• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.3
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• pp.273-294
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• 2016

This research is part of the Korean-ASEAN Official Development Assistance (ODA) project, specifically exploring the possibility of using public libraries as a place for Information and Communication Technology (ICT) literacy training for women from ASEAN (Association of Southeast Asian Nations) countries. Women from ASEAN countries are often minorities in ICT capacity building. A survey of 1,000 female public library users - 100 people from each of the ten ASEAN countries - and in-depth interviews with librarians from national libraries were conducted. The survey results showed that 68.8% of respondents perceived public libraries as a suitable place for ICT literacy training. 27.5% of respondents visited libraries for ICT-related activities, including information retrieval, e-mail, SNS, etc., Meanwhile, findings from the interviews highlighted the importance of having up-to-date ICT infrastructure - computers, Internet, professional ICT skill training for librarians, strategic planning for policies, budgets, and cooperation with other related institutions - in public libraries in order to provide effective ICT training.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.395-402
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• 2018

Increased emphasis on competency-based learning modules and widespread departure from traditional models of Halstedian apprenticeship have made surgical simulation an increasingly appealing component of medical education. Surgical simulators are available in numerous modalities, including virtual, synthetic, animal, and non-living models. The ideal surgical simulator would facilitate the acquisition and refinement of surgical skills prior to clinical application, by mimicking the size, color, texture, recoil, and environment of the operating room. Simulation training has proven helpful for advancing specific surgical skills and techniques, aiding in early and late resident learning curves. In this review, the current applications and potential benefits of incorporating simulation-based surgical training into residency curriculum are explored in depth, specifically in the context of plastic surgery. Despite the prevalence of simulation-based training models, there is a paucity of research on integration into resident programs. Current curriculums emphasize the ability to identify anatomical landmarks and procedural steps through virtual simulation. Although transfer of these skills to the operating room is promising, careful attention must be paid to mastery versus memorization. In the authors' opinions, curriculums should involve step-wise employment of diverse models in different stages of training to assess milestones. To date, the simulation of tactile experience that is reminiscent of real-time clinical scenarios remains challenging, and a sophisticated model has yet to be established.

• Journal of Radiation Protection and Research
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• v.48 no.1
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• pp.20-27
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• 2023

Background: As the number of nuclear facilities nearing their pre-determined design life increases, demand is increasing for technology and infrastructure related to the decommissioning and decontamination (D&D) process. It is necessary to consider the nature of the dismantling environment constantly changing and the worker doing new tasks. A method was studied that can calculate the effect of learning and the change in work time on the work process, according to the learning-forgetting curve model (LFCM). Materials and Methods: The LFCM was analyzed, and input values and scenarios were analyzed for substitution into the D&D process of a nuclear facility. Results and Discussion: The effectiveness and efficiency of the training were analyzed. It was calculated that skilled workers can receive a 16.9% less collective radiation dose than workers with only basic training. Conclusion: Using these research methods and models, it was possible to calculate the change in the efficiency of workers performing new tasks in the D&D process and the corresponding reduction in the work time and collective dose.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.145-153
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• 2024

Through the 106th session of the International Maritime Organization(IMO)'s Maritime Safety Committee(MSC), a mandatory safety training requirement for all personnel transferred or accommodated for offshore industrial activities was established and adopted under the name of SOLAS Chapter XV, IP(Industrial Personnel) Code. This regulation mandates pre-boarding safety training to enable individuals to anticipate and mitigate hazardous risks in navigation and operational environments. Consequently, the IP Code includes provisions regarding the training content for industrial personnel and regulations for the refusal of master who has a full responsibility for individuals who have not completed the required training(non-qualified industrial personnel). Referred to as the IP Code, this agreement is set to enter into force in July 2024, necessitating the establishment and operation of safety education for industrial personnel boarding ships before that date. Accordingly, this paper reviews the legal requirements related to training within IP code and analyzes the details of models including training objectives, target audience, duration, and course structure of safety trainings such as STCW, OPITO, GWO training, and other delegated training related to current ships. Additionally, it aims to propose a curriculum model for IP training courses which consists of a total of 16 hours over 2 days, offered by the Korea Institute of Maritime and Fisheries Technology, including teaching objectives, duration, and course structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.4
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• pp.113-124
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• 1989

In this study, we tried to extract the land-use change information of Seoul city using the multiple date images of the same geographic area. Multiple date image set is MSS('72, '79, '81, '93) and TM('85), and we carried out geometric correction, digitizing(due to the administrative boundary) in pre-processing process. In addition, we performed land-use classification with MLC(Maximum Likelihood Classifier) after improving the predictive accuracy of classification by filtering technique. At the stage of classification, ground truth data, topographic maps, aerial photographs were used to select the training field and statistical data of that time were compared with the classification result to prove the accuracy. As a result, urban area in Seoul has been increased('72 : 25.8 %${\rightarrow}$'81 : 43.0 %${\rightarrow}$'85 : 51.9 %) and Forest area decreased ('72 : 39.0 %${\rightarrow}$'85 : 28.4 %) as we estimated. Finally, it is concluded that the utilzation of satellite imagery is very effective, economical and helpful in the urban land-use/land-cover monitoring.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.7 no.1
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• pp.109-121
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• 2000

Increasingly nursing science is embracing the concepts and methodology derived from psycho-neuroimmunology. It has been previously shown that stress increases and immune function declines in students undergoing examinations. To date, however, no many studies have been reported on stress levels, immune function and interventions in Korean students undergoing their first clinical nursing rotation. It was proposed that nursing students during their first clinical rotation experience increase in stress because of the novelty of the situation and their lack of clinical knowledge. It was also hypothesized that biofeedback and progressive relaxation, methods of self-regulation of involuntary autonomic nervous system responses, would reduce the stress response. The purpose of this study is to test the effectiveness of progressive muscle laxation using biofeedback The effectiveness of the experimental methods was tested by measuring the degree of symptoms of stress (SOS) and the values of ephinephrine, pulse rate, blood pressure and natural killer cells. The subjects of this study were thirty nursing students divided into two groups: experimental group was progressive muscle relaxation group using biofeedback and control group. This study was conducted for 8 weeks of clinical practice. Biofeedback training was done by software developed by J&J company (1-410 form for progressive muscle training). Progressive muscle relaxation training according to Jacobson's Theory was done by messaged word from biofeedback. The data was analyzed using Chronbach' ${\alpha}$ and t-test of the SPSS program and the significance level of statistics was 5%. The results of the study were : 1) The progressive muscle relaxation training using biofeedback was effective for the reduction of symptoms of stress(t=-4.248, p<.001) under clinical practice stress conditions. 2) The progressive muscle relaxation training using biofeedback was not effective for the values of epinephrine(t=-1.294, p=.206). 3) The progressive muscle relaxation training using biofeedback was effective for the reduction of systolic blood pressure (t=-2.757, p=.01). 4) The progressive muscle relaxation training using biofeedback was effective for the reduction of diastolic blood pressure (p=-2.032, 0=.05). 5) The progressive muscle relaxation training using biofeedback was not effective for the reduction of pulse rate(t=-15, p=.988). 6) The progressive muscle relaxation training using biofeedback was effective for the maintenance of natural killer cells (t=2.381, p=02). The first clinical rotation for student nurses is a stressful experience as seen by the rise in the SOS in the control group. Biofeedback using progressive muscle relaxation were effective in preventing the rise of symptoms of stress and the blood pressure means when comparing the pre to post clinical experience, The mean natural killer cell count was depressed in the control group but not significantly different in the experimental groups, It is proposed here that stress via the hypothalamic - pituitary - adrenal axis suppressed the NK cell count whereas the relaxation methods prevented the rise in stress and the resulting immune depression. We recommend relaxation techniques using biofeedback as a health promotion technique to reduce psychological stress. In summary. the progressive muscle relaxation training using biofeedback was effective for the reduction of symptoms of stress under clinical practice stress conditions.

• Journal of the Korea Safety Management & Science
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• v.11 no.4
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• pp.221-228
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• 2009

Electronic Product Code Information Services(EPCIS) is an EPCglobal standard for sharing EPC related information between trading partners. EPCIS provides a new important capability to improve efficiency, security, and visibility in the global supply chain. EPCIS data are classified into two categories, master data (static data) and event data (dynamic data). Master data are static and constant for objects, for example, the name and code of product and the manufacturer, etc. Event data refer to things that happen dynamically with the passing of time, for example, the date of manufacture, the period and the route of circulation, the date of storage in warehouse, etc. There are four kinds of event data which are Object Event data, Aggregation Event data, Quantity Event data, and Transaction Event data. This thesis we propose an event-based data model for EPC Information Service repository in RFID based integrated logistics center. This data model can reduce the data volume and handle well all kinds of entity relationships. From the point of aspect of data quantity, we propose a formula model that can explain how many EPCIS events data are created per one business activity. Using this formula model, we can estimate the size of EPCIS events data of RFID based integrated logistics center for a one day under the assumed scenario.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.