• Journal of agricultural medicine and community health
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• v.49 no.1
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• pp.13-36
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• 2024

Objectives: The purpose of this field case report is 1) to analyze the community's strategy and performance in responding to infectious diseases through the case of COVID-19 infectious disease crisis response of Daegu Metropolitan City, and 2) to interpret this case using governance theory and infectious disease response governance framework. and 3) to propose a strategic model to prepare for future infectious disease outbreaks of the community. Methods: Cases of Daegu Metropolitan City's infectious disease crisis response were analyzed through researchers' participatory observations. And review of OVID-19 White Paper of Daegu Metropolitan City, Daegu Medical Association's COVID-19 White Paper, and literature review of domestic and international governance, and administrative documents. Results: Through the researcher's participatory observation and literature review, 1) establishment of leadership and response system to respond to the infectious disease crisis in Daegu Metropolitan City, 2) citizen's participation and communication strategy through the pan-citizen response committee, 3) cooperation between Daegu Metropolitan City and governance of public-private medical facilities, 4) decision-making and crisis response through participation and communication between the Daegu Metropolitan City Medical Association, Medi-City Daegu Council, and medical experts of private sector, 5) symptom monitoring and patient triage strategies and treatment response for confirmed infectious disease patients by member of Daegu Medical Association, 6) strategies and implications for establishing and utilizing a local infectious disease crisis response information system were derived. Conclusions: The results of the study empirically demonstrate that collaborative governance of the community through the participation of citizens, private sector experts, and community medical facilities is a key element for effective response to infectious disease crises.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.59-70
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• 2024

Objectives: The purpose of this study is to analyze each country's infectious disease response capacities and, based on this, find areas for improvement in Korea's infectious disease management response. Methods: First, the capacity to respond to the COVID-19 infectious disease was analyzed by country using the SPAR scores of 96 countries around the world released by WHO in 2022. Second, we analyzed each country's specific COVID-19 quarantine performance using Our World in Data and the Global Health Security Index (GHSI). Results: First, the quarantine intensity index on January 24, 2021 was the highest in the Southeast Asia branch at 67.6, which had strong quarantine measures, and the lowest at 44.5 in the Africa branch. As of December 31, 2022, the quarantine intensity index in Europe was significantly lowered to 11.6. Second, the factor that influenced the SPAR indicator on the total number of patients per million population was national laboratory (C4), p=.027, and the factor that influenced the total number of deaths per million population was infection prevention and control (C9), p=.005., Risk Communication and Community Participation (C10) p=.040. The influential factor on GDP per capita was infection prevention and control (C9) p=.009, and the influential factor on GHSI was infection prevention and control (C9) p=.002. Conclusion: The research findings indicate that it was difficult to find a correlation between the SPAR, which is each country's self-assessment of their infectious disease capacities, and the number of COVID-19 cases or the intensity of pandemic responses. However, mortality rates, as well as factors such as the Global Health Security Index (GHSI) and national income, appear to be somewhat influenced. For future improvements in infectious disease management and response in our country, it is necessary to develop pandemic strategies that can reduce socio-economic costs based on more scientific and reliable data like JEE or GHSI, especially in preparation for potential unknown emerging infectious diseases. Based on this, proactive decision-making led by a control tower of experts and effective health communication are also required to respond to public health crises at a national level.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.21-45
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• 2023

The purpose of this study was to analyze the curriculum competencies of relationship-forming ability and practical problem-solving ability reflected in the activity tasks corresponding to the content elements of 'Love and marriage', 'Preparation for parenthood', 'Pregnancy and childbirth', 'Child care', and 'Family culture and intergenerational relationship' in the 2015 revised high school technology & home economics textbooks. The data are 330 activity tasks from 12 kinds of high school technology & home economics textbooks. The sub-factors of the relationship-forming ability were selected as Respect for Diversity, Consideration and Care, Family Relationship and Community Spirit, Empathy Ability, Conflict Management, and Communication, and the sub-factors of practical problem-solving ability were selected as Practical Reasoning, Decision Making, Value Judgment, Critical Thinking, and Executive Power. Based on the analysis criteria, the results of the two analyses and the expert review are as follows. First, regarding both the core concepts 'Development' and 'Relationship', the share of relationship-forming ability was relatively higher than practical problem-solving ability, and conflict management and executive power were the least reflected. For the core concept 'Development', Family Relationship and Community Spirit and Critical Thinking were the most reflected sub-factors, and for the core concept 'Relationship', Consideration and Care and critical thinking were the most reflected sub-factors. Second, in the case of the relationship-forming ability, the examples of activity tasks across sub-factors of each subject competency were devised to understand diverse opinions and sentiments and to develop competencies to care for each other and maintain healthy family relationships. In the case of practical problem-solving ability, the tasks allowed students to objectively analyze the socio-cultural background underlying the real-life problem, explore alternatives, and apply in their own lives.

• Journal of Digestive Cancer Research
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• v.8 no.1
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• pp.1-50
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• 2020

Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.67-96
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• 2023

The purpose of this study was to develop and validate a scale that can grasp the reality of the three systems of action for middle and high school students in home economics. For this purpose, a total of 105 questions, 35 questions for each systems of action, were developed as a 5-point Likert scale in order to measure technical action, communicative action, and emancipative action as preliminary questions by reviewing domestic and international literature related to the three systems of action. The procedure for revising and supplementing the developed preliminary questions by reviewing the content validity of the home economics education expert was executed twice. A preliminary survey was conducted on middle and high school students with 70 developed preliminary questions, and 166 copies were collected. As a result of exploratory factor analysis of the collected questionnaires to test the validity of the scale, it was found that 38 questions 7 factors were appropriate. After constructing this survey based on the results of exploratory factor analysis, this survey was conducted on middle and high school students, and 548 copies were collected and a confirmatory factor analysis was performed. A total of 38 questions were finally selected through confirmatory factor analysis, including basic living ability 5 questions, self-management ability 4 questions, information processing ability 4 questions, communication/interpersonal ability 12 questions, critical thinking ability 3 questions, decision-making ability 7 questions, empowerment 3 questions. The Model Fit was χ²=1846.741(p<.001), CFI=0.865, TLI=0.853, RMSEA=0.058, and the Standardized Regression Weights for each question was more than 0.5, so it can be seen as a suitable measurement instrument for measuring the status of the three systems of action of middle and high school students in home economics. The three systems of action scales were found to have significant correlations with self-acceptance, future planning, intimacy, uniqueness, which are sub-factors of the self-identity scale, and social participation scales therefore confirmed that they have recognized concurrent validity.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.05a
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• pp.545-570
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• 2015

Korea is among the ten countries with the largest R&D budget and the highest R&D investment-to-GDP ratio, yet the subject of security and protection of R&D results remains relatively unexplored in the country. Countries have implemented in their legal systems measures to properly protect cutting-edge industrial technologies that would adversely affect national security and economy if leaked to other countries. While Korea has a generally stable legal framework as provided in the Regulation on the National R&D Program Management (the "Regulation") and the Act on Industrial Technology Protection, many difficulties follow in practice when determining details on security management and obligations and setting standards in carrying out national R&D projects. This paper proposes to modify and improve security level classification standards in the Regulation. The Regulation provides a dual security level decision-making system for R&D projects: the security level can be determined either by researcher or by the central agency in charge of the project. Unification of such a dual system can avoid unnecessary confusions. To prevent a leakage, it is crucial that research projects be carried out in compliance with their assigned security levels and standards and results be effectively managed. The paper examines from a practitioner's perspective relevant legal provisions on leakage of confidential R&D projects, infringement, injunction, punishment, attempt and conspiracy, dual liability, duty of report to the National Intelligence Service (the "NIS") of security management process and other security issues arising from national R&D projects, and manual drafting in case of a breach. The paper recommends to train security and technological experts such as industrial security experts to properly amend laws on security level classification standards and relevant technological contents. A quarterly policy development committee must also be set up by the NIS in cooperation with relevant organizations. The committee shall provide a project management manual that provides step-by-step guidance for organizations that carry out national R&D projects as a preventive measure against possible leakage. In the short term, the NIS National Industrial Security Center's duties should be expanded to incorporate national R&D projects' security. In the long term, a security task force must be set up to protect, support and manage the projects whose responsibilities should include research, policy development, PR and training of security-related issues. Through these means, a social consensus must be reached on the need for protecting national R&D projects. The most efficient way to implement these measures is to facilitate security training programs and meetings that provide opportunities for communication among industrial security experts and researchers. Furthermore, the Regulation's security provisions must be examined and improved.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.