless total hip arthroplasty is beneficial. On the other hand, careful effort is needed to prevent intraoperative periprosthetic femoral fractures.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.