• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.811-825
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• 2023

In accordance with the government's stance of actively promoting intelligent administrative service policies through data utilization, in the disaster and safety management field, it also is proceeding with disaster and safety management policies utilizing data and constructing systems for responding efficiently to new and complex disasters and establishing scientific and systematic safety policies. However, it is difficult to quickly and accurately grasp the on-site situation in the event of a disaster, and there are still limitations in providing information necessary for situation judgment and response only by displaying vast data. This paper focuses on deriving specific needs to make disaster situation management work more intelligent and efficient by utilizing intelligent information technology. Through individual interviews with workers at the Central Disaster and Safety Status Control Center, we investigated the scope of disaster situation management work and the main functions and usability of the geographic information system (GIS)-based integrated situation management system by practitioners in this process. In addition, the data built in the system was reclassified according to purpose and characteristics to check the status of data in the GIS-based integrated situation management system. To derive needed to make disaster situation management more intelligent and efficient by utilizing intelligent information technology, 3 strategies were established to quickly and accurately identify on-site situations, make data-based situation judgments, and support efficient situation management tasks, and implementation tasks were defined and task priorities were determined based on the importance of implementation tasks through analytic hierarchy process (AHP) analysis. As a result, 24 implementation tasks were derived, and to make situation management efficient, it is analyzed that the use of intelligent information technology is necessary for collecting, analyzing, and managing video and sensor data and tasks that can take a lot of time of be prone to errors when performed by humans, that is, collecting situation-related data and reporting tasks. We have a conclusion that among situation management intelligence strategies, we can perform to develop technologies for strategies being high important score, that is, quickly and accurately identifying on-site situations and efficient situation management work support.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.17-32
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• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.197-214
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• 2024

This study aimed to investigate students' perceptions at science and technology specialized universities towards entrepreneurship support policies and to derive policy improvement measures by applying a bottom-up approach to reflect the requirements of the policy beneficiaries, i.e., the students. Specifically, the research explored effective execution strategies for student entrepreneurship support policies through a survey and analysis of KAIST students. The findings revealed that KAIST students recognize the urgent need for improvement in sharing policy objectives with the student entrepreneurship field, reflecting the opinions of the campus entrepreneurship scene in policy formulation, and constructing an entrepreneurship-friendly academic system for nurturing student entrepreneurs. Additionally, there was a highlighted need for enhancement in the capacity of implementing agencies, as well as in marketing and market development capabilities, and organizational management and practical skills as entrepreneurs within the educational curriculum. Consequently, this study proposes the following improvement measures: First, it calls for enhanced transparency and accessibility of entrepreneurship support policies, ensuring students clearly understand policy objectives and can easily access information. Second, it advocates for student-centered policy development, where students' opinions are actively incorporated to devise customized policies that consider their needs and the actual entrepreneurship environment. Third, there is a demand for improving entrepreneurship-friendly academic systems, encouraging more active participation in entrepreneurship activities by adopting or refining academic policies that recognize entrepreneurship activities as credits or expand entrepreneurship-related courses. Based on these results, it is expected that this research will provide valuable foundational data to actively support student entrepreneurship in science and technology specialized universities, foster an entrepreneurial spirit, and contribute to the creation of an innovation-driven entrepreneurship ecosystem that contributes to technological innovation and social value creation.

• Bangmulgwan gwa yeongu (The National Museum of Korea Journal)
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• v.1
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• pp.96-119
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• 2024

Some inscribed bricks excavated from the western region of North Korea have been found bearing an era name used after 314 when the Nangnang and Daebang Commanderies had been completely ousted from the region. Others have been found with an era name used in the early fifth century. This indicates that the tradition of constructing brick chamber tombs was sustained for a century after the disappearance of the two commanderies. However, brick chamber tombs were never adopted as a burial system for the ruling class of the Goguryeo Kingdom. The Tomb of Jang Mui built in 348 and the Tomb of Dongni built in 353 both departed from the typical brick chamber tomb style of the region, and elements associated with stone chamber tombs were added to them. The Tomb of Dongsu (Anak Tomb No. 3), which is similar to the other two tombs in that its occupant is of Chinese descent, was constructed in 357 not as a brick chamber tomb, but as an earthen mound tomb with a stone chamber. Still, the continuation of brick chamber tomb tradition in the next half century has been somewhat puzzling. Although dated inscribed bricks have served as important evidence for understanding the continuation of the brick chamber tomb tradition, there has been a problem of continually repeating previous studies. It has also been pointed out that there was an error in the interpretation of era names in some of the dated inscribed bricks that had been believed to have been produced in or after 357. For example, "second year of Taean" (Taian in Chinese), which had been understood to correspond to 386 (during the Former Qin Dynasty), in fact refers to 303 (during the reign of Emperor Hui of the Western Jin Dynasty). In the case of "first year of Geonsi" (Jianshi in Chinese), which had been believed to indicate 407 (during the Later Yan Dynasty), it actually refers to 301. "Geonsi" is the era name used during the period when Sima Yun ousted Emperor Hui of the Western Jin Dynasty and briefly occupied imperial throne. Outside these two cases, the remaining dated inscribed bricks thought to have been produced in or after 357 are those dated to the "third year of Wonheung" (Yuanxing in Chinese). However, a reexamination of these bricks reveals that what is really "Yeongheung" (Yongxing in Chinese) has been misread as "Wonheung." The third year of Yeongheung corresponds to either 306 during the Western Jin Dynasty or 352 during the Later Zhao Dynasty, but it is highly probable that it refers to 306. This means that there is no conclusive evidence to support the hypothesis that brick chamber tombs were built in the area until the late fourth century and even into the early fifth. Accordingly, the Tombs of Jang Mui and Dongni should be viewed as the latest known brick chamber tombs to be constructed in the western region of North Korea. Moreover, brick chamber tombs appear to have been no longer built in the area around the time when the Tomb of Dongsu was constructed. These speculations accord with the historical circumstances of the time.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.179-194
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• 2024

The aim of this study was to explore how a novice science teacher's epistemological framing, characterized from her modeling instruction, evolved over time. We observed that the teachers' framing changed over time, as she collaborated with researchers to plan, facilitate, and reflect on a series of lessons to support students' small-group scientific modeling. We tried to understand how such experiences contributed to the changes in her framing. One 8th grade science teacher with two years of teaching experience participated in the study. The teacher collaborated with researchers for four months to co-plan and facilitate 18 lessons that included small-group scientific modeling. She also engaged in cogenerative reflection on the lessons for 13 times. All of her lessons and reflections were video-recorded, transcribed, and qualitatively analyzed for the purpose of the study. Our findings showed that the teacher's epistemological framing, characterized from her interactions with students during modeling lessons, evolved during the study period: transitioning from an emphasis on students merely "filling in blanks" to prioritizing "constructing personal reasoning" and ultimately to focusing on the "social construction of scientific reasoning." The teacher's perception about what students are capable of changed, as she observed students during the modeling lessons, and this led to the shifts in her framing. Furthermore, through her engagement in planning, implementing, and reflecting on modeling lessons with researchers, she came to recognize the value of student collaboration in knowledge-building processes. These results can offer implications for supporting and studying teachers' epistemological framing and modeling-based teaching by partnering with them.

• Korean Journal of Poultry Science
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• v.51 no.2
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• pp.65-71
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• 2024

The feather-sexing method is widely used commercially for chick sex identification. However, for feather-sexing to be industrially practical, the early-feathering (EF) and late-feathering (LF) genes must existed within the foundation stock, a suitable feather-sexing lines must be established, and the accuracy of sex identification by feather-sexing must be ensured. Therefore, this study introduces the method of constructing the Korean native chickens (KNC) feather-sexing lines using EF and LF genes and evaluates the effectiveness of feather sex determination on commercial chicks produced from the constructed KNC lines. The results showed that both EF and LF chickens existed within the foundation stock, with the frequency of LF genes ranging from 0 to 0.205. In feather-sexing line establishment, the paternal strain of the grandparent stock (GPS) was fixed as EF (kk) for both sexes, while the maternal strain was composed of males with LF homozygotes (Z^KZ^K) and females with EF (Z^kW). Thus, in the parent stock (PS), male breeder had EF (Z^kZ^k) and female breeder had LF (Z^KW), resulting in chicks produced from their crosses having LF (Z^KZ^k) for males and EF (Z^kW) for females, allowing sex determination based on feather development. Additionally, to evaluate the effectiveness of feather-sexing for the produced commercial chicks, a study was conducted on 1,000 samples of the produced chicks to investigate the concordance between vent-sexing and feather-sexing, showing a matching rate of 93.1%.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.234-240
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• 2007

Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.