• Journal of Korea Entertainment Industry Association
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• v.14 no.1
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• pp.137-147
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• 2020

This study is aimed to develop and validate the clinical competence scale of occupational therapy student. The development of clinical competence scale analyzed the definition of clinical performance and previous studies. preliminary examinations were conducted on 203 occupational therapy departments in 3rd and 4th grade to verify item analysis and job validity. After exploratory factor analysis, eight factors of professional consciousness, 11 items of occupational therapy evaluation factors, 4 items of occupational therapy intervention factors, and 4 items of communication factors were extracted into a total of 27 factors. As a result of verifying the reliability of each factor through the internal consistency coefficient Cronbach's α, it was found to be .87~.94 and the overall reliability was .96. The correlation between the total score and the factors of the clinical competence scale was statistically significant. Through the confirmatory factor analysis, the model fit test of the factor structure for 27 items of 4 factors (χ²=.76, df = .31, CFI = .81, TLI = .80, RMSEA = .79) is considered acceptable. Through this study, The clinical competence scale is a valid and reliable scale that can be useful for objectively assessing.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.224-236
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• 2022

This study tests the hypotheses that city slogan reduces the retrieval of city memory unrelated to the slogan from the long term memory and that some variables moderate this effect, using the experimental method. The theoretical basis for the hypotheses is from the structure of the long term memory and the principle of memory retrieval discussed in ANM(Associative Network Model). For the test of hypotheses, the study adopted 4 experimental groups (2(slogan relevance: high or low) * 2(slogan concreteness: high or low)) and 1 control group. Each experimental group was exposed to one slogan corresponding to its condition while the control group was not. Then, the recall score was compared among experimental and control groups. One hundred and seventy-four undergraduate students belonging to the college of the authors participated in the study. The sample group was between 18 and 27 years of age, with an average of 22.4 years, and 54 percent comprised males. Results showed that city slogan had a negative effect on the retrieval of city memory unrelated to the slogan in most experimental conditions. This effect was more evident when the slogan had high relevance or high concreteness. But the main effect did not appear when the slogan had low relevance and low concreteness.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.185-191
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• 2021

The purpose of this study is to develop middle school English classes using Problem-Based Learning(PBL) based on flipped learning and to examine its effects. Recently, various attempts to combine flipped learning and PBL have been made; however, many studies have not been applied to middle and high school curriculums yet. The attempt of this study is expected to have theoretical and practical significance. The instructional model was derived from the review of previous studies, and the development of instructional program followed the general design procedure(analysis-design-development-implement-evaluation), and its validity was secured with the advice of related experts. To verify the effectiveness of the program, the English academic achievement test and the English key competency test were conducted before and after the program. Changes in English academic achievement were analyzed by the paired-sample t-test, and the effect of key competency and the level of achievement test performance (high vs, low) on the pre-post score change was analyzed by the mixed effects repeated measures ANOVA. As a result of the analysis, both academic achievement and key competencies increased, and the low-level students in the pre-academic achievement test showed more improvements. In conclusion, the PBL class based on flipped learning is effective in improving the English academic achievement and key competencies of middle school students, and in particular, it is shown to be an effective teaching method for students with low academic achievement.

• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.539-563
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• 2022

Nine pig farms were evaluated for the welfare quality in Korea using animal- and environment-based parameters (particularly air quality parameters) during the winter of 2013. The Welfare Quality^® (WQ^®) protocol consists of 12 criteria within four principles. The WQ^® protocol classifies farms into four categories ranging from 'excellent' to 'not classified'. Each of these criteria has specific measures for calculating scores. Calculations for the welfare scores were conducted online using the calculation model in the WQ^® protocol. Environment-based parameters like microclimate (i.e., temperature, relative humidity, air speed, and particulate matter), bacteria (total airborne bacteria, airborne total coliform, and airborne total Escherichia coli), concentration of gases (carbon dioxide, ammonia, and hydrogen sulfide) were measured to investigate the relationship between animal- and environment-based parameters. Correlations between the results of animal- and environment-based parameters were estimated using spearman correlation coefficient. The overall assessments found that five out of nine farms were 'acceptable', and four farms were 'enhanced'; no farm was 'not classified'. The average score for the four principles across the nine farms, in decreasing order, were 'good feeding' (63.13 points) > 'good housing' (59.26 points) > 'good health' (33.47 points) > 'appropriate behaviors' (25.48 points). In the result of the environment aspect, the relative humidity of farms 2 (93.4%), 3 (100%), and 9 (98%) was much higher than the recommended maximum relative humidity of 80%, and four out of the nine farms had ammonia concentrations greater than 40 ppm. Ammonia had negative correlations with 'positive social behaviors' and positive emotional states: content, enjoying, sociable, playful, lively, happy and it had positive correlations with negative emotional states: aimless, distressed. The concentration of carbon dioxide had negative correlations with positive emotional states; calm, sociable, playful, happy and it had a positive correlation with negative emotional state; aimless. Our results indicate that the control of the environment for growing pigs can help improve their welfare, particularly via good air quality (carbon dioxide, ammonia, hydrogen sulfide).

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.4
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• pp.196-203
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• 2022

Purpose: This study aimed to evaluate the accuracy and clinical usability of an identification model using deep learning for 79 dental implant types. Materials and Methods: A total of 45396 implant fixture images were collected through panoramic radiographs of patients who received implant treatment from 2001 to 2020 at 30 dental clinics. The collected implant images were 79 types from 18 manufacturers. EfficientNet and Meta Pseudo Labels algorithms were used. For EfficientNet, EfficientNet-B0 and EfficientNet-B4 were used as submodels. For Meta Pseudo Labels, two models were applied according to the widen factor. Top 1 accuracy was measured for EfficientNet and top 1 and top 5 accuracy for Meta Pseudo Labels were measured. Results: EfficientNet-B0 and EfficientNet-B4 showed top 1 accuracy of 89.4. Meta Pseudo Labels 1 showed top 1 accuracy of 87.96, and Meta pseudo labels 2 with increased widen factor showed 88.35. In Top5 Accuracy, the score of Meta Pseudo Labels 1 was 97.90, which was 0.11% higher than 97.79 of Meta Pseudo Labels 2. Conclusion: All four deep learning algorithms used for implant identification in this study showed close to 90% accuracy. In order to increase the clinical applicability of deep learning for implant identification, it will be necessary to collect a wider amount of data and develop a fine-tuned algorithm for implant identification.

• Journal of Industrial Convergence
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• v.21 no.6
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• pp.61-71
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• 2023

The objective of this research is to examine the mediating roles of nursing professional values and occupational stress in the relationship between self-leadership and patient-centered nursing among nurses employed at COVID-19 designated hospitals. This study were 160 nurses at a COVID-19 designated hospitals, and the data were collected from January 10 to February 30, 2022. The collected data were analyzed by independent t-test, one-way ANOVA, correlation analysis, multiple linear regression analysis, and SPSS PROCESS Macro model No 4 bootstrapping method. The average score for self-leadership was 61.3±8.55, nursing professional values was 95.5±11.66, occupational stress was 51.3±4.76, and patient-centered nursing was 59.3±7.63. The mediating effect of nursing professional values and occupational stress was confirmed in the influence relationship between self-leadership and patient-centered nursing of nurses at COVID-19 designated hospitals. This result suggests that the content related to improve nursing professional values and reduce occupational stress should be considered when applying the patient-centered nursing enhancement program.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.391-398
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• 2022

The aim of this study was to identify the factors affecting the mental health of high school students. The participants comprised 216 students in general high school. Data collection was conducted from May 1, 2020 to May 20, 2020. The data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient and a multiple regression analysis. The average score for self-esteem was 3.75±0.64(1-5), perceived stress was 2.86±0.58(1-5), emotional regulation ability was 3.43±0.65(1-5) and mental health was 1.91±0.71(1-5). Mental health had a statistically significant relationship with self-esteem(r=-.64, p<.001), emotional regulation ability(r=-.61, p<.001) and perceived stress(r=.54, p<.001). The factors affecting mental health were self-esteem(β=.46, p<.001), emotional regulation ability(β=-.37, p<.001), negative perceived stress(β=.17, p=.001) ; the explanatory power of the model was 60.0%. Therefore, it will be necessary to develop a program that can help high school students improve their self-esteem and control their negative emotions in order to promote their mental health.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.