• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.26-39
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• 2016

Korea's main manufacturing industries, which have led its economy for the past three decades, are faced with a serious downturn and loss of competitive advantages due to the current economic depression, China's rise, and the drop of oil prices. Korean business firms must adopt the paradigm shift in their value strategies, along with a government-led industrial restructuring in order to gain sustainable competitive advantages. Business firms allocate their limited resources between value creation and value appropriation, however, what effect does strategic emphasis on value creation versus value appropriation have on a business firm's financial performance? This paper empirically addresses this issue by examining the effect of shifts in strategic emphasis on stock return. Furthermore, this study examines appropriate choices of strategic emphasis to gain differential financial performance. The data set used in this regression analysis comes from the KISLINE database of NICE Information Service. The variables that form the basis of this analysis are stock return, ROA, and Strategic Emphasis [(advertising expenditures-R&D expenditures)/assets]. The interactive effect with situational factors regarding the firm and the type of technological environment in which the firm is operating was also analyzed. Our results show that investors acknowledge a shift of strategic emphasis as a sign of stock valuation. In comparison to US, Korean business firms have weak value creation capabilities in high-technology industries, and weak value appropriation capabilities in low-technology industries. This proves Korean firms are fast followers in the global market. Our findings suggest that Korean firms have to adopt a balanced value strategy, nurturing value creation and developing value appropriation for overcoming the current economic downturn and becoming a first mover in the dawn of "Industry 4.0."

• Journal of Energy Engineering
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• v.7 no.2
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• pp.231-240
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• 1998

The global environmental problems such as CFC, energy losses in heat recovery system as well as summer peak time power demands, the development of high efficiency absorption refrigeration systems is one of the most promising method in this problems. The absorption refrigeration system to utilize treated sewage is available for environmental protection and energy conservation. Simulation analysis on the double-effect absorption refrigeration cucles with parallel or series flow type has been performed. LiBr+LiI+LiCl+LiNO$_3$ solution was selected as the new working fluid. The main purpose of this study is evaluating the possibilities of effective utilization of treated sewage as a cooling water for the absorber and condenser. The other purpose of the present study is to determine the optimum designs and operating conditions based on the operating constraints and the coefficient of performance in the parallel or series flow type. In this study, we found out the characteristic of new working solution through the cycle simulation and compared LiBr solution to evaluate. The absorption refrigeration machine using the new working fluid was obtained better results COP rise and compactness of system by comparison with LiBr solution.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.43-48
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• 2017

In recent years, the demand for ships and offshore platforms that can navigate and operate through the Arctic Ocean has been rapidly increasing due to global warming and large reservoirs of oil and natural gas in the area. Winterization design is one of the key issues to consider in the robust structural safety design and building of ships that operate in the Arctic and Sub-Arctic regions. However, international regulations for winterization design in Arctic condition regulated that only those ships and offshore platforms with a Polar Class designation and/or an alternative standard. In order to cope with the rising demand for operating in the Arctic region, existing and new Arctic vessels with a Polar Class designation are lacking to cover for adequate winterization design with HSE philosophy. Existing ships and offshore platform was not designed based on reliable data based on numerical and experiment studies. There are only designed as a performance and functional purposes. It is very important to obtain of reliable data and provide of design guidance of the anti-icing structures by taking the effects of low temperature into consideration. Therefore, the main objective of this paper reconsiders anti-icing design of aluminum helideck using the heating cable. To evaluate of reliable data and recommend of anti-icing design method, various types of analysis and methods can be applied in general. In the present study, finite element method carried out the thermal analysis with cold chamber testing for performance and capacity of heating cables.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.79-87
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• 2015

In this paper, a real time on-board orbit determination method using the extended kalman filter is suggested and its performance is analyzed in the environment of the orbit. Considering the limited on-board resources, the $J_2$ orbit propagate model and the GPS navigation solution are used for on-board orbit determination. The analysis result of the on-board orbit determination method implemented in DubaiSat-2 showed that position and velocity error are improved from 70.26 m to 26.25 m and from 3.6 m/s to 0.044 m/s, respectively when abnormal excursion errors is removed in the GPS navigation solution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

• STI Policy Review
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• v.8 no.2
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• pp.75-93
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• 2017

Recently, global competition in the manufacturing sector is driving firms in the manufacturing sector to conduct product innovation projects to maintain their competitive edge. The key points of product innovation projects are 1) what the purpose of the project is and 2) what expected results in the target market can be achieved by implementing the innovation. Therefore, this study focuses on the performance of innovation projects with a business viewpoint. In this respect, this study proposes the "achievement rate" of product innovation projects as a measurement of project performance. Then, this study finds the best strategies from various innovation activities to optimize the achievement rate of product innovation projects. There are three major innovation activities for the projects, including three types of R&D activities: Internal, joint and external R&D, and five types of non-R&D activities - acquisition of machines, equipment and software, purchasing external knowledge, job education and training, market research and design. This study applies decision tree modeling, a kind of data-mining methodology, to explore effective innovation activities. This study employs the data from the 'Korean Innovation Survey (KIS) 2014: Manufacturing Sector.' The KIS 2014 gathered information about innovation activities in the manufacturing sector over three years (2011-2013). This study gives some practical implication for managing the activities. First, innovation activities that increased the achievement rate of product diversification projects included a combination of market research, new product design, and job training. Second, our results show that a combination of internal R&D, job training and training, and market research increases the project achievement most for the replacement of outdated products. Third, new market creation or extension of market share indicates that launching replacement products and continuously upgrading products are most important.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.271-277
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• 2021

Companies are actively engaged in various innovation activities and intellectual property rights activities such as patents in order to survive in a fiercely competitive global environment. The purpose of this study is to understand the factors that influence corporate patent-related activities on business results. For this reason, we conducted a questionnaire survey of patent practitioners and R & D personnel of Chinese companies, and analyzed the causal relationship using PLS analysis tools. As a result of the analysis, it was found that compensation and obstacle factors have a significant effect on information sharing (collaboration)... It was found that information sharing (collaboration) has a significant impact on technology complementation (improvement) and corporate image. It was also found that technical complementation (improvement) and corporate image have an impact on management results. The results of this study will be able to find and strategically utilize the factors that promote and encourage companies to develop their patent activities.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.245-255
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• 2023

Time comparison techniques are necessary for generating and keeping Coordinated Universal Time (UTC) and distributing standard time clocks. Global Navigation Satellite System (GNSS) Common View, GNSS All-in-View, Two-Way Satellite Time and Frequency Transfer (TWSTFT), Very Long Baseline Interferometry (VLBI), optical fiber, and Network Time Protocol (NTP) based methods have been used for time comparison. In these methods, GNSS based time comparison techniques are widely used for time synchronization in critical national infrastructures and in common areas of application such as finance, military, and wireless communication. However, GNSS-based time comparison techniques are vulnerable to jamming or interference environments and it is difficult to respond to GNSS signal disconnection according to the international situation. In response, in this paper, Code-Division Multiple Access (CDMA) based All-to-All TWSTFT operation method is proposed. A software-based simulation platform also was designed for performance analysis in multi-TWSTFT signal environments. Furthermore, code and carrier measurement jitters were calculated in multi-signal environments using the designed simulation platform. By using the technique proposed in this paper, it is anticipated that the TWSTFT-based time comparison method will be used in various fields and satisfy high-performance requirements such as those of a GNSS master station and power plant network reference station.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.