• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.545-547
• /
• 2004

This paper proposes the novel investment priority decision method for distribution facilities considering the potential failure rate and the influence of customer caused by faults in distribution networks. The Proposed method decides the investment priority of the facilities combining, by the fuzzy rules, the KEPCO's priority decision for investment and the priority decision considering SAIFI(System Average Interruption Frequency Index) and SAIDI(System Average Interruption Duration Index). To verify the performance of the proposed method, these works utilized the projects for weak facility reinforcement planned in KEPCO in the Busan region in 2003 and 2004. The evaluation results showed that the reliability of the KEPCO in the Busan region using the proposed method can be enhanced more than using the conventional KEPCO's method.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.4
• /
• pp.35-52
• /
• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.31 no.2
• /
• pp.81-88
• /
• 2023

SHM (Structural Health Monitoring) technique for monitoring aircraft structural health and damage, EHM (Engine Health Monitoring) for monitoring aircraft engine performance, and APM (Application Performance Management) is used for each function. APMS (Airplane Performance Monitoring System) is a program that comprehensively applies these techniques to identify the difference between the performance manual provided by the manufacturer and the actual fuel mileage of the aircraft and reflect it in the flight plan. The main purpose of using APMS is to understand the performance of each aircraft, to plan and execute flights in an optimal way, and consequently to reduce fuel consumption. First, it is to check the fuel efficiency trend of each aircraft, check the correlation between the maintenance work performed and the fuel mileage, find the cause of the fuel mileage increase/decrease, and take appropriate measures in response. Second, it is to find the cause of fuel mileage degradation in detail by checking the trends by engine performance and fuselage drag effect. Third, the APMS is to be used in making maintenance work decisions. Through APMS, aircraft with below average fuel mileage are identified, the cause of fuel mileage degradation is identified, and appropriate corrective actions are determined. Fourth, APMS data is used to analyze the economic analysis of equipment installation investment. The cost can be easily calculated as the equipment installation cost, but the benefit is fuel efficiency improvement, and the only way to check this is the manufacturer's theory. Therefore, verifying the effect after installation and verifying the economic analysis is to secure the appropriateness of the investment. Through this, proper investment in fuel efficiency improvement equipment will be made, and fuel efficiency will be improved.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.6
• /
• pp.52-57
• /
• 1999

Funtional metal prototypes are often required in numerous industrial applications. These components are typically needed in the early stage of a project to determine form, fit and function. Recent R/P(Rapid Prototyping) part are made of soft materials such as plastics, wax, paper, these master models cannot be employed durable test in real harsh working environment. Parts by direct metal rapid tooling method, such as laser sintering, by now are hard to get net shape, pores of the green parts of powder casting method must be infiltrated to get proper strength as tool, and new type of 3D direct tooling system combining fabrication welding arc and cutting process is reported by song etc. But a system which can build directly 3D parts of high performance functional material as metal part would need long period of system development, massive investment and other serious obstacles, such as patent. In this paper, through the rapid tooling process as silicon rubber molding using R/P master model, and fabricate wax pattern in that silicon rubber mold using vacuum casting method, then we tranlsated the wax patterns to numerous metal prototypes by new investment casting process combined conventional investment casting with rapid pototyping & rapid tooling process. with this wax-injection-mold-free investment casting, we developed new investment casting process of fabricating numerous functional metal prototypes from one master model, combined 3-D CAD, R/P and conventional investment casting and tried to expect net shape measuring total dimension shrinkage from R/P part to metal part.

• Proceedings of the Korea Technology Innovation Society Conference
• /
• 2017.11a
• /
• pp.129-129
• /
• 2017

In planning and evaluating government R&D programs, one of the first steps is to understand the government's current R&D investment portfolio - which fields or topics the government is now investing in in R&D. Analysis methods of an investment portfolio of government R&D tend traditionally to rely on keyword searches or ad-hoc two-dimensional classifications. The main drawback of these approaches is their limited ability to account for the characteristics of the whole government investment in R&D and the role of individual R&D program in it, which tends to depend on the relationship with other programs. This paper suggests a new method for mapping and analyzing government investment in R&D using a combination of methods from natural language processing (NLP) and network analysis. The NLP enables us to build a network of government R&D programs whose links are defined as similarity in R&D topics. Then methods from network analysis show the characteristics of government investment in R&D, including major investment fields, unexplored topics, and key R&D programs which play a role like a hub or a bridge in the network of R&D programs, which are difficult to be identified by conventional methods. These insights can be utilized in planning a new R&D program, in reviewing its proposal, or in evaluating the performance of R&D programs. The utilized (filtered) Korean text corpus consists of hundreds of R&D program descriptions in the budget requests for fiscal year 2017 submitted by government departments to the Korean Ministry of Strategy and Finance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.1
• /
• pp.105-111
• /
• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.249-258
• /
• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of Biomedical Engineering Research
• /
• v.44 no.4
• /
• pp.225-241
• /
• 2023

The study empirically analyzed the differences in industry distribution and innovation activity performance in the metropolitan and non-metropolitan areas of Korea's bio companies, which are highlighted as future growth engines. The main innovation activities of the bio industry, which are focused on science and technology and expressed with high uncertainty, were analyzed, centering on human resources, technology cooperation, and investment promotion. As a result of the analysis, the biomedical industry in the metropolitan area was found to have a high proportion, and bio foods, bio-based chemicals, and energy industries in the non-metropolitan area, respectively. Moreover, the innovation activity performances differed between the two regions. In particular, the notable characteristics included human resources, investment promotion, and technical cooperation with medical institutions in the metropolitan area with a high proportion of biomedical industries, and technology personnel exchange and cooperation with private research institutions in the non-metropolitan area, which has a high proportion of bio foods, bio-based chemicals, and energy industries. This study is significant in that it is the first study to compare and analyze the performance of innovative activities based on the distribution of industries in the bio-industry, focusing on human resources, technology cooperation, and investment promotion. In addition, after investigating the distribution status and competitiveness of the domestic bio-industry by region, it will analyze the status and characteristics of the domestic bio-industry and present policy implications to implement relevant promotion policy more efficiently.

• Journal of Digital Convergence
• /
• v.12 no.4
• /
• pp.115-131
• /
• 2014

This study analyzed the impact of the investment of venture capital firms(VCFs) on the technological advancement of SMEs, which could be represented as the numerical increasement of patents. The results of this study are as follows: the higher proportion of VCFs' shares or the higher intensity of R&D, the more positive impact has been shown in the technological advancement of SMEs. Also, the joint investment of VCFs or the leading investors' stock acquirement had a positive impact on the technological improvement of them. Meanwhile, the meaningful relationships of company-size and the technical manpower with technological development were not identified although they were marginally positive. Those could be interpreted that the VCFs' supervision and control, including their managerial and technical advice, over invested companies display effectiveness for SMEs. It could also be interpreted that investors concentrate their investment on the relatively stable companies or the companies which other investors already finished screening.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.34 no.9
• /
• pp.1546-1557
• /
• 2010

This study analyzes the status of foreign direct investment in Korean textiles and fashion firms and investigates the factors determining their performance. A total of 1,251 cases (including 1,116 manufacturers and 135 of distributors from the 2009/2010 Korean Overseas Business Directory published by KOTRA) were used. The results of this study are as follow: 1) In the case of manufacturers, China was the most heavily invested in country, and the Asian region that included China, Vietnam, Indonesia and Bangladesh consisted of 80% total investment. In cases of distributors, China was also the first ranking country and other countries, that included Vietnam, United States, and Japan are major ones. 2) In terms of the foreign entry mode, wholly-owned subsidiaries represented 90% of total cases. As the index of the degree of localization, the ratio of local employees was very high. 3) Different countries were utilized by year, type of business, and area of process. In manufacturers, Indonesia, China, and Vietnam were the most heavily utilized countries in the 1980s, 1990s, and 2000s, respectively. For distributors, China was the major market ill the 1980s and 1990s but Vietnam has emerged as the biggest market in the 2000s. In terms of area of process, China was for manufacturing fibers and fabrics, Vietnam was for most items, Indonesia was for assembly, knit, accessories, and Bangladesh was for embroidery and accessories. 4) The determining factors of the age of foreign business as the proxy index and performance of foreign business entry, were different by the type of business. For manufacturers, four factors including the dollar amount of investment, number of local employees, the mode of foreign direct investment, and entry to China were significant. On the other hand, only two factors including the dollar amount of investment and entry (other than China) were significant distributors.