• Journal of Venture Innovation
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• v.4 no.2
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• pp.57-75
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• 2021

KHNP's shared growth activities are based on such public good. Reflecting the characteristics of a comprehensive energy company, a high-tech plant company, and a leading company for shared growth, it presents strategies to link performance indicators with its partners and implements various measures. Key tasks include maintaining the nuclear power plant ecosystem, improving management conditions for partner companies, strengthening future capabilities of the nuclear power plant industry, and supporting a virtuous cycle of regional development. This is made by reflecting the specificity of nuclear power generation as much as possible, and is designed to reflect the spirit of shared growth through win-win and cooperation in order to solve the challenges of the times while considering the characteristics as much as possible as possible. KHNP's shared growth activities can be said to be the practice of the spirit of the times(Zeitgeist). The spirit of the times given to us now is that companies should strive for sustainable growth as social air. KHNP has been striving to establish a creative and leading shared growth ecosystem. In particular, considering the positions of partners, it has been promoting continuous system improvement to establish a fair trade culture and deregulation. In addition, it has continuously discovered and implemented new customized support projects that are effective for partner companies and local communities. To this end, efforts have been made for shared growth through organic collaboration with partners and stakeholders. As detailed tasks, it also presents fostering new markets and new industries, maintaining supply chains, and emergency support for COVID-19 to maintain the nuclear power plant ecosystem. This reflects the social public good after the recent COVID-19 incident. In order to improve the management conditions of partner companies, productivity improvement, human resources enhancement, and customized funding are being implemented as detailed tasks. This is a plan to practice win-win growth with partner companies emphasized by corporate social responsibility (CSR) and ISO 26000 while being faithful to the main job. Until now, ESG management has focused on the environmental field to cope with the catastrophe of climate change. According to KHNP is presenting a public enterprise-type model in the environmental field. In order to strengthen the future capabilities of the nuclear power plant industry as a state-of-the-art energy company, it has set tasks to attract investment from partner companies, localization and new technologies R&D, and commercialization of innovative technologies. This is an effort to develop advanced nuclear power plant technology as a concrete practical measure of eco-friendly development. Meanwhile, the EU is preparing a social taxonomy to focus on the social sector, another important axis in ESG management, following the Green Taxonomy, a classification system in the environmental sector. KHNP includes enhancing local vitality, increasing income for the underprivileged, and overcoming the COVID-19 crisis as part of its shared growth activities, which is a representative social taxonomy field. The draft social taxonomy being promoted by the EU was announced in July, and the contents promoted by KHNP are consistent with this, leading the practice of social taxonomy

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.67-83
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• 2023

The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.1-20
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• 2023

The purpose of this study is to identify the unique entrepreneurial behavioral attributes of social enterprises that are distinct from for-profit enterprises at the organizational level, derive a social entrepreneurship model that reflects the unique characteristics of social enterprises as strategic decision-making and organizational behavioral tendencies. In order to effectively achieve the purpose of this study, previous studies were reviewed, and qualitative studies were conducted using the grounded theory method based on this. In this study, social entrepreneurship was identified as five sub-factors through a series of analysis processes, and 'Social value orientation; Innovativeness; Pro-activeness; Risk taking; Cooperation orientation' was newly proposed. It also proposed a new social entrepreneurship framework that integrates and explains the multidimensional model of social entrepreneurship by reviewing and connecting the relationships between each sub-factor of the research model. The 'social entrepreneurship framework' classified the social entrepreneurship model into 'pro-social motivation', 'pro-social behavior', and 'entrepreneurial behavior' attributes and explained them by linking them with each sub-factor that constitutes social entrepreneurship. The most remarkable difference between this study and previous studies is that it identified and added 'Cooperation orientation' as a sub-factor constituting social entrepreneurship from the organizational-level behavioral point of view. Through this study, 'Cooperation orientation' was identified as a major behavioral tendency for social enterprises to materialize pro-social motivation, strengthen the economic foundation of business activities, and improve the efficiency of business operations. 'Cooperation orientation' is a major behavioral tendency that strengthens the legitimacy of business activities between pro-social motivation and profit-seeking of social enterprises, improves the performance of social value creation activities, and overcomes the difficulties of resource constraints through cooperation with the outside and improves operational efficiency. In addition, it was confirmed that 'Cooperation orientation' is a major behavioral tendency of social enterprises that is manifested simultaneously in social value-oriented activities and entrepreneurial activities pursuing profit. The 'Cooperation orientation' newly identified in the study supplements the previous research, increases the explanatory power of the theory of social entrepreneurship, and provides the basis for theoretical expansion to subsequent researchers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.14 no.1
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• pp.48-55
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• 2009

Seasonal variations of microalgal biomass and community composition in both the sediment and the seawater were investigated by HPLC pigment analysis in an estuarine muddy sandflat of Gwangyang Bay from January to November 2002. Based on the photosynthetic pigments, fucoxanthin, diadinoxanthin, and diatoxanthin were the most dominant pigments all the year round, indicating that diatoms were the predominant algal groups of both the sediment and the seawater in Gwangyang Bay. The other algal pigments except the diatom-marker pigments showed relatively low concentrations. Microphytobenthic chlorophyll ${\alpha}$ concentrations in the upper layer (0.5 cm) of sediments ranged from 3.44 (March at the middle site of the tidal flat) to 169 (July at the upper site) mg $m^{-2}$, with the annual mean concentrations of $68.4{\pm}45.5,\;21.3{\pm}14.3,\;22.9{\pm}15.6mg\;m^{-2}$ at the upper, middle, and lower tidal sites, respectively. Depth-integrated chlorophyll ${\alpha}$ concentrations in the overlying water column ranged from 1.66 (November) to 11.7 (July) mg $m^{-2}$, with an annual mean of $6.96{\pm}3.04mg\;m^{-2}$. Microphytobenthic biomasses were about 3${\sim}$10 times higher than depth-integrated phytoplankton biomass in the overlying water column. The physical characteristics of this shallow estuarine tidal flat, similarity in taxonomic composition of the phytoplankton and microphytobenthos, and similar seasonal patterns in their biomasses suggest that resuspended microphytobenthos are an important component of phytoplankton biomass in Gwangyang Bay. Therefore, considering the importance of microphytobenthos as possible food source for the estuarine benthic and pelagic consumers, a consistent monitoring work on the behavior of microphytobenthos is needed in the tidal flat ecosystems.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1327-1341
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• 2022

Purpose To evaluate the pattern of use and the perception of digital breast tomosynthesis (DBT) among Korean breast radiologists. Materials and Methods From March 22 to 29, 2021, an online survey comprising 27 questions was sent to members of the Korean Society of Breast Imaging. Questions related to practice characteristics, utilization and perception of DBT, and research interests. Results were analyzed based on factors using logistic regression. Results Overall, 120 of 257 members responded to the survey (response rate, 46.7%), 67 (55.8%) of whom reported using DBT. The overall satisfaction with DBT was 3.31 (1-5 scale). The most-cited DBT advantages were decreased recall rate (55.8%), increased lesion conspicuity (48.3%), and increased cancer detection (45.8%). The most-cited DBT disadvantages were extra cost for patients (46.7%), insufficient calcification characterization (43.3%), insufficient improvement in diagnostic performance (39.2%), and radiation dose (35.8%). Radiologists reported increased storage requirements and interpretation time for barriers to implementing DBT. Conclusion Further improvement of DBT techniques reflecting feedback from the user's perspective will help increase the acceptance of DBT in Korea.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.16-22
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• 2024

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.177-198
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• 2024

Article 22(1) of the Library Act, which was completely revised in December 2006, stipulated that regional representative libraries are statutory organizations, and Article 25(1) of the Library Act, which was revised again in late 2021, renamed them as metropolitan representative libraries and expanded their duties. The reason why cities and provinces are required to specify or establish and operate metropolitan representative libraries is that in addition to their role as public libraries for public information use, cultural activities, and lifelong learning as stipulated in Article 23 of the Act, they are also responsible for the legal works of metropolitan representative libraries as stipulated in Article 26, and lead the development of libraries and knowledge culture by serving as policy libraries, comprehensive knowledge information centers, support and cooperation centers, research centers, and joint preservation libraries for all public libraries in the city or province. Therefore, it is necessary to analyze and diagnose whether the metropolitan representative library has been faithfully fulfilling its legal works for the past 15 years(2009-2023), and whether it is properly providing the results of its statutory planning and implementation on its website to meet the digital and mobile era. Therefore, this study investigated and analyzed the performance of the metropolitan representative library for the last two years based on the current statutory tasks and evaluated the extent to which it provides them through its website, and suggested complementary measures to strengthen its web services. As a result, it was analyzed that the web services for legal works that the metropolitan representative library should perform are quite insufficient and inadequate, so it suggested complementary measures such as building a website for legal works on the homepage, enhancing accessibility and visibility through providing an independent website, providing various policy information and web services (portal search, inter-library loan, one-to-one consultation, joint DB construction, data transfer and preservation, etc.), and ensuring digital accessibility of knowledge information for the vulnerable.

• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.263-276
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• 2024

Fish community characteristics and the influence of sampling gear were investigated in Lake Singal, South Korea, from August 2020 to October 2021. The employed sampling gears included a kick net, cast net, gill net, and fyke net, which are commonly utilized within the lake. Across three survey stations, a total of 18 fish species from seven families, comprising 3,501 individuals and contributing to a total biomass of 117,670 grams, were identified. Dominance among species was assessed based on individual count and biomass. Pseudorasbora parva was the most abundant, constituting 29.9% of the total catch, followed by Zacco platypus (25.1%) and Micropterus salmoides (19.3%). In terms of biomass, Carassius auratus was predominant, accounting for 45.1%, followed by Cyprinus carpio (17.4%) and M. salmoides (14.3%). Among the sampled species, three were identified as endemic to Korea: Squalidus japonicus coreanus, Cobitis nalbanti, and Odontobutis interrupta. Additionally, four exotic species were recorded, including M. salmoides and Lepomis macrochirus, both classified as invasive alien species, along with C. cuvieri and a variant of Cyprinus carpio (nudus type). Analysis of the average standard length (SL) and body weight (BW) revealed significant size variations among species. P. parva, the dominant species, measured 60 ± 24.1 mm (SL) and weighed 4.4 ± 3.42 g (BW). The subdominant species, Z. platypus, exhibited an SL of 82 ± 17.6 mm and a BW of 10.4 ± 7.27 g. M. salmoides, another dominant species, registered 96 ± 25.1 mm (SL) and 24.9 ± 96.02 g (BW), while C. auratus measured 125 ± 77.3 mm (SL) and weighed 168 ± 336.5 g (BW). In terms of gear-specific performance, the kick net captured eight species from three families, totaling 302 individuals with a biomass of 1,269 g. The cast net was more effective in coastal zones, collecting 11 species from four families, amounting to 948 individuals and 31,343 g of biomass. The gill net yielded the highest biomass, capturing 13 species from four families with 682 individuals weighing 69,695 g. The fyke net recorded the highest species diversity and number of individuals, capturing 15 species from seven families, totaling 1,569 individuals and 15,362 g of biomass. The fyke net proved most efficient in species and individual counts, whereas the gill net was superior for biomass collection. Conversely, the kick net demonstrated effectiveness in collecting small benthic species in coastal areas.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.