• Journal of Industrial Convergence
• /
• v.19 no.5
• /
• pp.47-59
• /
• 2021

This study explored the lifelong education field experience of adult college students. For this purpose, a qualitative case study was conducted with 5 adult college students from November 17, 2020 to May 14, 2021 through practical diaries, assignment analysis and in-depth interviews. As a result of the study, we discovered two things. First, the lifelong education field practice experience is a 'mountain to overcome! Lifelong education field practice', 'expansion of awareness of lifelong education', 'confidence field practice', and 'A picture is worth a thousand words'were revealed. Second, the meaning of the lifelong education field experience is 'formation of a learning community through teamwork with colleagues', 'realization that people are the core of the lifelong education', 'changes in the professional identity of the lifelong education history', and 'The life of a lifelong learning practitioner who wants to challenge' life' was revealed. These results will lead field training participants to grow qualitatively, and can be used as basic data for securing professional expertise and providing quality field training. A follow-up study was proposed for adult college students who belong to other colleges.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.199-208
• /
• 2021

Automatic Post Editing(APE) is the study that automatically correcting errors included in the machine translated sentences. The goal of APE task is to generate error correcting models that improve translation quality, regardless of the translation system. For training these models, source sentence, machine translation, and post edit, which is manually edited by human translator, are utilized. Especially in the recent APE research, multilingual pretrained language models are being adopted, prior to the training by APE data. This study deals with multilingual pretrained language models adopted to the latest APE researches, and the specific application method for each APE study. Furthermore, based on the current research trend, we propose future research directions utilizing translation model or mBART model.

• The Korean Journal of Emergency Medical Services
• /
• v.25 no.1
• /
• pp.223-234
• /
• 2021

Purpose: The purpose of the study is to check up the status of 119 emergency control centers usage. Therefore, the status of use of 119 emergency control centers and the incidence of pre-hospital cardiac arrest patients were investigated. Methods: The emergency activity daily reports and first aid diaries of 119 emergency control centers from January to December 2018 were reviewed. For more accurate status analysis, Among the first aid guidance received in the emergency rescue standard system, the cardiopulmonary resuscitation guide log was reviewed. Results: In 2018, the total usage of the 119 emergency control centers was 1,358,356 calls, hospital guidance werethe most commom (n=629,676, 46.4%), followed by first aid (n=428,027, 31.5%), disease consultation (n=170,238, 12.5%), medical oversight (n=111,188, 8.2%), and interhospital transfer (n=5,052, 0.4%). Regarding the user number per 1,000 persons, Jeju was the greatest at 48.0, whereas Changwon was the lowest at 13.0. A total number of dispatcher-assisted cardiopulmonary resuscitation was 12.181. The time from report to chest compression were 156.2±80.8 seconds for those with previous cardiopulmonary resuscitation training and 168.0±79.3 seconds for those without such training (p<.05). Conclusion: The ratio of first aid instructions, including dispatcher-assisted cardiopulmonary resuscitation, among total usage of the 119 emergency control centers increased. Therefore, additional efforts are required to improve the quality and expertise of information provided through the 119 emergency control centers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.147-157
• /
• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of the Korea Convergence Society
• /
• v.13 no.5
• /
• pp.103-112
• /
• 2022

As an alternative to improving the quality of life of the elderly and meeting the needs of high-level care, nursing services and needs, we aim to be the basis for formulating improvement proposals by applying the development of a fragrant healing-based fusion and complex social service professional training program. After conducting a questionnaire survey of 30 trainees in Guangzhou for 16 days from June 14, 2021 to September 23, 2021, and conducted a 7-day questionnaire survey, the education preference survey showed that in the educational preference, aromatherapy education was the highest surveyed at 42.11%, and the need for aromatherapy courses after the application of educational programs was the highest at 50%, and satisfaction was the highest at 48%. Accordingly, this study is used as a basis for the revitalization of the age-friendly industry and aims to contribute to the consolidation of senior care and nursing services and the securing of excellent personnel related to high-value-added fusion and complex social services.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.141-146
• /
• 2022

In modern educational practices, the issue of dependence on the experience of using multimedia by students and the adoption of technologies in education, the perception of their benefits and effectiveness in blended learning is little covered. The purpose of the academic paper lies in assessing the audiovisual context of multimedia technologies, its acceptance by students in practice on the example of using video lectures in blended learning. The methodology is based on an online survey of 120 students of Ukrainian universities who have assessed the experience level in using video lectures, as well as the constructs as follows: Technology Characteristics, Fit, Perceived Usefulness, Perceived Ease of Use, Attitude, Intention to Use, Actual Use. The results show that the majority of students use video lectures to a certain extent in their training (20,8% have used technology to a certain extent, 49,2% have often used technology in training, 20% are regular users of technology). It has been revealed that most students agree with the relevance of video lectures, the accuracy of lectures, the brevity of lectures, the clarity of lectures, as well as the high quality of lecture videos. It has been estimated that 42,5% believe that lecture videos are an effective tool towards supporting students in hybrid learning. 26,7% of students consider video lectures to be appropriate technologies for online / hybrid courses. In general, 37,5% of respondents find video lectures useful; however, 35,0% do not agree with this statement. 83,3% of students have rated the high level of ease of access to video. In total, 95% of students find lecture videos easy to use. In general, positive attitude of students to video lectures has been revealed.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.181-193
• /
• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.577-586
• /
• 2022

Detection and classification of steel surface defects are critical for product quality control in the steel industry. However, due to its low accuracy and slow speed, the traditional approach cannot be effectively used in a production line. The current, widely used algorithm (based on deep learning) has an accuracy problem, and there are still rooms for development. This paper proposes a method of steel surface defect detection combining EfficientNetV2 for image classification and YOLOv5 as an object detector. Shorter training time and high accuracy are advantages of this model. Firstly, the image input into EfficientNetV2 model classifies defect classes and predicts probability of having defects. If the probability of having a defect is less than 0.25, the algorithm directly recognizes that the sample has no defects. Otherwise, the samples are further input into YOLOv5 to accomplish the defect detection process on the metal surface. Experiments show that proposed model has good performance on the NEU dataset with an accuracy of 98.3%. Simultaneously, the average training speed is shorter than other models.

• Asian Journal of Innovation and Policy
• /
• v.12 no.1
• /
• pp.27-53
• /
• 2023

The purpose of this study is to examine the unique characteristics of the Korean academic system with regard to brain circulation, with a specific emphasis on the influence of overseas-trained academics on research activities within the Korean academic system. We have analyzed the statistical data on individual characteristics and performances of 48,499 Korean academics in science and engineering. We have examined the results at both the system and individual levels within the broader context of the macro characteristics of the Korean academic system. Our analysis reveals that the total number of domestically-trained academics exceeds the number of overseas-trained academics. However, in terms of research funding, overseas-trained academics tend to receive more funding than domestically-trained academics. Furthermore, after controlling other factors such as funding, personal attributes, and environmental factors, our analysis demonstrates that overseas training has a significant and favorable impact on the publication of internationally renowned journals. As such, the presence of overseas returnees has been essential for the effective functioning of the Korean academic system in the global research network and for conducting high-quality academic research. Therefore, the advantages of dependence on scientific core countries such as the US for overseas training have persisted. Nevertheless, upon scrutinizing the group of recently appointed 5,806 academics exclusively, we have discovered that junior academics who received their education domestically exhibit sufficient academic proficiency compared to their colleagues educated overseas. This observation highlights the potential for the Korean academic system to evolve into a self-sustaining system.

• Journal of Creative Information Culture
• /
• v.5 no.3
• /
• pp.255-264
• /
• 2019

In the previous study on the effectiveness of SW education, the educational goals were mainly focused on developing computational thinking ability and researching changes in computational thinking ability. But the word "computational thinking" is difficult to the public. Also the definition of computational thinking is varies from scholar to scholar. Therefore, this study aims to inform the public of the excellence and effectiveness of SW education through the change of "writing ability" which is more familiar to us. To achieve this goal, 10 lectures of SW training materials were produced and conducted in elementary science gifted class. As a result, significant changes have been made in the fluency and quality of writing skills, proving that SW training improves students' writing skills. Through this, we confirmed the visible results through SW education, which can be called justification of SW education. Furthermore, it is expected that various educational attempts will be developed using SW in other subjects.