• Journal of Digital Contents Society
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• v.15 no.1
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• pp.45-52
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• 2014

All medical information system stakeholders and the environment exists. Medical information systems for development in these environments and non-functional requirements, functional requirements and quality goals are to be met. In order to achieve these goals in a variety of ways currently being made to develop information systems and various applications are emerging. However, the process of developing these health information systems meet the basic requirements and does not consider that from the point of view should not be separate. This study of the development of health information systems related to quality measurement indicators for the analysis software architectures, and medical information, information quality evaluation of service quality information associated indicators evaluation are offered. This way of associated indicators for the quality of the output sum and analyze the trends in software architecture u-Healthcare should be available for assessment. Quality score compared with pre-set goals for achievement and satisfaction levels of analysis further support the cause excerpt field use in analysis and improvement is possible.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.35-46
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• 2009

Lane extraction and lane departure warning algorithms using the image sensor attached in the vehicle are addressed. With the research about intelligent automobile, there have been many algorithms about lane recognition and lane departure warning system. However, since these algorithms require to detect 2 lanes, the high time complexity and the low recognition rate under various driving circumstances are critical problems. In this paper, we present a lane departure warning algorithm using single lane extraction and center point analysis that achieves the fast processing time and high detection rate. From the geometry between camera and objects, the region of interest (ROI) is determined and splitted into two parts. Hough transform detects the part of the lane. After the detected lane is restored to have a pre-determined size, lane departure is estimated by calculating the distance from the center point. On real driving environments, the presented algorithm is compared with previous algorithms. Experiment results support that the presented algorithm is fast and accurate.

• Korean Journal of Comparative Education
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• v.27 no.3
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• pp.1-22
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• 2017

This study aims to examine the continuity of national education between prewar and postwar Japan in the context of nationalism and citizenship education by considering the direction and process of educational reform which has been a turning point in Japanese education policy. It explores the limitations of educational reform at the normative level and institutional and procedural level. Meiji Japan needed to form a united group to support modernization while also cultivating obedient people who supported the emperor, and the modern education system played a major role in achieving this task. After Japan's defeat in World War II, the nation sought to change the framework of authoritarian nationalism inherent in Japanese traditional through educational reforms and achieve the goals of democratization and non-militarization. The postwar educational reform has transformed the educational structure, but democracy and peace orientation have not been rooted internally. Under the backdrop of the Cold War, the education returned to the inverse.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.8-15
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• 2019

Recently, the importance of pre-shipment quality inspection has been emphasized due to the increase of STB supply. In this paper, we propose a method to support automation of quality inspection through simultaneous multi-channel input of STB video signal. The proposed method extracts a fingerprint using the center scan line of the image after stable video shot boundary detection using CeLDP combining color information and LDP code and performs synchronization between input video channels. The proposed method shows stronger shot boundary detection performance than the conventional shot detection method. Through the experiments applied to the real environment, it is possible to secure reliability and real-time quality check for synchronization between multi-channel inputs required for STB quality inspection. Also, based on the proposed method, we intend to study a large-scale quality inspection method in the future and propose a more effective quality inspection system.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.103-111
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• 2018

The purpose of this study is to suggest change probability of vocational education and training and support of teaching-learning methods. For this study, we applied a flipped learning strategy of two learning modules in Convergence Technology Campus of public vocational education and training institute and had an operation class. As a result, student satisfaction of flipped learning is 4.0 on average. 56.1% of education-trainees were higher an engagement of flipped learning class than teacher-centered class and 56.1% of education-trainees were used more learning energy. Based on results, we suggested the necessity of pre-learning system for application of education and training teaching methods on the 4th industry occupation and strategies to enhance teaching and learning competency.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.73-80
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• 2022

In this study, prior studies of 280 small business owners in Korea over the past two decades were comprehensively analyzed through keyword network and LDA topic modeling analysis, and overall views and trends in academia were examined. As core keywords, "sales" and "protection," which conflict with each other but are essential for stable and sustainable growth were selected, and 7 topics (Topic 1: start-up, topic 2: digital, topic 3: tax system, topic 4: capability, topic 5: coexistence, topic 6: regulation, and topic 7: funding) were drawn up. Based on the results of the analysis, the need to improve digital maturity for the continued growth and development of small business owners was raised, and the response at the pan-ministerial level and the stability of the performance of functions that can survive even after the new administration to solve the economic damage problems facing small business owners were suggested. In addition, attention to the long-term, speed, detail, and direction of government support in a new way, and a flexible approach to the negative way in which pre-allowance and post-regulation is given were suggested.

• Journal of Korean Critical Care Nursing
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• v.15 no.3
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• pp.101-114
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• 2022

Purpose : This study aimed to identify post-traumatic stress disorder (PTSD) and burnout experienced by healthcare providers who cared for patients with Coronavirus Disease 2019 (COVID-19) and their influencing factors. Methods : Data were collected from 135 healthcare providers who cared for patients with COVID-19 in a tertiary general hospital from June 8 to September 2, 2021, using a questionnaire. Descriptive statistics, Mann-Whitney U test, Kruskal-Wallis test, t-test, ANOVA and Scheffe's test, Pearson's correlation coefficients, and multiple regression were used for analysis using SPSS/WIN 27.0. Results : Participants' average PTSD score was 9.31 ± 11.80, and 8.9% were in the high-risk group. Participants' average burnout score was 51.77±21.28, and 62.2% were at high risk. PTSD scores differed significantly according to participants' age, education, job, position, and current workplace. Burnout scores differed significantly according to their age, gender, marital status, parental status, and education. There was positive correlation between participants' PTSD and burnout. The factors influencing participants' PTSD were term of self-isolation and age (R²=.09). There were no significant influencing factors on participants' burnout. Conclusion : This study reconfirmed that healthcare providers who cared for patients with COVID-19 experienced both PTSD and burnout, suggesting that interventions are needed such as regular pre-training or simulation training and establishing a support system.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.320-329
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• 2022

The purpose of this study was to identify factors influencing intention to stay among new graduate nurses. The participants were 127 new graduate nurses working at A University hospital in Gyeonggi Province. Data were analyzed by descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation coefficient, and multiple regression. The results showed that job satisfaction, reality shock, working period, and resilience were significant predictors of intention to stay and explained for 44.2% of the variance in intention to stay. The findings of this study suggest that interventions focusing on reducing the reality shock through pre-experiences or trainings in clinical situations and enhancing the resilience are needed to improve intention to stay for the new graduate nurses. Also it is necessary to establish a support system and work environment to improve nursing job satisfaction, and a long-term education program of more than 6 months is needed to help new nurses adapt to work.