• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.237-258
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• 2022

As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.922-934
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• 2009

The transport of contaminants and spatial variation in a deep reservoir are certainly governed by the thermal structure of the reservoir. There has been continuous efforts to utilize three-dimensional (3D) hydrodynamic and water quality models for supporting reservoir management, but the efforts to validate the models performance using extensive field data were rare. The study was aimed to evaluate a 3D hydrodynamic model, ELCOM, in Daecheong Reservoir for simulating heat fluxes and stratification processes under hydrological years of 2001, 2006, 2008, and to assess the impact of internal wave on the reservoir mixing. The model showed satisfactory performance in simulating the water temperature profiles: the absolute mean errors at R3 (Hoenam) and R4 (Dam) sites were in the range of $1.38{\sim}1.682^{\circ}C$. The evaporative and sensible heat losses through the reservoir surface were maximum during August and January, respectively. The net heat flux ($H_n$) was positive from February to September, while the stratification formed from May and continued until September. Instant vertical mixing was observed in the reservoir during strong wind events at R4, and the model reasonably reproduced the mixing events. A digital low-pass filter and zero crossing method was used to evaluate the potential impact of wind-driven internal wave on the reservoir mixing. The results indicated that most of the wind events occurred in 2001, 2006, 2008 were not enough to develop persistent internal wave and effective mixing in the reservoir. ELCOM is a suitable 3D model for supporting water quality management of the deep and stratified reservoirs.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.402-406
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• 2005

In this paper, we present a tool to calculate the distribution of amino acid contacts in proteins as well as in protein domains. The proteins are grouped according to the classification by Yanay Ofran and Burkhard Rost[1]. In addition, a protein's distribution was compared with that of proteins in the same group as well as the entire collection of proteins across all groups. With these statistics, biologists can pick out proteins which have characteristics that defer from the norm.

• Journal of Distribution Science
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• v.11 no.10
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• pp.27-36
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• 2013

Purpose - This paper empirically studies the effects of advanced design innovation strategy on business performance, to investigate manufacturing industries that can develop design-driven-innovation strategies. Many researchers now recognize the importance of design in a CEO's decision-making process. To analyze these effects, this study deduces the definition of advanced design strategy by reviewing existing studies. The advanced design is a strategy that is applied to improve business performance instead of the appearance of a product for increasing its sales. In terms of business processes, the advanced design strategy is defined as the incorporation of business activities prior to the development of the product, to offer new experiences and values to users, from those designs. Research design/data/methodology - This paper establishes a model for empirical analysis. In this study, we derived factors of the characteristics of advanced design based on previous studies. We tried to investigate whether advanced design innovation strategy and entrepreneur's characteristics could have any impact on business performance. At the same time, we tried to find out the moderating effect of entrepreneurs' characteristics. The advanced design is made up of three elements: precedence, integration, and immersion of design activities. These three elements are independent variables for the model. The dependent variables are: increased rate of sales, R & D performance, and public image of the company. Specifically, this study establishes a CEO's characteristics as a moderating variable between the independent and dependent variables. Results - We proved that the level of entrepreneurs' characteristics has a moderating effect on the business performance. The findings of this study offer the following theoretical implications. The precedence of design activities positively affects the increased rate of sales by offering new experiences to users and creating new values. The integration of design activities also has a positive effect on the R&D performance. In addition, the immersion of design activities positively influences all the elements comprising business performance. The analysis of moderating variables elucidates that CEO's characteristics have a moderating role between precedence, integration, and immersion of design activities, and business performance. Conclusions - The practical implications of the study are as follows. This study contributes to the progression of advance design theories by conducting an empirical study on the advanced design concept. More importantly, the empirical study on the CEO group seeking exploratory innovation supports Verganti's "design-driven innovation" concept, according to which design can make innovation successful by offering useful values to users, as evident in the case of many innovative companies, such as Nintendo and Apple. Future studies need to investigate the reliability of practical examples, including the various activities of business. We suppose that there may be real differences between the results of this study and the applicative situation in the presence of a CEO group.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.263-277
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• 2011

Global atmospheric $CO_2$ distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of $CO_2$ column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric $CO_2$ at $2^{\circ}{\times}2.5^{\circ}$ horizontal resolution using global $CO_2$ surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem $CO_2$ columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning $90^{\circ}S-82^{\circ}N$ were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation ($0.66{\leq}R^2{\leq}0.99$) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT $CO_2$ retrievals underestimate $CO_2$ column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric $CO_2$ budget and trends through 7 tagged $CO_2$ tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global $CO_2$ trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous $CO_2$ reductions from human activities are necessary in order to stabilize atmospheric $CO_2$ levels.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.749-760
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• 2022

Omics-driven synthetic biology is a multidisciplinary research field that creates new artificial life by employing genetic components, biological devices, and engineering technique based on genetic knowledge and technological expertise. It is also utilized to make valuable biomaterials with limited production via current organisms faster, more efficient, and in huge quantities. As the bioeconomic age begins, and the global synthetic biology market becomes more competitive, investment in research and development (R&D) and associated sectors has grown considerably. By overcoming the constraints of present biotechnologies through the merging of big data and artificial intelligence technologies, huge ripple effects are envisaged in the pharmaceutical, chemical, and energy industries. In agriculture, synthetic biology is being used to solve current agricultural problems and develop sustainable agricultural systems by increasing crop productivity, implementing low-carbon agriculture, and developing plant-based, high-value-added bio-materials such as vaccines for diagnosing and preventing livestock diseases. As international regulatory debates on synthetic biology are now underway, discussions should also take place in our country for the growth of bioindustries and the dissemination of research findings. Furthermore, the system must be improved to facilitate practical application and to enhance the risk evaluation technology and management system.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.6
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• pp.143-151
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• 2001

During the past several years, the major interests of car manufacturers in development of automatic transmission were in durability and shift quality. However, a large number of researches for improving shift quality that are based on dynamic characteristics of shifting mechanism have been rarely adopted in the developing process because it is quite difficult to predict the shifting performance from the dynamics simulation. One of the important reasons for the difference between simulation results and experiments arises from the automatic transmission hydraulic system that consists of many valves with high order model and shows a lot different dynamics to temperature variation. In this work, hardware-in-the-loop simulation system for automatic transmission was developed f3r improving the accuracy of simulated result by combining the real-time simulation model with the real hydraulic system. The real-time simulation for automatic transmission model excluding hydraulic system is executed with TI's TMS320C31 DSP and the interfacing board which includes 12bit A/D, PWM signal generator and driver, serial driver ,etc is designed for acquiring the simulation data and signal interface with hydraulic system. We verified the proper operation and correctness of shifting result by comparing the off-line simulation result with that of HILS and experimental result which was performed on transmission dynamometer driven by electric motor.