The third-party logistics industry mainly delivers goods from a place to an arrival place on behalf of the freight owner. To handle the work, they need a transportation route including transportation equipment between the starting place and the arrival place, schedule information for departure/arrival and transportation cost. Actually, automatic searching for an optimal transportation route, which considers arrival and departure points for intermodal transportation, is not a simple problem. To search efficiently transportation route, the collection of schedule information for intermodal transportation and transportation route generation have become critical and vital issues for logistics companies. Usually, they manually make a plan for a transportation route by their experience. Because of this, they are limited in their ability if there is too much cargo volume and a great many transactions. Furthermore, their dependence on the conventional way in doing business causes an inefficient selection of transporters or transportation routes. Also, it fails to provide diverse alternatives for transportation routes to the customers, and as a result, increases logistics costs. In an effort to solve these problems, this study aims to develop a route planning system based on agent, which can collect scattered schedule information on the Web. The route planning system also has an algorithm for transportation route generation in intermodal transportation.
The purposes of this study are to derive supportive measures for the effective operation of vocational education and training high schools participating in the apprenticeship system (apprenticeship schools) and make policy suggestions by analyzing the need analysis and investigating the importance and the difficulty of teachers regarding the operation of those schools. To achieve these purposes, the study developed a questionnaire by deriving the areas and items for the operation of apprenticeship schools, and analyzed 121 completed questionnaires of head and senior teachers who manage the apprenticeship programs across the nation after conducting a survey. The findings of this study can be summarized as follows: First, the teachers of apprenticeship schools found all the operation areas of the schools are important but difficult. Out of the operation areas, teachers had relatively high needs for 'promotion, selection and management of enterprises', 'student management', 'development of apprenticeship programs, formation of the curriculum, and establishment of operation plans of the curriculum'. Second, the teachers found all the detailed items of the operation areas of apprenticeship schools are important but difficult generally. According to the results, it is required to relax the criteria for forming apprenticeship organizations; operate the local government-oriented apprenticeship system; establish information systems between schools and enterprises; improve the support methods of relevant agencies; and increase incentives of teachers who are in charge of apprenticeship programs. It is also necessary to operate exclusive agencies supporting for OJT; operate apprenticeship education centers of local small and medium-sized business associations; provide exclusive supervision of students; cultivate teachers who support industry-academia cooperation; and legislate on the NCS-based qualifications.
Journal of the Korea Academia-Industrial cooperation Society
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v.19
no.10
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pp.490-497
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2018
This study is to identify factors influencing therapeutic diversification strategy for Korean pharmaceutical companies. So, it purposes to find deciding factors for companies to adopt strategy for diversification or specialization. In this paper, we set six factors that are expected to influence diversification. The factors are asset scale, sales, profitability, and growth potential. Also, we included number of employees and SG&A cost ratio as factors that reflect business characteristics. We analyzed the impacts of each factor on diversification and found a relationship amongst each other. In this paper, regression analysis is applied with forward selection method, considering that it takes 5 years for companies to decide on diversification strategy. As a result, asset scale, the number of employees and profitability have significantly influenced decisions of diversification at t-1 time, and growth potential was significantly influenced at t-2 time. Korean pharmaceutical companies generally tend to go with specialization model if they have larger asset scale, higher profitability and growth potential. However, they have tendency to decide on diversification strategy, if they have large number of employees. Overall, the result of this study is expected to provide useful information for Korean pharmaceutical companies to set the suitable strategy for their situation to become a competitive company in global pharmaceutical market.
Yoo, Hyoung Sun;Kim, Ji Hui;Jun, Seung-Pyo;Seo, Jinny;Yoo, Jae Young
Journal of Korea Technology Innovation Society
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v.16
no.1
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pp.41-62
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2013
In order to facilitate the R&D of SMEs and foster innovative SMEs, the Small and Medium Business Administration is funding the SMEs in the focused supporting industries which are selected by a primary consideration of the political conformity. The correlations among the distribution of engaged SMEs, the distribution of SMEs which applied the fund, and the distribution of SMEs funded in the industries are analysed, in order to realize the effectiveness of the top-down method for selecting the focused supporting industries. As a result, there was a large deviation of both the number of the engaged SMEs and the number of the applied SMEs in the each industry, which means that the selected industries did not accurately reflect the needs for the technology development of SMEs. In addition, the distribution of the applied SMEs was strongly depended on the distribution of the engaged SMEs, which shows the trend that SMEs are trying to make R&D plans in their current industry rather than following the political drive. Moreover, because the distribution of the funded SMEs is strongly correlated with that of the applied SMEs, one could know that the selected industries were neither fostered strategically nor evenly but the fund was distributed simply according to the distribution of the applied SMEs. In this study, in order to complement the limitations of the top-down method, the bottom-up method which primarily consider the needs for the technology development of SMEs was suggested. It is desirable to consider in the order of the needs and the capabilities of SMEs, the technological perspective and marketability of the industry, and the political importance in the bottom-up method, and it is expected that the method could be flexibly used in the planning of the various programs.
Journal of the Korea Academia-Industrial cooperation Society
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v.19
no.5
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pp.480-488
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2018
The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.
Journal of the Korea Academia-Industrial cooperation Society
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v.19
no.2
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pp.192-201
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2018
This study intends to find efficient support methods for small and medium-sized businesses (SMBs) especially in the increasingly important mobile sector. We investigated regarding perceptions of mobile sector-specific support programs and their current state of usage, and perceptions of the new mobile market and the present conditions of each stage of production based on analysis of a survey that included 204 respondents in each SMB to achieve this goal. Concretely, detections through this research were the reasons for non-selection of support businesses, the future prospective mobile business area, the company's own first-preference item and the reason for the preference, plus new mobile areas of willingness to apply the company's own product and its own first-preference item, and the reason for the preference. In addition, we found out covering costs and revenue size of the maximum-revenue product for each stage (from the first to the fourth stage), the kind of a product's execution method connected to the product (e.g. self-production, outsourcing to another company to produce a product), and the execution method for pre-products. We can suggest a policy regarding efficient support alternatives for SMBs related to the mobile sector thanks to these findings.
Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R2 = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R2 = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.
The meaning and role of science culture based on such values as rational thinking, creativity, critical validation has been growing in the process of discussing various social problems. In order to diffuse science culture, it is important to sustain citizen's activeness by providing contents which can induce interest on the base of two-way communication between public and experts and to support citizen activities performed voluntarily. To that end, various people such as scientist, government policymaker, communicator, those in charge of culture and art, exhibition curator should make up collaboration system and such requirement as motivation, leadership, agreement between the participants, communication, trust relationship is also to be met properly in order to proceed collaboration efficiently. This study reviews how these factors are coming true in governmental project for science culture and develops proposal for improvement on the base of opinions collected through expert meetings, interviews, workshop and data research. In addition, it explains that government must strengthen scientific cultural project personnel and lay infra such as communications hub, regional center, platform and improve the business selection method to promote competition and collaboration among project participants with reformation of reward and regulatory systems. It is performed to suggest comprehensive ways to increase efficiency of project for science culture out of not the deficit model which regard public as passive acceptant but context model or PES(public engagement in science) that take public who focus his attention and participate actively into account.
Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1st to 2nd and 3rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1st (54.69%), 2nd (73.52%), and 3rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1st (0.640), 2nd (0.816), and 3rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.
Purpose - The current study is to compare the cognition of stakeholders on hosting a mega sports event between Korea and the United States. In particular, to understand their cognition and perceptual conflict towards hosting a mega sports event, the study employed conflict theory. Furthermore, the study reviewed the role of social capital in the process of managing the mega sports events. Research Design, Data, and Methodology - Of homogeneous sampling, purposeful sampling method and criterion-based selection approach were used to collect interview data from key stakeholders who have been involved in hosting a mega sports events in Korea and the United States. In-depth interview transcripts were reviewed multiple tiems after transcription to extract concepts and meanings that were pertinenet to the experience involving hosting a mega sports event. Further member checks was conducted to increase the credibility of the results. Results - Results can be summarized as followed: First, stakeholders of Korea have a strong desire for positive economic effects of a mega sports event, compared to those in the United States who are more concerned in enhancing the public interests and concerns. Second, in Korea, various socio-political issues emerged at the same time and conflicts among multiple stakeholders have aggravated the situations to coordinate the issues. This was because legal system supporting socio-trust has not been established. On the other hand, major stakeholders of the United States consisted of community members who have socio-trust and networks. Thereby these social resources have been found playing a key role in building social capital that assists the stakeholders to coordinate the current issues and to solve them. Conclusions - The current study analyzed the cognition and perceptual conflict of stakehoders in a mega sports event. Social capital has beend found as a key catalyst to increase a network and cooperation among stakeholders. In order to enhance social capital in managing a mega sports event hosted in Korea, legal systems that establish networks and relationships among the related stakeholders need to be developed. Furthermore, the systematic guideline needs to be developed, organizing the sub-committees according to the types of stakeholders and the categorized common needs.
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