• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.455-460
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• 2020

The governments of Korea, China, and Japan have operated comprehensive research institutes for railway technologies. Korea Railroad Research Institute (KRRI), China Academy of Railway Sciences Corporation Limited (CARS), and Railway Technical Research Institute (RTRI) are representatives of comprehensive railway research institutes in each country. KRRI was found to be the most advanced in the quantitative competitiveness of patents. In terms of qualitative competitiveness, KRRI has strength in civil engineering, whereas RTRI has strength in electricity. KRRI was found to have the greatest efforts in securing competitiveness in overseas property rights. By comparing the publication of papers, CARS published the most papers. On the other hand, from 2015, KRRI showed an upward trend and published the most papers. By examining the impact of the papers by the citation, KRRI was found to have higher competitiveness than the other two institutions. In the future, it will be necessary to perform big data analysis on patents and papers of the three organizations, derive the key research areas and promising technology areas for each institute, and establish a mid-to-long-term development plan for railway technology based on scientific evidence.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.225-253
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• 2015

The standard is a nation's one of the most important industrial issues that improve the social and economic efficiency and also the basis of the industrial development and trade liberalization. This research analyzes the enactment and the utilization of Korean industrial standards(KS) of various industries. This paper examines Korean industries' KS utilization status based on the KS possession, enactments and inquiry records. First, we implement multidimensional scaling method to visualize and group the KS possession records and the nation's institutional issues. We develop several hypothesis to find the decision factors of how each group's KS possession status impacts on the standard enactment activities of similar industry sectors, and analyzes the data by implementing regression analysis. The results show that the capital intensity, R&D activities and sales revenues affect standardization activities. It suggests that the government should encourage companies with high capital intensity, sales revenues to lead the industry's standard activities, and link the policies with the industry's standard and patent related activities from R&D. Second, we analyze the impacts of each KS data's inquiry records, the year of enactments, the form and the industrial segment on the utilization status by implementing statistical analysis and decision tree method. The results show that the enactment year has significant impact on the KS utilization status and some KSs of specific form and industrial segment have high utilization records despite of short enactment history. Our study suggests that government should make policies to utilize the low-utilized KSs and also consider the utilization of standards during the enactment processes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.247-253
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• 2011

In 2009, the Korean international construction industry showed a great performance, totaling 49.1 billion of contract and then this achievement has been considered a key milestone presenting that the international construction industry is one of the primary export industries of Korea. However, because of the construction firms' equalized levels of technology and price competitiveness, the competition among bidders is becoming more intensive. Moreover, this changing market circumstance leads construction firms to apply for bidding with the lowest price that could not meet the expected profitability of a project. Therefore, to develop various strategies based on project characteristics becomes one of the critical capabilities that construction firms should possess. Based on these motives, this study is aimed to investigate associations among number of bidders, contract award rate, profitability on international projects. For the correlation analysis, a set of data is structured by collecting all projects ranging from 1993 to 2009, excluding projects funded by official development and domestic funds. The number of bidders were grouped depending on project characteristics such as market regions, project types, bidding types, and order organization types. As the result of correlation analysis, contract award rate increases as the number of bidders increase, but the relationship between the number of bidders and profitability is negative. Understanding the correlations among variables can be employed in developing strategies to improve construction firms' competitiveness in the international construction market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.104-112
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• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Korean Journal of Construction Engineering and Management
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• v.10 no.6
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• pp.88-98
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• 2009

Construction industries have increasingly utilized Building information Modeling (BIM) technologies. Interoperability - the capability for BIM data to run from one computer application to another in the life cycle of a project has become one of the principal research areas. Enhancing interoperability inevitably requires information structures that are standardized throughout the construction industries. As a candidate of the data exchange standard, Industry Foundation Classes (IFC) has been developed and several researches recently performed to measure its richness of digital data exchange. But doubts have been brought up whether IFC meets a sufficient level of interoperability since the research result revealed a number of cases of information misrepresentation and loss. This research presents the lessons learned from the interoperability tests of three widely used 3D design applications including Graphisoft's Archicad, Autodesk's Revit, and Bentley's Bentley Architecture. One building's architectural and structural design data were modeled with the three tools and exchanged through IFC respectively for interoperability test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.843-852
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• 2022

Despite continuous safety management, the death rate of construction workers is not decreasing every year. Accordingly, various studies are in progress to prevent construction site accidents. In this paper, we developed an AI-based priority inspection target selection model that preferentially selects sites are expected to cause construction accidents among construction sites with construction costs of less than 5 billion won (KRW). In particular, Random Forest (90.48 % of accident prediction AUC-ROC) showed the best performance among applied AI algorithms (Classification analysis). The main factors causing construction accidents were construction costs, total number of construction days and the number of construction performance evaluations. In this study an ROI (return of investment) of about 917.7 % can be predicted over 8 years as a result of better efficiency of manual inspections human resource and a preemptive response to construction accidents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.