• Journal of Climate Change Research
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• v.9 no.2
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• pp.181-195
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• 2018

On December 12, 2015, the Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Paris Agreement, in which several developed and developing countries all committed to participating in the reduction of greenhouse-gas (GHG) emissions. South Korea has submitted an intended nationally determined contribution (INDC) proposal with a target to cut down 37% greenhouse gas business as usual (BAU) until 2030 in preparation for the 2030 GHG BAU. Under the post-2020 regime, which will be launched from 2021 as the agreement entered into force early, it is expected that efforts to support GHG reduction and adaptation to climate change in developing countries will be accelerated with the utilization of technologies and financial resources of developed countries. South Korea has established the Basic Plan for Climate Change Response and the Basic National Roadmap for Greenhouse Gas Reductions by 2030 to promote the response to climate change at the government level. The Ministry of Science and ICT, as the National Designated Entity designated by the UNFCCC, has come up with middle and long-term strategies for climate technology cooperation. South-Korea has an abundance of energy-consuming industries to support its export-oriented industrial structure; it is thus expected that achieving the GHG reduction target will incur a considerable cost. Moreover, in order to meet the reduction target (11.3%) of the intended nationally determined contribution proposed by South Korea, it is necessary for South Korea to actively promote projects that can achieve GHG reduction achievements, and financial resources are needed as leverage to reduce risks that can occur in the early stages of projects and attract private sector investment. This paper summarizes the theoretical discussions on climate finance and conducted a comparative analysis on the status of the funds related to climate change response in the UK, Germany, Japan and Denmark. Through this, we proposed the legal and policy tasks that should be carried forward to raise public funds that can be used for creation of new industries related to climate change as well as to reduce GHG emissions in South Korea. The Climate Change Countermeasures Act, which has been proposed by the National Assembly of South-Korea, stipulates the establishment of funds but there is no additional funding except for general account. In this regard, it is also possible to take measures such as the introduction of carbon tax or the collection and use of royalties through technology research and development projects for climate change, such as Industrial Technology Innovation Promotion Act. In addition, since funds are used in various fields such as domestic greenhouse gas reduction, technology development, and overseas projects, it is necessary to establish a system in which various ministries cooperate with the operation of the fund.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.72-80
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• 2021

Based on the previous year's statistics, 37% of buildings in South Korea are aged over 30 years. As the number of the aging buildings increases, so does the need for maintenance. Building maintenance involves a significant number of works; the work of 'maintenance manager' accounting for the largest part. Currently, the maintenance history record is mostly in drawing or handwritten form which makes reviewing the data highly time consuming. Therefore, to improve the convenience of maintenance works and optimize historical data management, the existing maintenance process was analyzed. Problems were derived and a smart device-based automation model was established. In order to establish a smart device-based automation model, ① general flow of facility management process was analyzed and related articles were reviewed, ② current maintenance process was optimized, ③ functional block diagram of BIM Data, COBie Data, IoT, and AR-based automated maintenance management model was created, ④ a smart device-based automated maintenance management model was constructed, ⑤ finally, the above system was verified by testing the aforementioned model in the field site, evaluating the time required for the maintenance process and reviewing maintenance history data against the current one.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.1
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• pp.85-94
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• 2022

This retrospective study aimed to evaluate the difference in measurement between conventional orthodontic analysis and artificial intelligence orthodontic analysis in pediatric and adolescent patients aged 7 - 15 with the mixed and permanent dentition. A total of 60 pediatric and adolescent patients (30 mixed dentition, 30 permanent dentition) who underwent lateral cephalometric radiograph for orthodontic diagnosis were randomly selected. Seventeen cephalometric landmarks were identified, and 22 measurements were calculated by 1 examiner, using both conventional analysis method and deep learning-based analysis method. Errors due to repeated measurements were assessed by Pearson's correlation coefficient. For the mixed dentition group and the permanent dentition group, respectively, a paired t-test was used to evaluate the difference between the 2 methods. The difference between the 2 methods for 8 measurements were statistically significant in mixed dentition group: APDI, SNA, SNB, Mandibular plane angle, LAFH (p < 0.001), Facial ratio (p = 0.001), U1 to SN (p = 0.012), and U1 to A-Pg (p = 0.021). In the permanent dentition group, 4 measurements showed a statistically significant difference between the 2 methods: ODI (p = 0.020), Wits appraisal (p = 0.025), Facial ratio (p = 0.026), and U1 to A-Pg (p = 0.001). Compared with the time-consuming conventional orthodontic analysis, the deep learning-based cephalometric system can be clinically acceptable in terms of reliability and validity. However, it is essential to understand the limitations of the deep learning-based programs for orthodontic analysis of pediatric and adolescent patients and use these programs with the proper assessment.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• Korean Journal of Animal Reproduction
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• v.9 no.2
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• pp.133-139
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• 1985

An immunoassay may be defined as an analytical procedure involving the competitive reaction between a limiting concentration of specific antibody and two populations of antigen, one of which is labelled or immobillized. The advent of immunoassay has revolutionised our knowledge of reproductive physiology and the practice of veterinary and clinical medicine. Radioimmunoassay (RIA) was the first of these methods to be developed, which meausred the analyte with good sensitivity, accuracy and precision (1,2). The essential components of RIA are:-(i) a limited concentration of antibodies, (ii) a reference preparation, and (iii) an antigen labelled with a radioisotope (usually tritium or iodine-125). Most procedures invelove isolating the antibody-bound fraction and measuring the amount of labelled antigen. Good facilities are available for scintilltion counting, data reduction nd statistical analysis. RIA is undergoing refinement through:-(i) the introduction of new techniques to separate the antibody-bound and free fractions which minimize the misclassification of labelled antigen into these compartments, and the amount of non-specfic binding. (3), (ii) the development of non-extration for the measurement of haptens (4), (iii) the determination of a, pp.rent free (i.e. non-protein bound) analytes (5), and (iv) the use of monoclonal antibodies(6). In 1968, Miles and Hales introduced in important new type of immunoassay which they termed immunora-diometric assay (IRMA) based on t도 use of isotopically labelled specific antibodies(7) in a move from limited to excess reagent systems. The concept of two-site IRMAs (with a capture antibody on a solid-phase, and a second labelled antibody to a different antigenic determinant of the analyte) has enabled the development of more sensitive and less-time consuming methods for the measurement of protein hormones ovar wide concentration of analyte (8). The increasing use of isotopic methos for diverse a, pp.ications has exposed several problems. For example, the radioactive half-life and radiolysis of the labelled reagent limits assay sensitivity and imposes a time limit on the usefulness of a kit. In addition, the potential health hazards associated with the use and disposal of radioactive cmpounds and the solvents and photofluors necessary for liquid scientillation counting are incompatable with the development of extra-laboratory tests. To date, the most practical alternative labels to radioisotopes, for the measurement of analytes in a concentration > 1 ng/ml, are erythrocytes, polystyrene particiles, gold sols, dyes and enzymes or cofactors with a visual or colorimetric end-point(9). Increased sensitivity to<1 pg/ml may be obtained with fluorescent and chemiluminescent labels, or enzymes with a fluorometric, chemiluminometric or bioluminometric end-point. The sensitivity of any immunoassay or immunometric assay depends on the affinity of the antibody-antigen reaction, the specific activity of the label, the precision with which the reagents are manipulated and the nonspecific background signal (10). The sensitivity of a limited reagent system for the measurement of haptens or proteins is mainly dependent upon the affinity of the antibodies and the smalleest amount of reagent that may be manipulated. Consequently, it is difficult in practice to improve on the sensitivity obtained with iodine-125 as the label. Conversely, with excess reagent systems for the measurement of proteins it is theoretically possible to increase assay sensitivity at least 1000 fold with alternative luminescent labels. To date, a 10-fold improvement has been achieved, and attempts are being made to reduce the influence of other variables on the specific signal from the immunoreaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.817-823
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• 2023

Manufacturing plants face the challenge of reducing energy use in response to climate change. Reducing energy consumption can be seen as one of the most important issues, such as reducing production costs and improving efficiency. Among manufacturing industries, the increase in energy consumption in the food industry is gradually increasing along with the improvement of the standard of living and the increase in population. In order to save energy in food processing plants, it is important to identify and analyze energy consumption characteristics in energy-consuming processes. Prior to this, it is necessary to monitor and analyze existing energy consumption to derive reduction measures. In this study, a small and medium-sized food processing plant producing processed meat products was used as a case study to identify and analyze the energy consumption structure at typical cycle/stage level of the batch heating process. From this, we tried to establish realistic and quantitative goals that can be obtained under individual process operating conditions. The results of this study will be used as basic data for the development of diffusion and pervasive energy saving FEMS technology for common core processes of food factories of small and medium-sized enterprises in the future.

• Information Systems Review
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• v.24 no.1
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• pp.59-87
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• 2022

Since over-the-top (OTT) service has emerged as a new way of consuming video contents, OTT markets grow exponentially and the competition among the OTT services becomes intense. Only limited systematic research effort has been paid to understand why users subscribe such OTT services among other services. Therefore, we used developmental sequential mixed methods approach to find out the quality factors and their effect on post-subscription experiences and continuance intention. In the qualitative study, we derived six factors which a user considers important to continue the subscription. Based on the explored factors, we hypothesized a research model with modified three qualities from ISSM. The proposed research model was validated through quantitative research, a survey of 226 OTT service users in South Korea, using structural equation modeling. The results indicated that content quality is the key factor affecting both perceived enjoyment and satisfaction whereas system quality affects satisfaction, and service quality only affects enjoyment. Enjoyment affects satisfaction which sequentially affects continuance usage intention. This study contributes to research by modifying ISSM through mixed methods. It also provides OTT service providers with insight to enhance users' post experience and continuance intention to use the service through qualities derived from the interview.

• Journal of Radiation Industry
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• v.17 no.2
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• pp.127-134
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• 2023

Pharmacokinetic (PK) data provide pivotal information in drug development, and they are usually first studied in the preclinical stage using various animals. However, quite often, animal PK data may not match with human PK, especially in metabolites. Thus, most regulatory agencies in the world make it mandatory to obtain metabolite information using ¹⁴C radiolabeled drug in human for small molecule drug candidates. However, such studies are expensive and time consuming and they are usually done at the end of Phase II trials using ~3.7 MBq of ¹⁴C labeled drug in a limited number of human subjects. Introduction of accelerator mass spectrometry (AMS) in this kind of study has revolutionized it. Since AMS can measure ¹⁴C level as close as natural abundance, it can quantify the amounts of ¹⁴C labeled drugs and their metabolites produced in human body that consumes less than the amount of 0.0037 MBq of ¹⁴C labeled drug, a very safe level of radioactive dose in human. Therefore, it is now possible to conduct human ¹⁴C studies safely in early clinical trials without spending hefty amount of money and time. Korea Radioisotope Center for Pharmaceuticals(KRICP) at Korea Institute of Biological and Medical Sciences(KIRAMS) has established an AMS facility in 2018, housing a 0.5MV AMS manufactured at the US National Electrostatics Corps (NEC). The AMS instrument has been validated using various standard samples that have been prepared at Lawrence Livermore National Laboratory in the US, a worldly reputable provider of AMS standards. In this paper, we present a mass balance study for acetaminophen in rats using AMS and prove that the study results are equivalent with those of literature, which shows the AMS facilities at KRICP has successfully installed and be ready to be used in the various PK studies using ¹⁴C labelled compounds for new drug development.