• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.6
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• pp.27-38
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• 2018

The purpose of this study is to find out common investment decision factors for CVC's invested technology-based startups and analyze them. We examined 17 CVCs that invested in technology startups for three years and six months from 2015 to June 2018. As a result, the final 9 CVCs that can be used in this study were confirmed and 188 companies were analyzed. This study was conducted as a case study to propose and demonstrate CVC investment objectives and investment decision factors analysis model. The results of this study are as follows. First, CVC focused on strengthening investment. Second, In 2015, Invested in an average of 19 months of technology-based startups. In recent years, we invested in 36 months of proven technology-based startups. Thirdly, ICT service was the main business type of the invested startups. Fourth, the investors were concentrated on the stage of Series A~B. It is observed that CVC investment determinants have a significant impact on product or service and parent company relations. In addition, it was found that factors such as innovation, business planning competency, enterprising, strategic competency, leadership, and opportunity recognition competency were influential factors for the startups of invested companies and it was found that these factors are important for CVC investment decision. Understanding of CVC investment determinants presented in this study is based on the establishment of the investment process of the investee, entrepreneurship and management education program. The results of this study can be applied to the selection of excellent startups, entrepreneurship education programs, mentoring, development of coaching guidelines, and establishment of investment process of other investment institutions when investing in CVC.

• Journal of Ecology and Environment
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• v.42 no.4
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• pp.272-277
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• 2018

Background: Studying the ecosystem carbon cycle requires analysis of interrelationships between soil respiration (Rs) and the environment to evaluate the balance. Various methods and instruments have been used to measure Rs. The closed chamber method, which is currently widely used to determine Rs, creates a closed space on the soil surface, measures $CO_2$ concentration in the inner space, and calculates Rs from the increase. Accordingly, the method is divided into automatic or manual chamber methods (ACM and MCM, respectively). However, errors of these methods and differences in instruments are unclear. Therefore, we evaluated the characteristics and difference of Rs values calculated using both methods with actual data. Results: Both methods determined seasonal variation patterns of Rs, reflecting overall changes in soil temperature (Ts). ACM clearly showed detailed changes in Rs, but MCM did not, because such small changes are unknown as Rs values are collected monthly. Additionally, Rs measured using MCM was higher than that using ACM and differed depending on measured plots, but showed similar tendencies with all measurement times and plots. Contrastingly, MCM Rs values in August for plot 4 were very high compared with ACM Rs values because of soil disturbances that easily occur during MCM measurements. Comparing Rs values calculated using monthly means with those calculated using MCM, the ACM calculated values for monthly averages were higher or lower than those of similar measurement times using the MCM. The difference between the ACM and MCM was attributed to greater or lesser differences. These Rs values estimated the carbon released into the atmosphere during measurement periods to be approximately 57% higher with MCM than with ACM, at 5.1 and $7.9C\;ton\;ha^{-1}$, respectively. Conclusion: ACM calculated average values based on various Rs values as high and low for measurement periods, but the MCM produced only specific values for measurement times as representative values. Therefore, MCM may exhibit large errors in selection differences during Rs measurements. Therefore, to reduce this error using MCM, the time and frequency of measurement should be set to obtain Rs under various environmental conditions. Contrastingly, the MCM measurement is obtained during $CO_2$ evaluation in the soil owing to soil disturbance caused by measuring equipment, so close attention should be paid to measurements. This is because the measurement process is disturbed by high $CO_2$ soil concentration, and even small soil disturbances could release high levels into the chamber, causing large Rs errors. Therefore, the MCM should be adequately mastered before using the device to measure Rs.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.179-190
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• 2019

To alleviate financing difficulties of small and medium sized enterprises (SMEs), the government and municipal governments are providing a variety of SME policy funding programs. This study introduced the policy funding program of Gangwon-do and quantitatively analyzed the financial performance of companies supported by the Gangwon-do SME policy fund in the year 2014. Specifically, we compared the financial ratios for three years, from 2013 to 2015, between funded firms and non-funded firms. In addition, we applied a regression analysis to see if the policy funding program contributed to profitability (the operating profit growth and return on equity), stability (the interest coverage ratio and debt-to-equity ratio), and growth (the asset growth and sales growth) of the funded firms. The empirical results show that the firms that received the policy funds did not show any improvement compared to non-funded firms in terms of profitability, stability, and growth. This suggests that Gangwon-do should improve the policy funding program, that currently provides only an interest amount of 2-4% of the corporate loan principal, without any strategic selection criteria for the target funded firms, and without any follow-up management system, after support.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.244-250
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• 2019

Due to the industrial development and population growth, it has recently been shown that there are many problems caused by the rinked treatment water in local goverments and sewage treatment plants. The rinked treatment water has a characteristic of low flow rate and high concentration unlike general sewage. These characteristics increase sewage treatment difficulty and sewage treatment fee of sewage treatment facilities. Among the many influencing factors that increase sewage treatment unit cost, 'linked treatment load/design inflow load (%)' was derived as the most correlated factor. Through the selection and modeling of sewage treatment plants, the excess scope of design discharge water quality was investigated under the conditions of temperature and the conditions of 'linked treatment load/design inflow load (%)' taking into account the effects of the four seasons. The study found that for TN, 'linked treatment load/design inflow load (%)' was 19.7%, 22.6%, 25.1%and 27.7%, respectively, under conditions of $5^{\circ}C$, $10^{\circ}C$, $20^{\circ}C$ and $25^{\circ}C$. In case of TP, 'rinked treatment load/design inflow load (%)' was 10.7%, 12.2%, 15.6% and 17.5% at $5^{\circ}C$, $10^{\circ}C$, $20^{\circ}C$, and $25^{\circ}C$, respectively, under conditions of $5^{\circ}C$, $10^{\circ}C$, $20^{\circ}C$ and $25^{\circ}C$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.565-572
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• 2019

As a public research institute, government-supported institutes have led the industrial development and the advancement of science and technology in Korea. Robert Levering has annually selected 'Fortune 100 GPTW(Great Place to Work)' by measuring the Trust Index which was originated by himself. The objectives of this study are to attempt newly to analyze the inter-relations among 5 elements of GPTW trust index such as credibility, respect, fairness, camaraderie, and pride perceived by the K institute's 262 employees; to verify that GPTW trust index is valid for the selection of the fortune 100 GPTW. Amart PLS 2.0 and SPSS 18 were utilized for the statistical analysis. The results of this study are as follows. Credibility has positive effect to pride; respect has positive effect to pride and camaraderie; fairness has positive effect to camaraderie; and camaraderie has positive to pride. Pride and camaraderie have positive effect to the perceived GPTW. Recent studies mainly focused on the relations between GPTW trust index as independent, mediating variables or parameters and organizational performances. It is meaningful that this study has firstly tried to analyze the inter-relations among 5 elements of the trust index for the employees of research institute. This study has implication that the in order to enhance the performance the institute has to manage GPTW trust index perceived by the employees.

• The Journal of Korean society of community based occupational therapy
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• v.8 no.3
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• pp.25-35
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• 2018

Objective : The purpose of this study was to identify changes in reading ability among children with Borderline Intelligence by applying an electronic media reading training protocol. Methods : A picture book reading training protocol was applied to 10 childrens with borderline intelligence using electronic media to improve reading skills. This protocol was performed for 10 session once a week. After the analysis of the content validity index about the protocol presented in this study, this prococol was applied to the subjects. To analyze the changes of the reading ability for the subjects, KNISE-BAAT type A and B reading test were used. Results : According to the tests taken before and after implementing, the Application of Picture Booking Training Protocol using Electronic Media there was a significant improvement in Reading ability (Understanding words, Completion sentence, Vocabulary selection, Vocabulary arrangement, Understanding short text). However, there was no significant difference in Oral Reading. Conclusion : Application of Picture Booking Training Protocol using Electronic Media may be used as a beneficial measure to improve the reading abilities of children with Borderline Intellectual.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.50-64
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• 2019

According to the Maritime Safety Act, there are all 34 routes including 5 traffic safety zones, 3 traffic separation schemes, 26 routes designated by regional maritime affairs departments in the Republic of Korea. In the SOLAS convention, the route information should be is effectively used for the safe navigation. However, the route information is complicatedly composed of the location of the route, the navigation rule by each route, the restriction of the navigation, and the anchorages. Moreover, the present method of providing information using the navigational chart and other publications is not effective for users to grasp the navigational information. Therefore, it was conducted to study the design of the S-100 based routing information data model developed by the International Hydrographic Organization to find ways to more effectively provide route information. To do this, the analysis of route requirement, selection of items, encoding test and users' review were carried out. Through expert user review, it was evaluated that the study on the design of the route information data model can be utilized as a good basic data for the route information integration service. Future research on the development of route information data models is expected to provide integrated route information services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• The Plant Pathology Journal
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• v.35 no.1
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• pp.11-18
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• 2019

Gaeumannomyces graminis var. tritici is a soil borne pathogenic fungus associated with wheat roots. The accurate quantification of gene expression during the process of infection might be helpful to understand the pathogenic molecular mechanism. However, this method requires suitable reference genes for transcript normalization. In this study, nine candidate reference genes were chosen, and the specificity of the primers were investigated by melting curves of PCR products. The expression stability of these nine candidates was determined with three programs-geNorm, Norm Finder, and Best Keeper. $TUB{\beta}$ was identified as the most stable reference gene. Furthermore, the exopolygalacturonase gene (ExoPG) was selected to verify the reliability of $TUB{\beta}$ expression. The expression profile of ExoPG assessed using $TUB{\beta}$ agreed with the results of digital gene expression analysis by RNA-Seq. This study is the first systematic exploration of the optimal reference genes in the infection process of Gaeumannomyces graminis var. tritici.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.870-874
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• 2018

In the worst maritime accidents, people should abandon ship, but ship structures are narrow and complex and operation takes place on rough seas, so escape is not easy. In particular, passengers on cruise ships are untrained and varied, making evacuation prospects worse. In such a case, the evacuation management of the crew plays a very important role. If a rescuer enters a ship at distress and conducts rescue activities, which zones represent the most effective entry should be examined. Generally, crew and rescuers take the shortest route, but if an accident occurs along the shortest route, it is necessary to select the second-best alternative. To solve this situation, this study aims to calculate evacuation routes using Q-Learning of Reinforcement Learning, which is a machine learning technique. Reinforcement learning is one of the most important functions of artificial intelligence and is currently used in many fields. Most evacuation analysis programs developed so far use the shortest path search method. For this reason, this study explored optimal paths using reinforcement learning. In the future, machine learning techniques will be applicable to various marine-related industries for such purposes as the selection of optimal routes for autonomous vessels and risk avoidance.