• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.173-179
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• 2009

Industrial water supplied by water resource project is essential input materials along with labor, capital and land for companies. It is very important to stably secure these input materials in order for the industry to generate additional values. If the supply of industrial water is stopped, it is known damage for the industry is greater than domestic water or agriculture water based on same amount of supply. Like this, the actual value of industrial water has been highly acknowledged from the intuitive perspective, but study on the value and benefits of industrial water has been rarely conducted. Therefore, this study verified the value of industrial water supplied from water resource project, and used marginal production value as a measure to estimate the benefits of industrial water in the analysis of economic efficiency. As a result of empirical analysis using Cobb-Douglas production function and Translog production function, industries' average marginal production value was $5,427KRW/m^3$ and $5,583KRW/m^3$ respectively. The marginal production value for eleven industries were estimated by using same method. The marginal production value by industries presented by this study will be used as important data to calculate benefits of industrial water in the future. Moreover, the result of this study will provide reasonable criteria for decision making on the allocation of water in emergency situation, and problem of resource supply from water resource project.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.771-781
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• 2006

This Study is about the development of LCC Analysis Model and Evaluation of VE. It was carried out to help the person's intention decision about choosing the pavement construction method that can deal with 'Pavement Life Factor' like Area Character and Traffic Volume efficiently, by considering the total life cycle cost of pavement life cycle happens according to the numbers of public use year. For this, we developed the new LCC Analysis Model by using the Data of Seoul city the representative city in Korea, and carried out VE Evaluation that reflects the opinions of specialists. This Analysis Model consists of cost items that affects directly the choice of pavement construction, except for the common cost items of the various pavement construction. And we investigated the propriety by applying our model to the example line that are used for the public at present. About the base data of cost items that are used for our analysis, we enhanced our model's confidence by using the statistics data of Seoul and the standard data of unit cost calculation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.17-30
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• 2021

Environmental deterministic perspectives and resource-based perspectives have different perceptions on the factors that determine corporate performance. While the environmental deterministic viewpoint sees the external environment as having a significant impact on corporate performance. On the other hand, the resource-compliant viewpoint believes that it is important to obtain the necessary resources through appropriate decision-making in order to overcome the uncertainty of the environment. Although the external environmental impact on corporate performance is important, the study is in the position that efforts within the company to cope with environmental uncertainty are necessary. This study identified the role that factors within the company play in the process of affecting the external environment of the company's performance. This study looked at whether the CEO's innovation direction plays an mediating role in the market environment favorability affecting sales growth rate. The data was collected using a survey method. We collected data from 138 small and medium-sized manufacturing companies in Gyeongin area. The collected data was analyzed using SPSS 22 packages. According to the analysis, market environment favorability positively affects sales growth rate, and the CEO's innovation direction plays a mediating role between market environment favorability and sales growth rate. The results of this study showed that depending on the market environment, the CEO's interest and willingness to innovate, present a vision for innovation, and institutionalize innovation activities increase management performance through innovation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.39-50
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• 2022

Based on the signaling theory, this study examined whether startups are more likely to attract venture investment when receiving government R&D subsidies. First, we reviewed previous studies of the investment decision-making process of venture capitalists and understood the conditions that influence investment decisions. Based on previous studies on the signal effect of government subsidies, particularly government R&D grants, on inducing private fund investment, this study revealed a mechanism to induce venture investment by startups. In addition, in order to verify whether government R&D subsidies have the effect of inducing venture investment, an empirical analysis was conducted based on data from startups under seven years and certified as a venture companies in 2021. This paper used PSM(Propensity Score Matching) method and DID(Difference In Difference) analysis for an empirical study to analyze the average treatment effect on the treated group(beneficiary startups of government R&D grants). As a result of empirical analysis, companies that receive more government R&D subsidies after starting a business are more likely to attract venture investment. From two to three years after conducting the first government R&D project, startups that received government R&D grants attracted more venture investment than those that did not. The results of this paper demonstrate that government R&D projects can also affect the venture investment ecosystem, giving policy implications to government R&D projects targeting startups. It is also expected to suggest strategic implications to startups that need new funding.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Industry Promotion Research
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• v.9 no.1
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• pp.89-101
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• 2024

The purpose of this study is to examine the relationship between parental/parental attachment and career indecision among college students, and to examine the goodness of fit and influence of variables in a model that assumes that socially imposed perfectionism has an influence on the relationship between parental/parental attachment and career indecision. It's about verification. For this purpose, an online survey was conducted by randomly sampling 250 college students attending 4-year institutions across the country, and data analysis was conducted using a three-stage regression method using SPSS Win 25.0. The analysis results are as follows. First, psychological attachment to parents appears to have a negative effect on career indecision, confirming that the more a stable attachment relationship with parents is formed, the less difficulties in career decision-making. Second, the mediating effect of socially imposed perfectionism was confirmed in the relationship between psychological attachment to parents and career indecision. This shows that the more stable the psychological attachment to the father and mother is formed, the lower the level of socially imposed perfectionism and career indecision. Based on these research results, implications for career counseling practice and follow-up research were discussed.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.75-84
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• 2023

Many developing countries face challenges in estimating long-term discharge due to the lack of hydrological data for water supply planning, making it difficult to establish a rational water supply plan for decision-making on water distribution. The study area, the Bandung region in Indonesia, is experiencing rapid urbanization and population concentration, leading to a severe shortage of freshwater. The absence of water reservoir prediction methods has resulted in a water supply rate of approximately 20%. In this study, we aimed to propose an approach for predicting water reservoirs in developing countries by analyzing water safety and potential water supply using the MODSIM (Modified SIMYLD) network model. To assess the suitability of the MODSIM model, we applied the unit hydrograph method to calculate long-term discharge based on 19 years of discharge data (2002-2020) from the Pataruman observation station. The analysis confirmed alignment with the existing monthly optimal operation curve. The analysis of power plant capacity revealed a difference of approximately 0.30% to 0.50%, and the water intake safety at the Pataruman point showed 1.64% for Q95% flow and 0.47% for Q355 flow higher. Operational efficiency, compared to the existing reservoir optimal operation curve, was measured at around 1%, confirming the potential of using the MODSIM network model for water supply evaluation and the need for water supply facilities.