• Knowledge Management Research
• /
• v.24 no.4
• /
• pp.103-129
• /
• 2023

This study aims to analyze the network structure of technological innovation resources in SMEs, especially manufacturing firms, and reveal the differences between innovative and non-innovative firms. The study first analyzes connection centrality, flow-mediated centrality, and power centrality for all firms, and derives structural equivalence through CONCOR analysis. Then, the network structure of innovative and non-innovative firms was compared and analyzed according to innovation performance and creation. The results show that entrepreneurship and corporate innovation strategy have a significant impact on the analysis of technological innovation resources of all firms. According to the CONCOR analysis, the innovation resources of SMEs are organized into seven clusters, which can be defined as intrinsic product innovation resources, competitive advantage promotion resources, cooperative activities resources, information system resources, and innovation protection resources. The network analysis of innovative and non-innovative firms showed that innovative firms focused on enhancing competitiveness and improving quality, while non-innovative firms tended to focus more on existing products and customers. In addition, innovative firms had eight clusters, while non-innovative firms had six clusters, suggesting that innovative firms utilize resources diversely to pursue structural change and new value creation, while non-innovative firms operate technological innovation resources in a more stable form. This study emphasizes the importance of entrepreneurship and corporate innovation strategy in SMEs' technological innovation, and suggests that strong internal efforts are needed to increase innovativeness. These findings have important implications for strategy formulation and policy development for technological innovation in SMEs.

• Journal of vocational education research
• /
• v.36 no.1
• /
• pp.47-75
• /
• 2017

The purpose of this study was to provide basic data for policy making for secondary vocational education in each region and transformation in vocational high schools. To achieve this, the regional strategic industries in Daejeon and Jeonnam were selected, new demand for vocational high school graduates was forecasted in each industry and occupation. The results of the study are as follows. First, locational quotient analysis and regional shift-share analysis revealed that Daejon and Jeonnam have different strategic industries. Daejon, unlike Jeonnam strategically develops 'manufacturing food, beverage and tobacco', 'manufacturing timber and paper, printing and copying', 'public service and administration of national defense and social security' and 'manufacturing electrical devices, electronics and precision devices'. Jeonnam has specialized industries distinguished from Daejon's, which are 'manufacturing of machinery transportation equipments and etc', 'manufacturing of non-metallic minerals and metal products', 'electric, gas, steam and water supply systems/industries', 'manufacturing coal and chemical products, refining petroleum', 'mining' and 'agriculture, forestry and fishery'. Second, new demand for vocational high school graduates by occupations and industries showed regional differences(in Daejon and Jeonnam). According the forecast, Daejon will have many workforce demands based on manufacturing industries, on the other hand Jeonnam's focused on service industries. Analysis by occupations was also different, Daejon showed high demands on professional and related workers, while Jeonnam requested many new office and service workers. Third, new workforce demand by occupations in regional strategic industries is big part of overall new workforce demand both in Daejon and Jeonnam. Forth, according to the results of analyzing the new demand for vocational high school graduates in Daejeon and Jeonnam in terms of industry location quotient and change effect, there was high demand in industries with positive total change effects. In terms of location quotient, Daejeon and Jeonnam showed different results.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.10
• /
• pp.289-299
• /
• 2016

Weapon systems require reliability in the development phase for efficient combat readiness. Improved reliability in various manufacturing processes have been achieved using data analysis. However, data analysis in the development phase is difficult due to problems such as the lack of data, high cost, and the importance of security. Therefore, Post Logistics Support (PLS) data collected following integration is analyzed for long-term quality improvement of weapon systems. In this study, we propose a methodology for examining the correlation between the failure rate and PLS data as follows: First, key variables affecting reliability were identified the correlation between variables on the failure rate examined. Second, corresponding analysis was conducted for determining the correlation between patterns of categorical data. Third, extract categories with the higher contribution and quality of representation, and find the highest variable correlated with failure period through visualization. Then, after selecting patterns which have shorter failure period, the cause of decreased reliability was confirmed through frequency analysis. This study will contribute to improving reliability when developing new weapon systems and will help to strengthen the combat readiness of military.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.11
• /
• pp.672-682
• /
• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.6
• /
• pp.29-39
• /
• 2020

Purpose: The purpose of this study is, in the situation where rapid response to the rapidly changing environment is required due to the development of the fourth industrial revolution such as artificial intelligence, virtual reality, and the internet of things, robotics, big data, additive manufacturing, bio-health, sharing economy and in the organizational culture aspiring toward the innovation of a major company, small business and a public institution, to analyze what influence a job-engagement and stress make, and what influence individual's self-efficacy as a moderator mediator makes, and to offer basic data for improving job-engagement and lowering job-stress. Research design, data, and methodology: For doing this, the literature and the empirical studies were combined. Deriving innovation-oriented organizational culture as factors affecting the job engagement and job stress through the literature, and have established hypotheses to verify them. We have collected data of 281 from ex,ecutives and staff-members working in areas including major company, small business and officials (the central government, a local public service, the prosecution, the police, and school). And these data were analyzed by SPSS 23 version. Results: Based on these data, the results of analysis were as follows; First, the innovation-oriented organizational culture which was recognized by organizational members had effect on job-stress. Second, the innovation-oriented organizational culture which was recognized by organizational members influenced job-stress. Third, in the relationship between the innovation-oriented organizational culture and job-engagement, self-efficacy did not influenced job-engagement. Finally, in the relationship between the innovation-oriented organizational culture and job-stress, self-efficacy influenced job-stress. Conclusions: Innovation-oriented organizational culture places importance on the organization's adaptability and flexibility in the external environment, so companies need to establish an innovation-oriented organizational culture favorable to achieving survival and successful innovation, and to develop and disseminate programs of positive and continuous organizations to improve task enthusiasm, reduce task stress, and enhance organizational performance. In the future, it will be necessary to verify the effectiveness of various organizational culture types through comparative analysis with companies that actively maintain an innovation-oriented organizational culture (Google, Kakao, etc.) and companies that prefer hierarchy-oriented organizational culture, relationship-oriented organizational culture, and market-oriented organizational culture.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.4
• /
• pp.835-845
• /
• 2013

Association rule of data mining techniques is the method to quantify the relevance between a set of items in a big database, andhas been applied in various fields like manufacturing industry, shopping mall, healthcare, insurance, and education. Philosophers of science have proposed interestingness measures for various kinds of patterns, analyzed their theoretical properties, evaluated them empirically, and suggested strategies to select appropriate measures for particular domains and requirements. Such interestingness measures are divided into objective, subjective, and semantic measures. Objective measures are based on data used in the discovery process and are typically motivated by statistical considerations. Subjective measures take into account not only the data but also the knowledge and interests of users who examine the pattern, while semantic measures additionally take into account utility and actionability. In a very different context, researchers have devoted a lot of attention to measures of confirmation or evidential support. The focus in this paper was on asymmetric confirmation measures, and we compared confirmation measures with basic association thresholds using some simulation data. As the result, we could distinguish the direction of association rule by confirmation measures, and interpret degree of association operationally by them. Futhermore, the result showed that the measure by Rips and that by Kemeny and Oppenheim were better than other confirmation measures.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.9 no.1
• /
• pp.153-164
• /
• 2014

This study is to investigate the effects of intangible assets such as research & development, education & training and advertisement on firm values of high-technology firms and low-technology firms listed in the KOSDAQ market, and to analyze the value-relativeness between the audit quality of companies and the expenditure of intangible assets. The substitute measurement of firm values is Tobin's Q model. The sample period for positive analysis is from 2003 to 2008, and the samples, excepting for financial business, are manufacturing companies of closing accounts corporate on December, based on companies of KOSDAQ that are listed in security. Finally, data from about 305 companies are used in this analysis. Followings are the results of the analysis. First, research & development, education & training of high-technology firms have an effect on firm values, and education & training of low-technology have an effect on firm values. Second, we find that audit quality(BIG4) increases the value relevance of R&D expenditures of high-technology firms and audit quality(BIG4) increases the value relevance of education & training expenditures of low-technology firms. This paper is meaningful in that it verified the value-relativeness of cost of intangible assets compared with high-technology firms to low-technology firms.

• Journal of Environmental Science International
• /
• v.17 no.4
• /
• pp.423-437
• /
• 2008

Until comparatively lately, the annual time series of the $SO_2$ concentration had been shown in a decreasing trend in Ulsan as well as other Korean cities. However, the high concentration of $SO_2$ was frequently found in the specific countermeasure region including the national industrial complex such as Mipo and Onsan in the Ulsan city for the period of $2001{\sim}2004$. There are many conditions that can influence the high concentration of $SO_2$ at monitoring sites in Ulsan, such as: First, annual usage of the fuel including sulfur increased in comparison with the year before in spite of the fuel conversion policy which wants to use low sulfur oil less than 3% and LNG. Second, point source, such as the power plants and the petroleum and chemistry stacks, was the biggest contributor in $SO_2$ emission, as a analyzed result of both the air quality modeling and the stack tole-monitoring system (TMS) data. And third, the air pollutants that occurred in processes of homing and manufacturing of the fuel including sulfur were transported slow into a special monitoring site by accumulating along the frontal area of see-breeze. It was concluded that Ulsan's current environmental policy together with control methods should be changed into the regulation on total amount of emission, including a market-based emission trading with calculating of atmospheric environmental critical loads, for the $SO_2$ reduction like the specific countermeasure for the $O_3$ and PM10 reduction in the Seoul metropolitan area. And this change should be started in the big point sources of $1{\sim}3$ species because they are big contributors of Ulsan's $SO_2$ pollution. Especially it is necessary to revitalize of the self-regulation environmental management. Other control methods for sustaining the $SO_2$ reduction are as follows: maintenance of the fuel conversion policy, reinforcement of the regional stationary source emission standard, and enlargement of the stack TMS.

• Design & Manufacturing
• /
• v.11 no.1
• /
• pp.45-49
• /
• 2017

Currently, a lot of efforts to make increases the manufacturing efficiency have tried and there is growing the interest to implementing the machining operation through CAM automation and optimization. This kind of movement has shown gradually in 5X milling as well as 3X milling task. By the way, in case of 5X milling, it is difficult to hire the CAM experts who is an experience for 5X machining and also it has too big trouble to use them due to high cost. For this reason, you can see the manufacturer who is concern the CAM S/W to provide the NC automation program that beginners can generate easily the 5X milling in short term and the existing 5X milling process can be improved. These requirements need to make a NC automation process including the practical machining strategies same as the generation by NC expert. In order to support this, it is necessary to directly apply the 3D machining part based on NC template which includes the machining procedures, standard cutter library, auto machine area selection, analyze tool for part shape, machining condition setting considering the material stiffness to be provided by CimatronE and it should be created the 5axis machining data by a minimized operation. With user-friendly, CimatronE's NC machining automation tools improve the 5-axis machining process and speed up the process, maximizing work efficiency and improving product productivity compared to existing machining tasks.

• Knowledge Management Research
• /
• v.19 no.2
• /
• pp.151-162
• /
• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.