• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.43-65
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• 2023

Recently, with the increasing international interest on environmental issues, efforts have been made to reduce greenhouse gas emissions due to ship fuel, however, the dependence on fossil fuel is expected to continue for a while. Since fuel costs account for a high portion of the total operating cost of a ship, it is necessary to analyze the influence of oil prices on the shipping markets. The purpose of this study is to evaluate the relationship between the international oil prices and the four major shipping markets for bulk carriers. This study employed WTI as the oil price variable while monthly data from 2017 to 2020 from the four major shipping markets by classifying freight rates, charter rates, newbuilding prices, and secondhand prices were also considered in multiple ship sizes of capesize, panamax, supramax, and handysize. Firstly, the results of the correlation analysis using the VAR model indicate that changes in international oil prices have a statistically positive (+) significant effect on BCIS only in the second time lag, on BSIS at all lags, and on BHIS only in the first staggered period. Secondly, as a result of correlation analysis using the VECM model, in the case of BPIC, BHIC, BCIN, and BHIR, the cointegration coefficient value has a negative (-) significant effect at the 5% significance level in the cointegration relationship with international oil prices. Further, in the case of the dynamic correlation, the increase in oil price in the first period of the lag leads to a decrease in the BCIN newbuilding prices while the increase in the oil price in the first and second period in the lag leads to a decrease in the BHIR used ship prices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.301-308
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• 2018

This study explores an analytical mathematical model designed to estimate the optimal debt ratio of the Korean automobile industry, which has a more significant effect on the national economy than that of other industries, and attempts to estimate the optimal debt ratio based on objective data. The analytical model is based on ROA and ROE which uses the debt ratio as an independent variable and employs ROS, TAT, and NFCL as the related parameters. Regarding the NFCL, the optimal debt ratio is usually defined as the debt ratio that maximizes the ROA and ROE and is calculated using analytical procedures, such as by adding an equation that considers the debt ratio and the linearity relationship to the analytical model. This is because the optimal debt ratio can be calculated reliably by making use of an estimated value within a certain range, which is derived from more than two calculations rather than a single estimation starting from one calculation formula. In this study, for the estimation of the optimal debt ratio, the ROA and ROE are expressed as a quadratic equation with the debt ratio as the independent variable. Using this analysis procedure, the optimal debt ratio obtained using the data from the Korean automobile industry over a sixteen year period, which would optimize the profitability of the Korean automobile industry, was found to be 188% of the debt ratio in the ROA and 213% of the debt ratio in the ROE. This result was obtained by overcoming the problem of the reliability of the estimation value in spite of the limitations of the logical theory of this study, and can be interpreted as meaning that maintaining a debt ratio of 188% to 213% can enhance the profitability and reduce the risks in the Korean automobile industry. Furthermore, this indicates that the existing debt ratio of the Korean automobile industry is lower than the optimal value within the estimated range. Consequently, it is necessary for corporations to change their future debt ratio policies, given that the purpose of debt ratio management is to maintain safety and increase profitability, and to take into account the characteristics of the specific industry.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.4
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• pp.488-498
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• 2007

The purposes of this study were to: a) provide evidences concerning the effects of emotional leadership b) examine the impacts of emotional leadership on employee-related variables, 'job satisfaction', 'organizational commitment', 'organizational performance' and 'turnover intention', and c) identify a conceptual framework underlying emotional leadership. A survey was conducted from August 23 to November 3, 2005 to collect data from mid-level managers in food service company headquarters (N=219). Statistical analyses were completed using SPSS Win (12.0) for descriptive, reliability, factor and correlation analyses and AMOS (5.0) for confirmatory factor analysis and structural equation modeling. The main results of this study were as follows. First, the managers gave the highest point to their leaders in the emotional leadership competence 'organizational awareness : reading the currents, decision networks, and politics at the organizational level' and gave the lowest point in the emotional leadership competence 'influence: wielding effective tactics for persuasion'. Second, the means of job satisfaction was above the midpoint (3 points). Employees' job satisfaction with 'coworkers' was relatively high. However, the extents of satisfaction with 'payroll' 'promotion', and 'work environment' were relatively low. Third, the organizational commitment was above the midpoint (3 points). In the organizational commitment, 'loyalty' factor was higher than 'commitment' factor. Fourth, the means of organizational performance was above the midpoint. The highest organizational performance variable was 'internal efficiency; trying to reduce cost' and the lowest organizational performance variable was 'internal fairness ; equitable treatment and all are treated with respect with no regard to status and grade'. Fifth, most respondents intended on 'thinking of quitting ; towards turnover process'. Sixth, the test of hypothesis using structural equation modeling found that emotional leadership produced p[Isitive effects on job attitude and job performance. Emotional leadership enhanced job satisfaction and organizational commitment, and in turn, employees' attitude positive effects on organizational performance; emotional leadership also had a direct impact on organizational performance

• Journal of Distribution Research
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• v.5 no.2
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• pp.49-68
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• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.1-36
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• 2020

This study is to reveal the acceptance factors of the Market Sentiment Index (MSI) created by reflecting the investor sentiment extracted by processing unstructured big data. The research model was established by exploring exogenous variables based on the rational behavior theory and applying the Technology Acceptance Model (TAM). The acceptance of MSI provided to investors in the stock market was found to be influenced by the exogenous variables presented in this study. The results of causal analysis are as follows. First, self-efficacy, investment opportunities, Innovativeness, and perceived cost significantly affect perceived ease of use. Second, Diversity of services and perceived benefits have a statistically significant impact on perceived usefulness. Third, Perceived ease of use and perceived usefulness have a statistically significant effect on attitude to use. Fourth, Attitude to use statistically significantly influences the intention to use, and the investment opportunities as an independent variable affects the intention to use. Fifth, the intention to use statistically significantly affects the final dependent variable, the intention to use continuously. The mediating effect between the independent and dependent variables of the research model is as follows. First, The indirect effect on the causal route from diversity of services to continuous use intention was 0.1491, which was statistically significant at the significance level of 1%. Second, The indirect effect on the causal route from perceived benefit to continuous use intention was 0.1281, which was statistically significant at the significance level of 1%. The results of the multi-group analysis are as follows. First, for groups with and without stock investment experience, multi-group analysis was not possible because the measurement uniformity between the two groups was not secured. Second, the analysis result of the difference in the effect of independent variables of male and female groups on the intention to use continuously, where measurement uniformity was secured between the two groups, In the causal route from usage attitude to usage intention, women are higher than men. And in the causal route from use intention to continuous use intention, males were very high and showed statistically significant difference at significance level 5%.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.81-93
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• 2022

Since 2008, China's shipping industry has been in a slump, with shipbuilding orders falling sharply, and high-growth excess capacity has become increasingly apparent, leaving many firms with sharply reduced orders at risk of bankruptcy and shutdown. To ensure the development of the shipbuilding industry and enhance the international competitiveness of the shipbuilding industry, it is necessary to analyze the present situation of the shipbuilding industry and the financial situation of the shipbuilding enterprises. And analyzing the problems faced by enterprises from the perspective of capital structure is very meaningful to the shipbuilders with high capital operation. We are trying to analyze the determinants of capital structure of China's shipbuilding listed companies. 30 listed Chinese shipbuilding and listed companies have been designated as sample companies that can obtain financial statements for 13 consecutive years. They also divided 30 sample companies into shipbuilding, shipbuilding-related manufacturing, and shipbuilding-related transportation. Dependent variable is the debt level of the year, independent variable includes the debt level of the previous year, fixed asset ratio, profitability ratio, depreciation cost ratio and asset size. The regression model of the panel used to analyze determinants is capital structure. The results of the empirical analysis are as follows. First, a fixed-effect model for the entire entity showed that the debt-to-equity ratio and the size of the asset in the previous period had a positive effect on the debt-to-equity ratio in the current period. Second, the impact of the profitability ratio on the debt level in the prior term also supports the capital procurement ranking theory rather than the static counter-conflict theory. Third, it was shown that the ratio of the depreciation of the prior term, which replaces the non-liability tax effect, affects the debt-to-equity ratio in the current period.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.397-432
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• 2015

This paper investigates the effects of technology collaboration between the main supplier and buyer on buyer's new product development under dynamic environment. Based on 428 Korean manufacturing firms, we conducted regression analysis. The technology collaboration between the main supplier and buyer is adopted as a independent variable and quality, cost and lead time performance of new product development projects are used as dependents variables. Environment dynamic is also used as a moderate variables. We found that the in general, technology collaboration is positively associated with the performance of buyers' new product development, but in the high degree of dynamic environment, technology collaboration is negatively associated with the performance of buyers' new product development unlike our expectation. Thus, we divide our sample into two groups; shipbuilding industry with the low degree of environment dynamic and electronic and IT device industry with the high degree of environment dynamic and conducted a post hoc analysis. As a result, in ship building industry, the technology collaboration is significant to improve NPD projects performance, while in electronic and IT device industry, the technology collaboration with a main supplier is not significant as well as coefficient is negative. In that, under the highly dynamic condition with the fast change of technology and products obsolescence the NPD collaboration with the main supplier does not works unlike a stable environment. This implies that the NPD attributes of buyer are different by their environmental factor and the fit between given environmental feature and the collaboration synergy is critical factor for improving the effect of NPD collaboration between supplier and buyer.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.571-580
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• 2011

The purpose of this study was to evaluate the effects of secondary task such as sending text message (STM) and searching navigation (SN) using the variable indicating control of vehicle ((Medial-Lateral Coefficient of Variation, MLCV), (Anterior-Posterior Coefficient of Variation, APCV)) and motion signal (Jerk-Cost function, JC). Participants included 50s taxi drivers; 14 males and 14 females. Participants were instructed to keep a certain distance (30m) from the car ahead with constant speed (80km/hr or 100km/hr). Experiement consisted of driving alone for 1minute and driving with secondary task for 1minute. Both MLCV and APCV were significantly increased during Driving + Sending Text Message(STM) and Driving + Searching Navigation(SN) than Driving only. Also, JC was increased during Driving + STM and Driving + SN than Driving only. In this study, we found that even in the experts group who are taxi driver and have 25 years driving experience, the smoothness of motion is decreased and the control of vehicle is disturbed when they were performing secondary tasks like sending text message or searching navigation.