• Journal of Distribution Science
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• v.14 no.5
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• pp.51-57
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• 2016

Purpose - In the year of 2015 and 2016, one of the items that got attention in CES was a drone. It has been 100 years since a drone emerged, but most were used for military purposes. As its use became diverse as of 2010, it got attention of the general public. In Korea, it was in 2011 that a drone was known to the public through an aerial video shooting for television program. This study tried to come up with suggestions by comparing domestic with overseas cases, and tried to consider the related technologies and systems with applying the role of drones in logistics service. Research design, data, and methodology - The overseas cases were regarded as drone's logistical purpose. The Prime Air service by US Amazon is still not commercialized and under pre-testing due to Federal Aviation Regulations, although it started in 2013. In Germany, DHL succeeded in delivering service testing which is called Parcelcopter, but it is not commercialized yet. Other than these, there are more attempts to prepare logistics service in China with Taobao, in France with Geopost's test, and in Africa. In Korea, CJ Korea Express tested delivery with a self-developed drone Results - In order to study for utilizing drones for logistics as the prerequisites, some overseas and domestic cases, which are currently considered, were reviewed. Also, the technologies and institutional requirements to commercialize drones for logistical purpose were reviewed. The reasons for using drones in logistics is to ensure the price competitiveness by reducing cost. The empirical test also will be needed because drone pilot areas are designated by ministry of land, infrastructure and transport. Conclusions - In order to utilize drones in logistics and foster the industry, this study would like to suggest the followings. First of all, size of drones for logistics needs to be fixed and their operating system should be standardized. Centralized investment resources are needed through standardization to ensure the market occupancy. Secondly, it is necessary to get the converged businesses that do research, develop and commercialize drones with the investment of private sector. Example can be found in Korea aerospace Industries. We can respond to the rapid growth of the market with intensive investing by integrating the private sector investment. Thirdly, institutional arrangements are needed to be established quickly. In the case of high-technologies like drones, institutional support often does not follow the rapid development of the technology. The problems can be found in securing drones-only airspace, creating drone-specialized pilot's license, matters related to remote controller and complementary regulations for drones in Aviation Act. If these regulations are not prepared or complemented at the right timing, technologies cannot be commercialized even though the development is completed. Fourthly, there is a need to secure the transparency regarding possible privacy and personal information protection problem while operating drones. Finally, in order to foster this new industry, government should focus on supporting R&D more in the long term than short term outcomes.

• The Journal of Industrial Distribution & Business
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• v.9 no.6
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• pp.77-85
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• 2018

Purpose - The purpose of this study is to subdivide trade transaction-centered structure in a logistics/distribution industry system to apply blockchain, to establish and resolve with which types of technology, and to provide policy direction of government institution and technology to apply blockchain in this kind of industry. Research design, data, and methodology - This study was conducted with previous researches centered on cases applied in various industry sectors on the basis of blockchain technology. Results - General fields of blockchain application include digital contents distribution, IoT platform, e-Commerce, real-estate transaction, decentralized app. development(storage), certification service, smart contract, P2P network infrastructure, publication/storage of public documents, smart voting, money exchange, payment/settlement, banking security platform, actual asset storage, stock transaction and crowd funding. Blockchain is being applied in various fields home and abroad and its application cases can be explained in the banking industry, public sector, e-Commerce, medical industry, distribution and supply chain management, copyright protection. As examined in the blockchain application cases, it is expected to establish blockchain that can secure safety through distributed ledger in trade transaction because blockchain is established and applied in various sectors of industries home and abroad. Parties concerned of trade transaction can secure visibility even in interrupted specific section when they provide it as a base for distributed ledger application in trade and establish trade transaction model by applying blockchain. In case of interrupted specific section by using distributed ledger, blockchain model of trade transaction needs to be formed to make it possible for parties concerned involved in trade transaction to secure visibility and real-time tracking. Additionally, management should be possible from the time of contract until payment, freight transfer to buyers through land, air and maritime transportation. Conclusions - In order to boost blockchain-based logistics/distribution industry, the government, institutionally, needs to back up adding legal plan of shipping, logistics and distribution, reviewing standardization of electronic switching system and coming up with blockchain-based industrial road maps. In addition, the government, technologically, has to support R&D for integration with other high technology, standardization of distribution industry's blockchain technology and manpower training to expand technology development.

• Safety and Health at Work
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• v.14 no.2
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• pp.141-152
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• 2023

Objective: We performed a meta-analysis of epidemiological results for the association between occupational exposure as a firefighter and cancer as part of the broader evidence synthesis work of the IARC Monographs program. Methods: A systematic literature search was conducted to identify cohort studies of firefighters followed for cancer incidence and mortality. Studies were evaluated for the influence of key biases on results. Random-effects meta-analysis models were used to estimate the association between ever-employment and duration of employment as a firefighter and risk of 12 selected cancers. The impact of bias was explored in sensitivity analyses. Results: Among the 16 included cancer incidence studies, the estimated meta-rate ratio, 95% confidence interval (CI), and heterogeneity statistic (I²) for ever-employment as a career firefighter compared mostly to general populations were 1.58 (1.14-2.20, 8%) for mesothelioma, 1.16 (1.08-1.26, 0%) for bladder cancer, 1.21 (1.12-1.32, 81%) for prostate cancer, 1.37 (1.03-1.82, 56%) for testicular cancer, 1.19 (1.07-1.32, 37%) for colon cancer, 1.36 (1.15-1.62, 83%) for melanoma, 1.12 (1.01-1.25, 0%) for non-Hodgkin lymphoma, 1.28 (1.02-1.61, 40%) for thyroid cancer, and 1.09 (0.92-1.29, 55%) for kidney cancer. Ever-employment as a firefighter was not positively associated with lung, nervous system, or stomach cancer. Results for mesothelioma and bladder cancer exhibited low heterogeneity and were largely robust across sensitivity analyses. Conclusions: There is epidemiological evidence to support a causal relationship between occupational exposure as a firefighter and certain cancers. Challenges persist in the body of evidence related to the quality of exposure assessment, confounding, and medical surveillance bias.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.99-123
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• 2004

We focus in this study on incremental financing decisions and estimate a logit model for the probability a firm will choose a private placement over a public bond issue. We hypothesize that information asymmetry, financial risk, agent cost, and proprietary information may affect a firm's choice between public debt and private placements. We find that as the size of firm increases, the probability of choosing a private placement declines significantly. The age of the firm, however, is not a significant factor affecting the firm's choice between public and privately-placed bond. The coefficients on the firm's leverage and non-investment grade dummy are significantly positive, meaning firms with high financial risk and credit risk select private placements. The findings regarding agency-related variables, PER and Tobin's Q, are somewhat complex. We find significant evidence that firms with high PER prefer private placements to public bonds, suggesting that borrowers with options to engage in asset substitution or underinvestment are more likely to choose private placements. The coefficient of Tobin's Q is negative, but not significant, which weakly support the hold-up hypothesis. When we construct an interaction term on the Tobin's Q with a non-investment rating dummy, however, the Tobin's Q interaction term becomes positive and significant. Thus, high Tobin's Q firms with a speculative rating are significantly more likely to choose a private placement, regardless of the potential hold-up problems. The ratio of R&D to sales, proxy for proprietary information, is positively significant. This result can be interpreted as evidence in favor of a role for proprietary information in the debt sourcing decision process for these firms.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.47-59
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• 2016

Parcel service is to deliver goods from one place to the designated destination requested according to user request. Parcel operations such as sorting, distributing, etc. or the managed information are heterogeneous by the companies. Additionally, it is impossible to support interoperability between companies with unformatted data of manual processing. Most parcel package boxes attached to paper typed waybill is attached is delivered to consignee. So, security problems such as personal information leaking are occurred, or extra processing time and logistics costs are needed due to wrong or the damaged information. Business environment of parcel service is rapidly changed as introducing unmanned delivery or the advanced technology such as Internet of Things. User want to know the accurate status or steps from parcel service request to delivery. To provide these requirements, the unified and integrated waybill information for reliable transportation of parcel service is needed. This information will provide to pickup or delivery carrier, warehouse or terminal, and parcel service user per pickup, transport, and delivery stage of parcel delivery service. Therefore, this paper defines the simplified and unified information model for parcel service waybill by analyzing information systems used for logistics unit processes that is occurred to parcel service, and manual work processes, and developing the relevant information of work flows occurred between business processes or transactions with the collected or processed information by from parcel service's stages. It is possible to share these standard model between business entities, and replacing paper typed waybill will improve national life safety as preventing security threats by paper typed waybill. As a result, it will promote the public interest from the stakeholder's perspective.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.1-13
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• 2017

The purpose of this study is to find out which factors determine the success of start-up in the initial market and what are the most important determinants. For the empirical analysis, the questionnaire related to the analysis of success factors for start-up success was designed according to the quantitative analysis (AHP technique). First, we selected 8 representative success factors for successful start-up in the initial market. In order to determine the degree of priority among these factors, we surveyed 12 entrepreneurs who are interested in entrepreneurship, universities, research institutes, and public officials. As a result of the empirical analysis, 51% of the funds in the tier 1 were ranked as the top priority to determine success factors. Followed by research and development (32.5%), management (8.7%) and marketing (7.8%). In particular, when each of the four items is calculated as 100 according to the result of the tier 1, and the tier 2 is converted, the foreign investment is analyzed as 43.7%. It was followed by 15.14% of R & D facilities, 14.07% of ideas, 8.7% of managerial ability, 7.29% of domestic investment, 5.85% of buyer feedback, 3.3% of development strategy and 1.95% of marketing strategy. Among the eight success factors, overseas investment items showed the closest preference to half, and it was the most important variable that determines the success or failure of market entry. The implication of this study is that many start-ups in Korea expect to receive investment and support from overseas accelerators. This means that overseas investment itself has been recognized as a start-up that makes services and products that can be used in the global market. A high preference for attracting foreign investment is due to the fact that the amount of investment is larger than that of Korea and that it can flexibly cope with the pressure on the performance compared to domestic investors. In this study, it was meaningful that we could confirm this fact through questionnaires of start-up experts. In future research, we need to find a viable alternative through studying how to provide start-up to foreign direct investment at the national level.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.6
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• pp.175-183
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• 2016

The government set up "national happiness, the hope of a new era of national vision under' job center of the creative economy" to achieve by national goals in the first and figure achieved through the establishment of new growth engines of the youth unemployment problem solve and national level there are a number of business start-up support. September 8, 2015 announced the Government's look at the '2016 Year of the budget, the government for new growth engines greatly promoted the venture entrepreneurship ecosystem revitalization and research and development (R & D) the business for enhanced performance in 2017. According to the direction of this study is to evaluate the current creative economy business incubator at the comparison whether the correct orientation mainly entrepreneurs, entrepreneurs preliminary recognition of student entrepreneurship ecosystem. Entrepreneurs 113 people in that way, 71 people pre-entrepreneurs, students 60, workers were founding agencies conducted an online survey of 47 people, 16 people Investors, 50 public and 11 additional persons including a total of 368 people. This study is in line with the orientation of these entrepreneurs to create economic status and recognition of the Republic of Korea entrepreneurship ecosystem, pre- entrepreneurs, students will examine the comparative analysis around. Analysis, social perception of entrepreneurship is somewhat higher than it was confirmed that the negative response of 32.2% to 36.3% of positive response. Social awareness of entrepreneurs showed a 2-fold higher response rate than the negative of response of 17.1% to 41.7% responding that positive recognition for the current start-up environment is bad, the response is good response to higher response rate than 23.5% to 41.1% It showed. The percentage of responses that better respect the entrepreneurship environment of the future Republic of Korea showed a higher response rate than the rate of 23% in response to deteriorate to 41.2%, with 52.9% awareness is the percentage that responded that the bad part about the ruthless Korea's entrepreneurship environment in China good part as response rate approximately three times greater than the 17.7% showed high response rates. Social awareness of entrepreneurs experience the presence of the founding start-up experience was confirmed that the more negative the number increases, the more the contrary the number of start-up experience increased awareness of the current and future environment of entrepreneurship was identified as a positive entrepreneurship environment. Also recognized was confirmed to change the parent of the more positive changes in the start-up of entrepreneurs doctor also positive about entrepreneurship, start-up entrepreneurs start with a doctor's motivation for founding non-economic reasons than for economic reasons has confirmed Higher. This study showed the overall level overview analysis of the status and recognition of the Republic of Korea entrepreneurship ecosystem. Future studies need to be a proposal for an existing previous studies for more precise direction to go forth to analyze the entrepreneurship ecosystem with a focus on problems and improvement of the Republic of Korea entrepreneurship ecosystem.