• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.622-629
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• 2014

This study aims to develop a standard diagnostic table for mountain ginseng so that the cultivators not only can check their current level of management with the table, but also can understand and address operational challenges better by themselves utilizing the table. The standard diagnostic table consists of 3 categories and 18 subcategories to diagnose the general status of forestry households, the indicators of management performance and the level of management. To develop the table, the study conducted a survey on the actual condition of management, targeting 81 forestry households throughout 15 municipalities including Mu-ju, Jeollabukdo, and Ham-yang, Gyeongsangnamdo, all of which are the chief producing districts of mountain ginseng. Then, the study calculated total scores by regions by aggregating the scores of 18 subcategories, in order to evaluate and compare the management level among regions based on the scores. According to the result, the average score of 81 forestry households was 57.2 point-58% of which surveyed belonged to the range of 40-60 point. Compared by regions, the average score of Jeollabukdo regions was 52.9 point, the lowest, and that of Gyeongsangnamdo regions was 61.4 point, the highest. It is remarkable that among the indicators of management base, the average score of 'mounding (the height of mound)' item was recorded rather low with 1.59 point, reflecting the fact that the cultivators tend to raise mountain ginseng with no additional mounds. As for the indicators of production skills, the average score of the pest control item was remarkably low with 1.28 point. Over 90% of cultivators answered that they do not usually forecast or survey the pest disease in advance. Meanwhile, it is also noticeable that the item of sowing and planting methods, and the item of seed were both rather high, recording 4.00 and 4.47 point respectively. As for the item of management and sales skill, however, the score was rather low with 2.20 point, meaning that the forestry households still have a low interest in the business management.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.249-258
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• 2016

In this research, we analyzed the research and development process of small and medium enterprises, and diagnosed the problem of the research and development process of domestic small and medium enterprises, and evaluated the influence of innovative ability on the speed of research and development and corporate performance. In evaluating these effects, it is possible to grasp the direction of power generation according to the type of analysis, taking into account the meltdown factor of factors related to innovative capabilities. The main purpose of this research is to confirm the influence on the speed of research and development according to the innovative capacity and business environment and to verify the reliability and validity of the research by Klumbach alpha Was used. In this research, we analyzed how the speed of R & D affects R & D activities, it is a research aimed at the necessity of a resource-based approach to the internal capacity of a company, Have a valuable value. Based on the influence on the company, each factor is a research that analyzed the influence on R & D and financial indicators through maintaining company's development level, Research that has practical value that can base on the development of R & D capacity on corporate strategy formulation.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2541-2549
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• 2013

The Construction industry has been dealing with much trouble due to global economic recession and domestic political trends emphasizing on welfare than development. Consequently, domestic construction market has been dramatically shrunk during the last a few years, and international market has become the only potential solution for the industry. However, there has been lack of efforts in developing a quantified measure of global competencies for Korean engineering and construction organizations. This study attempted to develop quantified indices for Korean engineering and construction contractors with which the level of global construction competencies can be objectively monitored. In doing so, a survey questionnaire was developed to identify relative importances of core competency elements which were derived from extensive literature reviews and experts interviews. AHP (Analytic Hierarchy Process) was employed as a main analysis method in developing quantification measures. The analysis results reveal little differences in competency requirements between engineering and construction firms and it implies that the global market becomes more integrated and requires a total solution for a construction project. The developed core competency measures can be used to quantify the level of preparedness of Korean engineering and construction firms at the time of evaluation and also be used as a basis for performance benchmarking indicators if they are compared with business showings.

• Journal of Korea Water Resources Association
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• v.53 no.spc1
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• pp.813-826
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• 2020

This study analyzes the effects of the Smart Water City (SWC) project that was introduced from 2014 to 2016 in Paju City, Gyeonggi Province, Korea, focusing on the achievement of the business goals. The SWC is referred to as a city that embraces a healthy water supply system based on Smart Water Management (SWM) that promotes the efficiency of water management by combining Information and Communication Technologies (ICTs) with water and sewerage facilities. In order to evaluate the effectiveness of the SWC project, this study deploys evaluation criteria corresponding to the project objectives, and analyzes the outputs before and after the project. The results show that the SWC has contributed to enhancing water supply services and the reliability and drinking rate of tap water. Specific improvement areas include the rise of average water flow rate and water leakage reduction, the diffusion of water quality monitoring system, and the reduction of floating particle concentration and turbidity in drainage pipes was achieved. These were possible because of specific implementation plans for clear goal setting and achievement and active services for citizens. The data related to water quantity and quality showed improved performance compared to before the introduction of SWMS, which is a positive effect. However, a quantitative analysis of the outputs has limitations in identifying other external factors that have led to the changes. In the future, guidelines for spreading SWC and more comprehensive and specific evaluation indicators for SWC should be prepared, and SWMS should be developed in consideration of the needs of users.

• Review of Culture and Economy
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• v.17 no.2
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• pp.3-28
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• 2014

The characteristics of performing arts differ from those of utilitarian goods in terms of economics. Factors other than price need to be considered to understand the demand for performing arts. Audience surveys as well as econometric demand studies have confirmed that socio-economic factors such as age, income, employment, and education are major determinants of the demand for performing arts. This study focused on the attributes of concerts rather than consumer characteristics to determine the concerts audiences select in terms of transaction cost. Genre, price, internet search trends, and the purpose of performance as well as price are tested as determinants of demand by using the data set for a major concert hall in Seoul. Genre and the specific purpose of concerts influence the demand for concerts. Internet search trends of the performer are used as indicators of popularity and information exposure, which are positively correlated with demand. This result supports the hypothesis that larger audiences would attend concerts that require lower information search costs. To note, price has a positive effect on demand in the higher price range, which means that concerts at higher prices attract larger audiences, whereas normal goods have a negative slope in the demand curve. This result can be explained by the hypothesis that consumers use price as an indicator of the quality expected of a concert. Transaction cost for selecting classical concerts thus forms an inverse-U shape curve against ticket price. These results provide some explanation of why audiences of classical music choose to attend concerts at high ticket prices while offering evidence in favor of the hypothesis that performing arts are selected in a social context.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.188-196
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• 2021

Korea has invested heavily in projects related to national rivers, but there is no evaluation technique and system to manage river projects that can evaluate the effectiveness of the river projects after they are completed. Their absence leads to the inability of information on river construction sections, analysis of project effects, and benchmarking between projects. This may cause over-budget, overlapping investment problems due to the implementation of similar projects in the same section, and incorrect business elements may be repeatedly utilized. In order to solve this shortcoming, this study developed river project evaluation techniques and a river project (construction) management system. The development of evaluation techniques enables comparison and analysis between projects and can be utilized in establishing maintenance plans. The system can also provide inquiry of construction information, visualization of construction, and management of performance items. In this study, the evaluation techniques developed through prior research were modified and supplemented, and the effectiveness was verified by applying them to national river projects in A river and B river. It is expected that the evaluation techniques and system utilization measures presented will increase the work efficiency of river projects and enhance the efficiency of river projects.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.37-50
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• 2023

Venture capital invests the necessary capital and supports management and technology in promising small and medium-sized venture companies in the early stages of start-up with promising technology and excellent manpower. It plays a role as a key player in the venture ecosystem that realizes profits by collecting the investment through various means after growth. Venture capital's job is to recruit various investors(LPs) to invest in small and medium-sized venture companies with growth potential through the formation of venture investment funds, and to collect investment as companies grow, distribute and reinvest. The main tasks of venture capitalists, which play the most important role in venture investment, are finding promising companies, corporate analysis and evaluation, investment screening, follow-up management, and investment recovery. Venture capital's success indicators are fund formation and return on investment, and venture capitalists are rewarded with annual salary, performance-based incentive, and promotion with work performance such as investment, exit, and fund formation. Compared to the recent rapidly growing venture investment market, investment manpower is insufficient, and venture capital is making great efforts to foster manpower and establish infrastructure and systems for long-term service, but research has been conducted mainly from a quantitative perspective. Accordingly, this study aims to empirically analyzed the impact of investment experience, delegation of authority, job fit, and peer relationships on fund formation and return on investment according to the characteristics of the venture capital industry. The results of these empirical studies suggested that future venture capital needs a job environment and manpower operation strategy so that venture capitalists with high job fit and investment experience can work for a long time.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.