• 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.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.65-77
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• 2023

Background: The shortage of dental hygienists as assistant is a great concern to dental clinics, while dental hygienists are rather pursuing the role of oral hygiene control and preventive treatments which is the main role for dental hygienists in the United States. The dental hygienist and dental assistant system in the United States can be a reference in these discussions. Methods: Educational requirements for licensure and work areas for dental hygienists and dental assistants were investigated through the information provided by the American Dental Association (ADA), American Dental Hygienists Association, National Board Dental Hygiene Examination (NBDHE), Dental Assistants Association of America (ADAA), and Dental Assistants National Board (DANB). Results: In the United States, each state has different systems, but in general, dental hygienists obtain licenses after completing 2~3 years of associate degree programs in dental hygiene after obtaining basic learning skills, and mainly perform tasks related to patient screening procedures, oral hygiene management and preventive care. Dental assistants can take the license test after completing a training course of 9~11 months to obtain a dental assistant certification. Additional expanded work typically requires passing state qualification tests, completing a training program, obtaining a degree, or gaining clinical experience for a certain period of time, depending on the state Conclusion: The scope of work of dental hygienists designated by the Medical Engineer Act and the Enforcement Decree in Korea includes both the work of dental hygienists and dental assistants in the United States, and if a dental assistant system like the United States is introduced to address the current shortage of dental assistants, institutional supplementation such as adjustment of the scope of work and expansion of the role of dental hygienists in oral hygiene management and prevention work is needed and in-depth discussion is necessary.

• Journal of Bio-Environment Control
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• v.33 no.3
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• pp.163-171
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• 2024

This study was aimed to investigate the effects of layer-by-floor environmental conditions and lower shelf supplemental lighting on the productivity of fresh shoots when growing rosemary in multi-layer cultivation. The 10-cm cuttings from stock plants of common rosemary (Rosemarinus officinalis) were planted in a 128-hole tray, rooted, and then transplanted into pots of 750, 1,300, and 2,000 mL. Afterwards, they were placed on multi-layer shelves (width × length × height: 149 × 60 × 57 cm, 3-layer) in a two-linked greenhouse and cultivated using the sub-irrigation. The productivity of young shoots by layer of the multi-layer shelf was the highest on the third floor (top floor), but productivity decreased sharply after September due to stem lignification caused by excessive light during the summer. Conversely, the lower two layers exhibited faster growth rate of young shoots until the late cultivation period, but the quality decreased due to stem softening and leaf epinasty. To address the excessive light problem on the third floor during the summer, shading was implemented at 30% opacity in July and August, resulting in a 210% increase in rosemary young shoots count and a 162% increase in fresh weight per unit area compared to the unshaded control. To improve the lighting deficiency on the lower layer, supplemental lighting with LED at 30 W increased rosemary young shoot harvest by 168% from June to September compared to no supplemental lighting, but it decreased productivity after September. Therefore, when growing rosemary in multi-layer, it is judged that intensive production of young shoots is possible if the third floor (top layer) is shaded with 30% of light from July to August to prevent stem lignification, and the lower layer is temporarily supplemented with LED 30 W from June to September to increase young shoot growth.

• Journal of Fisheries and Marine Sciences Education
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• v.10 no.2
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• pp.139-161
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• 1998

This thesis includes a study on the legal regulation, the system and formalities on the control of illegal fishing. And the author analyzed the details of the lists of illegal fishing controlled by fishing patrol vessels of Ministry of Maritime Affairs and Fisheries from 1994 to 1996 in adjacent sea area of Korea. The results are summarized as follows ; 1. The fishing patrol vessels controlled total 826 cases in 2,726 days of 292 voyages by 17 vessels in 1994, total 1,086 cases in 3,060 days of 333 voyages by 18 vessels in 1995 and total 933 cases in 3,126 days of 330 voyages by 19 vessels in 1996. 2. The fishing period of illegal fishing was generally concentrated from April to September. But year after year, illegal fishing was scattered throughout the year. 3. The most controlled sea area of illegal fishing was the south central sea area in the sea near Port of Tongyeong. The sea area occupied about 36~51% of totality and the controlled cases were gradually increased every year. The second was the south western sea area in the sea near Port of Yosu. The sea area occupied about 18-27% and the controlled cases were a little bit increased every year. The third was the south eastern sea area in the sea near Pusan. The sea area occupied about 13~23% and the controlled cases were gradually decreased year by year. 4. The most controlled kind of illegal fishing was the small size bottom trawl. This occupied about 81-95% of totality and the controlled cases were gradually increased year by year. The second was the medium size bottom trawl. This occupied about 4-7% and the controlled cases were gradually decreased year by year. The third was the trawl of the coastal sea, this occupied about 2~4% and the controlled cases were a little bit decreased every year. 5. The most controlled address of illegal fishing manager was Pusan city which occupied about 33-51% of totality. The second was Cheonnam which occupied about 24-29%. The third was Kyungnam which occupied about 16~35%. 6. The most controlled violation of regulations was Article 57 of the Fisheries Act which occupied about 56-64% of totality. The second was Article 23 of Protectorate for Fisheries Resources which occupied about 21-36%. And the controlled cases by it were gradually increased every year.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.