• Clinical and Experimental Reproductive Medicine
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• v.4 no.2
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• pp.23-34
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• 1977

A study was carried out for the evaluation on family situation of 900 homemakers those who had received tubal sterilization operation (laparoscopic and minilaparotomy) at family planning clinic, Seoul Red Cross Hospital from October 1974 to September 1977. The results obtained are as follows: 1) In age distribution, predominant age group consisted of those $31{\sim}35$ years frequency of 43.2% and average age was 33.3 years. 2) Educational level showed that homemakers who finished primary school accounted for 37.3% of the total and those having middle school education comprised 28.7%, and 24.3% of them were high school graduates, 8.3% of them were college graduates. On the other hand, husband's education level showed that, 37.6% of them were high school graduates, 29.7% were college graduates and 19.9% were middle school graduates, therefor, educational level of husbands was one step higher than wives. 3) In the gravidity at the tubal sterilization, the highest incidence(18.4%) among 853cases was the group of gravida 5, and 16.2% gravida 4, and the gravidity ranged $1{\sim}23$. Average gravidity of clients was 6.0. 4) Among the total number of 900 clients, 778cases (91.3%) had no experience of spontanous abortion, history of 1 abortion in 5.9%, 2 abortions in 1.8%, and it showed the decresed incidence of spontanous abortion recently. Average was 0.15. As regarding induced abortion, in spite of only 142 homemakers (16.7%) had no history of induced abortion, 20.5% had experienced 1 induced abortion before sterilization, 17.7% had 2 induced abortions, 14.6% had 3 abortions, 10.3% had 4 abortions, and 0.2% (2cases) had over 20 abortions. Average was 2.7. 5) In regarding to the number of living children, the greatest number (45.0%) of clients had 3 children, and 26.5% 2 children, 19.7% 4 children. Average number of their living children was 3.03. 6) Sex ratio of living children, among 18 clients those had 1 child, 17 homemakers had 1 boy and no girl, 1 homemaker had no boy and 1 girl only. Sex ratio showed that woman who had 2 boys and no girl accounted for 46.3%, however, those having no boy and 2 girls ocmprised only 1. 8% among 229 clients who had 2 children. Among 389 clients who had 3 children, in spite of woman who had 3 boys and no girl comprised 16.5%, but no boy and 3 girls only 1.5%. Among 170 clients who had 4 children, homemakers with 4 boys and no girl accounted for 4.1%, however, no boy and 4 girls 1.8% of the total. Among 52 clients, who had 5 children, woman with 5 boys and no girl comprised 3.9%, no boy and 5 girls 0%. Among 7 cases who had 6 children, there were 3 cases who had 3 boys and 3 girls, but only 1 cases had 1 boy and 5 girls and so on. These results showed a strong trend of male preference in Korea and this could be one of the inhibit factors for family planning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.321-340
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• 2017

The purpose of this study is to design a teaching model applying a problem-based learning model and to analyze the educational benefits that students felt. This study initiated a problem-based learning model from an analysis of existing studies. Through the consultation of experts, the scenario was modified. The problem was designed according to the design stage activity (problem analysis, PBL class suitability judgment, contents analysis, learner analysis, environment analysis, PBL operating environment decision, PBL class) and Strategic Design (problem situation design, learning resource design, Facilitation design, operational strategy design, evaluation design, PBL operating environment design). Based on the initial scenarios, the researcher analyzed the results of the problem - based learning through learners' reflective diaries. The researcher was able to confirm that the critical thinking and creativity were improved in the first PBL problem situation, and the method for smooth communication and cooperation was utilized. The results on analyzing the effects of education about the first problem-based learning and students' opinions about modification will be used for the second revision and supplement of the course design. This study introduces a case of PBL course development and expects further application and research.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.753-762
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• 2024

Tunnels have limited evacuation areas, and It is difficult for cars coming from behind to recognize the accident situation in front. Since an accident is very likely to lead to a serious secondary accident, a IoT-based smart tunnel accident warning system was studied to prepare for traffic accidents that occur in tunnels. If the measured values from the flame detection sensor, gas detection sensor, and shock detection sensor in the tunnel exceed the standard, it is judged to be an emergency situation and an alert system is designed to operate. The accident information message was designed to be displayed on the LCD and transmitted to drivers inside and outside the tunnel through a Wi-Fi communication network. A performance test system was established and performance evaluation was performed for several accident scenarios. As a result of the test, it was confirmed that the accident alert system can accurately detect accidents based on given reference values, perform alert procedures, and transmit alert messages to smart phones through Wi-Fi wireless communication. And through this, its effectiveness could be confirmed.

• Journal of the Daesoon Academy of Sciences
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• v.49
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• pp.365-399
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• 2024

Tang Taizong was regarded as the emperor with the idea of cherishing the common people, paying close attention to his subjects' military service, and mobilizing of the people as recorded in Zhenguan Zhengyao (Essentials for Government), from the Zhenguan period during the reign of Tang Taizong. Contrary to the above, in reality, Li Shimin, the name of Tang Taizong, mobilized many subjects in the construction of Luoyang Palace in second capital city called Dongdu and Jiucheng Palace, a summer palace during Sui-Tang dynasty, and he requisitioned the subjects of Guannei Province and Longyou Province to install and protect provisional powers and a county in the Gaochang Kingdom. Given his antinomic behavior, the evaluation of Li Shimin as an ideal Confucian monarch should be reconsidered based on Zhenguan zhengyao and other historical records. Dividing the 19 years before Goguryeo-Tang War in 645 into two periods, from the throne to 637 and from 639-644, the subjects were burdened by the construction of Luoyang Palace and Jiucheng Palace in the former period. In the latter period, after the conquest of the Gaochang Kingdom, there was a requisition for the transport of troops and supplies to be stationed in Xizhou, a reorganized prefecture and counties in the Gaochang Kingdom territory; probably mainly limited to the provinces of Guannei and Lungyou, which were geographically close to the West Regin that is now known as Xinjiang Uygur Autonomous Region. The burden in the latter period was relatively less than in the former period. Even so, the number of households in 639, fourteenth year of Tang Taizong's reign, was only 34.1% and 26.8%, respectively, in 609, the sixth year of Sui Yangdi's reign. In this situation, Taizong's Goguryeo Invasion was not conducted in an economically situation during the early days of the Tang Dynasty.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.565-575
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• 2011

The purpose of this study was to quantitatively evaluate the expression of both multi-metric qualitative habitat evaluation index (QHEI) and biological integrity index (IBI) for artificial structures eco-artificial fish reef (EAFR) for fishes asylum and habitat. Especially, both experimental evaluation and biological verification were performed in Water and Environmental Center's outdoor test-bed of Korea Institute of Construction Technology located in Andong-city, Gyeongsangbuk-do. The experimental conditions reflecting the situation of domestic river include the flow rate (e.g., $0.0{\sim}1.5m\;s^{-1}$), the width (e.g., 1.0~3.0 m), the depth (e.g., 0.05~0.70 m), and variable bed materials. Both QHEI and IBI were monitored for 8 months from May to December 2010. Whereas QHEI values were highest at experimental points of the E~F with an average of 83.1, those were lowest at B~C with an average of 78.1. However, QHEI values inside EAFR were more than 98.9, regardelss of space and time, and indicated more than the highest good of the state (Good) in the habitat. Overally, IBI values showed similar trend with QHEI, but were 44.2 in the winter dry season, compared to 32.8 of QHEI values. IBI values Also, IBI values inside EAFR were greater than those at the experimental channel by 5.7 to 11.4% and 18.7 to 34.8% in flow and stagnant conditions, respectively, indicating that EAFR can secure asylum and habitat for fish during the dry season. For comprehensive aquatic ecosystem assessment, the experimental channel showed generally fair conditions (Fair~Good), whereas EAFR showed good conditions (Good), suggesting that EAFR can be applied to aquatic ecosystem restoration and improvement.

• The Korean Journal of Archival Studies
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• no.25
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• pp.3-46
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• 2010

Digital records management is one whole system in which many social and technical elements are interacting. To maintain the trustworthiness, the repository needs periodical audit and certification. Thus, individual electronic records management agency needs toolkit that can be used to self-evaluate their trustworthiness continuously, and self-assess their atmosphere and system to recognize deficiencies. The purpose of this study is development of self-certification toolkit for repositories, which synthesized and analysed such four international standard and best practices as OAIS Reference Model(ISO 14721), TRAC, DRAMBORA, and the assessment report conducted and published by TNA/UKDA, as well as MoRe2 and current national laws and standards. As this paper describes and demonstrate the development process and the framework of this self-certification toolkit, other electronic records management agencies could follow the process and develop their own toolkit reflecting their situation, and utilize the self-assessment results in-house. As a result of this research, 12 areas for assessment were set, which include (organizational) operation management, classification system and master data management, acquisition, registration and description, storage and preservation, disposal, services, providing finding aids, system management, access control and security, monitoring/audit trail/statistics, and risk management. In each 12 area, the process map or functional charts were drawn and business functions were analyzed, and 54 'evaluation criteria', consisted of main business functional unit in each area were drawn. Under each 'evaluation criteria', 208 'specific evaluation criteria', which supposed to be implementable, measurable, and provable for self-evaluation in each area, were drawn. The audit and certification toolkit developed by this research could be used by digital repositories to conduct periodical self-assessment of the organization, which would be used to supplement any found deficiencies and be used to reflect the organizational development strategy.

• Journal of the Society of Disaster Information
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• v.15 no.3
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• pp.440-449
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• 2019

Purpose: Fire-fighting organizations are the very first agencies that take actions at a disaster scene, and emergency rescue training is carried out for prompt and systematic response. However, there is a need for a change due to the limitations in emergency rescue trainings such as perfunctory trainings or trainings without considering regional or environmental characteristics. Method: This study is to conduct theoretical review with regard to emergency rescue training and present a measure to improve the emergency rescue training through attitude survey targeting fire-fighting organizations in Gangwon area. Result: Facilities that cause difficulties when doing emergency rescue activity were mostly hazardous material storage and processing facilities. In terms of the level of emergency rescue and response task, most respondents answered that the emergency rescue was insufficient. The respondents answered that the effectiveness of emergency rescue training was helpful, but some responses showed that the training was not helpful because of scenario-based training, seeming training, similar training carried out every year, unrealistic training, and lack of competent authorities' interest and perfunctory participations. Most respondents answered for the appropriateness of emergency rescue training and evaluation that they were satisfied, however, they were not satisfied with the evaluation methods irrelevant to the type of training, evaluation methods requiring unnecessary training scale, and evaluation methods leading perfunctory participations of competent authorities. Lastly, respondents mostly answered that training reflecting various damage situations are necessary regarding the demand on the improvement of emergency rescue training. Conclusion: The improvement measures for emergency rescue training are as follows. First, it is necessary to set and prepare various training contents in accordance with regional characteristics by reviewing major disasters occurred in the region. Second, it is necessary to revise the emergency rescue training guidelines and manuals for appropriate training plan for each fire station, provide education and training for working-level staff members, and establish training in a way that types, tactics, and strategies of emergency rescue training could be utilized practically. Third, it is necessary to prepare a scheme that can lead participation and provide incentive or penalty from the planning stage of training in order to increase the participation of supporting and competent authorities when an actual disaster occurs. Fourth, it is necessary to establish support arrangements and cooperative systems by authority through training by fire stations or zones in preparation for disaster situations that may occur simultaneously. Fifth, it is necessary to put emphasis on the training process rather than the result for emergency rescue training and evaluation, pay attention to the identification of supplement points for each disaster situation and make improvements. Especially, type or form of training should be considered rather than evaluating the execution status of detailed processes, and the evaluation measure that can consider the completeness (proficiency) of training and the status of role performance rather than the scale of training should be prepared. Sixth, type and method of training should be improved in accordance with the characteristics of each fire station by identifying the demand of working-level staff members for an efficient emergency rescue training.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.