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This paper proposes a novel mechanism called Event-driven Uncore Frequency Scaler (eUFS) to improve the energy efficiency of the HPC systems. UFS exploits the hardware events such as LAPI (Last-level Cache Accesses Per Instructions) and CPI (Clock Cycles Per Instruction) to dynamically adjusts the uncore frequency. Hardware events are collected at a reference time period, and the target uncore frequency is determined using the collected event and the previous uncore frequency. Experiments with the NPB benchmarks demonstrate that the eUFS reduces the energy consumption by 6% on average for class C and D NPB benchmarks while it only increases the execution time by 2% on average.

In this paper, we developed an artificial intelligence literacy education program to strengthen the AI convergence education capacity and cultivate literacy of in-service elementary and secondary teachers, and verify the effect on the degree of interest in artificial intelligence convergence education by applying it. As a test tool, the level of interest questionnaire scale developed by George, Hall & Stiegelbauer(2006) was used based on the center of interest acceptance model of Hall et al.(1979). As a result of analyzing the degree of interest in artificial intelligence convergence education before and after the application of the artificial intelligence literacy education program, the types of non-users were found both before and after the application of the program, but the overall degree of interest increased compared to before application. As a result of analyzing the satisfaction result of the artificial intelligence literacy education program, a response that was satisfied in most areas was derived, but there was a tendency to be somewhat less satisfied with the case of convergence and application of artificial intelligence and industry.

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.

In this paper, we propose a method to effectively search by automatically classifying scenes in which specific images such as pitching or swing appear in baseball game images using a CNN(Convolution Neural Network) model. In addition, we propose a video scene search system that links the classification results of specific motions and game records. In order to test the efficiency of the proposed system, an experiment was conducted to classify the Korean professional baseball game videos from 2018 to 2019 by specific scenes. In an experiment to classify pitching scenes in baseball game images, the accuracy was about 90% for each game. And in the video scene search experiment linking the game record by extracting the scoreboard included in the game video, the accuracy was about 80% for each game. It is expected that the results of this study can be used effectively to establish strategies for improving performance by systematically analyzing past game images in Korean professional baseball games.

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

Given a document, keyphrase extraction is to automatically extract words or phrases which topically represent the content of the document. In unsupervised keyphrase extraction approaches, candidate words or phrases are first extracted from the input document, and scores are calculated for keyphrase candidates, and final keyphrases are selected based on the scores. Regarding the computation of the scores of candidates in unsupervised keyphrase extraction, this study proposes a method of adjusting the scores of keyphrase candidates according to the types of keyphrase candidates: word-type or phrase-type. For this, type-token ratios of word-type and phrase-type candidates as well as information content of high-frequency word-type and phrase-type candidates are collected from the input document, and those values are employed in adjusting the scores of keyphrase candidates. In experiments using four keyphrase extraction evaluation datasets which were constructed for full-text articles in English, the proposed method performed better than a baseline method and comparison methods in three datasets.

Since social big data often includes new words or proper nouns, statistical morphological analysis methods have been widely used to process them properly which are based on the frequency of occurrence of each word. However, these methods do not properly recognize compound nouns, and thus have a problem in that the accuracy of keyword extraction is lowered. This paper presents a method to extract compound nouns in keyword analysis of social big data. The proposed method creates a candidate group of compound nouns by combining the words obtained through the morphological analysis step, and extracts compound nouns by examining their frequency of appearance in a given review. Two algorithms have been proposed according to the method of constructing the candidate group, and the performance of each algorithm is expressed and compared with formulas. The comparison result is verified through experiments on real data collected online, where the results also show that the proposed method is suitable for real-time processing.

This study investigated differences in learning styles and learning strategies according to learning factors: major fields, achievements, and grades and differences in learning strategies according to learning styles for college students. Unlike previous studies that analyzed differences focused on the dominant learning style, the learning style was subdivided into a balanced and dominant learning style. In the analysis of the 179 participants in M colleges, it was found that the difference between the learning style and the learning strategy according to the learning factors was not significant. But, there was a significant difference in the use of cognitive strategies according to the learning style in the dimension of information input, and in the use of all strategies according to the information processing style. It was analyzed that active learners had a high level of using cognitive strategies, visual learners had a high level of using external strategies, and balanced learners had a high level of using internal strategies. Based on the results, the training strategies to understand the learning style and to improve the level of use of the learning strategy in the learning competency improvement program was proposed.

The purpose of this study was to investigate the effects of aqua exercise program on body composition, blood components, and physical fitness in the elderly women. This study included 23 elderly women. The aqua exercise program was performed for fifty minutes a day, 2 times a week for 12 weeks with moderate-intensity level. Data was analyzed with SPSS using descriptive statistics and the paired t-test. The findings of this study presented that the aqua exercise program showed significant improvements in the body mass index (t=2.42, p=.024), the abdominal circumference (t=4.36, p〈.001), and the level of blood glucose (t=3.19, p=.004) and LDL cholesterol (t=3.33, p=.003). These results suggest that aqua exercise is an effective strategy which has positive effects on body composition and blood components among elderly women. It might be possible to promote health condition in elderly women by implementing aqua exercise program which could be considered aging and excercise intensity.

In this paper, effects on industrial accident prevention based on better safety and health environment by utilizing ergonomics expert were studied. This study was mainly based on the data from ‘the survey for occupational safety and health trend’ conducted by Occupational Safety and Health Research Institute. The number of industries participated in the survey was 2,084. Main results of the study were as follows; 1) Only 22.9% of the industries participated in the survey utilized ergonomics expert. The rest of the industries have never had an ergonomics expert due to the reasons such as lack of knowledge for the field of ergonomics, etc. 2) Specific activities done by the industries with ergonomics expert in order to have better safety & health were ‘providing work orders’(94.8%), ‘providing monitoring guidelines’(85.5%), ‘providing information for dangerous work’(95.8%), ‘providing safety education’(96.6%), and ‘other safety management’(94.1%). 3) When the odds ratio for the levels of communication and the levels of environmental stability regarding safety & health for the different groups(with experts and without experts), it was found that the group with ergonomics experts had a significant higher ORs(2.391, 95% confidence interval(1.949-2.932) and 2.128, 95% confidence interval(1.786-2.537)) respectively than those of the industries without ergonomics expert. The results suggested that ergonomics expert has been unique in most of time in terms of his/her contributions in the field of industrial safety and health.

This paper intends to draw implications for preparing for Post-Corona in the health field and policy fields as the global pandemic is experienced due to COVID-19. The purpose of this study is to analyze the news and trends of media companies through temporal analysis of the three infectious diseases, SARS-CoV, MERS-CoV, and SARS-CoV-2 (COVID-19), in which the domestic infectious disease preventive system was active throughout the first year of the outbreak. To this end, by using the news analysis program of the Korea Press Foundation ‘Big Kinds’, the number of news articles per year was digitized based on the period when each infectious disease had an impact on Korea, and major trends were implemented and analyzed in a word cloud. As a result of the analysis, the number of articles related to infectious diseases peaked when the World Health Organization (WHO) declared a warning and (suspicious) confirmed cases occurred. According to keyword and word cloud analysis, ‘infectious disease outbreak and major epidemic areas’, ‘prevention authorities’, and ‘disease information and confirmed patient information’ were found to be the main common features, and differences were derived from the three infectious diseases. In addition, the current status of the infodemic was identified by performing word cloud analysis on information in uncertainty. The results of this study are significant in that they were able to derive the roles of the health authorities and the media that should be preceded in the event of a new disease epidemic through previously experienced infectious diseases, and areas to be rearranged.

Gallbladder polyps (GBPs) may be a cause of gallbladder cancer. The known risk factors for GBPs are male gender, obesity, metabolic syndrome, and dyslipidemia. Especially, male gender has been known that it strongly affects on the prevalence of GBPs. Therefore, this study aimed to investigate risk factors affecting the prevalence of GBPs among adults on Jeju Island according to genders. We examined 5,574 subjects who visited a single health-screening center at Jeju National University Hospital between January 2015 and December 2019. Univariate and multiple logistic regression analysis were performed to identify risk factors affecting the prevalence of GBPs. The prevalence of GBPs were 8.9% in male subjects and 8.1% in female subjects, respectively. Multivariate analysis revealed that ≥60 year age (odds ratio [OR] 0.659; P=0.027) and aspartate aminotransferase >32 IU/L (OR 0.658; P=0.009) in male subjects and high-density lipoprotein-cholesterol ≥60 mg/dL (OR 0.514; P=0.013) in female subjects were independent factors affecting the prevalence of GBPs. The prevalence of GBPs in men (8.9%) is comparable to that in women (8.1%) on Jeju Island. Age <60 years and the higher level of aspartate aminotransferase in men and the normal or lower high-density lipoprotein-cholesterol in women were independent risk factors of GBPs on Jeju Island.

This study aimed to develop a new Hanbok Jeogori prototype based on design extracted through a preference survey on new Hanbok Jeogori. This study was conducted based on K-patterns selected as excellent patterns in prior research and utilized the SPSS 27.0 program for analysis of appearance evaluation. As a result of the first appearance evaluation and garment pressure evaluation, the shoulder area was required to be modified, so the shoulder end point was moved within 1cm to rearrange the armhole line. Modification of sleeve length and circumference was also required, reducing sleeve length by 1.5cm and sleeve circumference by 0.5cm at both ends. As a result of the second evaluation, the length of Jeogori, hem circumference, and sleeve length were required, reducing the length of the front and back Jeogori by 1 cm and reducing the sleeve length by 1cm. The hem circumference was reduced by 0.2cm each in the princess line, which was highly evaluated in the final evaluation. In future research, it is thought that pattern studies by material, length, and age should be conducted and corrections should be derived through actual wear experiments.

This research studies the perception types of 119 rescue workers among emergency rescue department students, and was carried out to identify the types of perception of 119 rescue workers among firefighters and to prepare basic data to find out the characteristics of each type. As a result of analysis on the Q sample consisting of 27 statements by executing the Q UANL program on a total of 54 students from the Emergency rescue department, it is confirmed that there were 3 types, which accounted for 45% of the total variable. When looking at the explanatory power per type, it turned out: 32% for Type I; 6.7% for Type II; and 5.8% for Type III. Each type was named as follows: our Superman for Type I ; suffering hero for Type II ; and rescue expert for Type III. Overall, there were 119 rescue workers as follows : rescue workers in lexical meaning; and 119 rescue workers who were in difficult situations suffering from post-traumatic stress disorder and needed to be covered and protected by citizens. In addition, there was a perception of 119 rescue workers who were recognized as a specialist and carry out his/her lifesaving duties without a single mistake. Therefore, in order for 119 rescue workers to be recognized as a specialized field of rescue, a program in which 119 rescue workers can share various training and experiences must be provided and researched.

In this study, we proposed the possibility of reliability analysis of risk index by using Monte Carlo simulation as basic data of safety accident occurrence data and expert opinion by construction industry type. Through this, it is expected that risk index for safety accidents according to detailed types of works will be presented stochastically and it will be possible to predict the risk factors and the expected range of damage based on the reliability analysis in the construction safety management plan. It will also reduce many of the planning risks that are common to decision makers in the field of construction management. In identifying risks, road bridge construction was classified into earthworks, drainage works, and bridge construction, and possible safety accidents were classified based on expert data. The risk index was calculated for each detailed construction of road and bridge construction, drainage construction, and bridge construction.

This study was conducted to confirm the influence of interpersonal disposition and emotional intelligence on interpersonal competence of allied health college students. For the research, a questionaire survey was conducted for students enrolled in the Department of Nursing and Physical Therapy from November 29 to December 16, 2019 and a total of 275 data were collected. Data were analyzed by t-test, ANOVA, correlation analysis, and multiple regression analysis using SPSS program. As results of the study, the average of interpersonal competence was 4.10 and that of thoughtfulness-unconsideration was the highest among the eight interpersonal types and the average emotional intelligence was 2.73. The interpersonal dispositions that had a significant effect on interpersonal competence were initiative-congruence and aggression-non aggression and their explanatory power was 29.4% and the most influential interpersonal disposition was initiative-congruence. Emotional intelligence that had a significant effect on interpersonal competence were self-emotional expression, control of others and motivation and their explanatory power was 24.0% and the most influential emotional intelligence was motivation.

Regression analysis is a well-known statistical technique useful to explain the relationship between response variable and predictor variables. In particular, Researchers are interested in comparing the regression coefficients(intercepts and slopes) of the models in two independent populations. The Chow test, proposed by Gregory Chow, is one of the most commonly used methods for comparing regression models and for testing the presence of a structural break in linear models. In this study, we propose the use of permutation method and compare it with Chow test analysis for testing the equality of two independent linear regression models. Then simulation study is conducted to examine the powers of permutation test and Chow test.

The purpose of this meta-analysis was to determine the efficacy of proprioceptive neuromuscular facilitation training on Gait. We included all randomized controlled trials assessing the efficacy of proprioceptive neuromuscular facilitation training on gait ability control in patients after stroke. This study was conducted according to the PRISMA guideline. CINAHL, PubMed and RISS were searched for studies published up to December 2020, and all randomized controlled trails assessing PNF intervention were included. This analysis included only randomized controlled trials. A total of 12 studies were selected from 1475 records obtained from the databases. The meta-analysis was performed using the R software. The overall intervention effect was moderate (standardized mean difference (SMD): 0.67, 95% confidence interval (CI): 0.50-0.84). Additionally, Gait rite (SMD: 0.69), 10m walking test (SMD: 0.59) had medium effect sizes. These findings indicate that PNF is an effective intervention for improving gait ability in patients stroke.

The purpose of the present study was to investigate the relationship between perceived parenting style and life satisfaction among adolescents transitioning from middle to high schools and to ascertain whether self-esteem functions as a mediating variable between the two aforementioned variables. Based on the results of literature review, a research model, in which there were perceived parenting style, self-esteem, and life satisfaction as latent variables, was designed. The current study analyzed the 6th and 7th wave data of the 4th grade panel of elementary school from the Korea Children and Youth Panel Survey (KCYPS). Major results were as follows. First, perceived democratic parenting style had a significant positive effect on life satisfaction whereas controlling parenting style did not have a significant impact on life satisfaction. Second, self-esteem partially mediated the effect of perceived democratic parenting style on life satisfaction. Third, self-esteem fully mediated the effect of perceived controlling parenting style on life satisfaction. Finally, implications and recommendations were provided to improve parenting style and to enhance self-esteem of adolescents experiencing school transitions.

Road fine dust is not simply a problem of air pollution, but is threatening the health and life of the public, including pedestrians using sidewalks. There are various policy proposals related to road fine dust reduction, but the actual effect cannot be guaranteed. Therefore, in order to ensure the safe and pleasant passage of the people, the pavement pavement (sidewalk pavement) for fine dust management on the road should be designed by considering the location, use, function, etc., and appropriate paving materials and construction methods should be selected and designed or sidewalk facilities should be prepared. Therefore, this study examines the concept and legal basis of sidewalk pavement, reviews the status of sidewalk pavement-related technologies that purify air pollution, and utilizes sidewalk pavement materials under the revised sidewalk pavement guidelines for reducing fine dust, 「Act on Promotion of Purchasing of Green Products」 We intend to establish a legal basis for early application of sidewalk pavement technology by proposing measures and amendments to local government sidewalk pavement management rules.

This study uses data from the 11th year(2016) to the 15th year(2020) of the Korea Welfare Panel(KOWEPS) for the five years that the survey was completed to predict the life satisfaction of each individual by generation, centered on single-person households. This is a longitudinal analysis using a latent growth model. The generational division was based on the turning point of a major event experienced by the cohort that brought about a decisive change in the socialization process, historical event, and technological development as factors. The analysis data used in this study were of 27,447 persons in the 15th year of the Korea Welfare Panel(2020), 2,778 persons who were single-person households even in the first year from the last 5 years, and 1,448 persons who were single-person households continuously for 5 years. Excluding those born in 1922 to 1944(843 persons), 605 persons born between 1945 and 1996 were included. As a result of the study, Generation Y(millennials) than Generation X(new generation) and Generation X generation than Baby Boomers showed higher overall life satisfaction. Since overall life satisfaction by generation does not have an effect on the rate of change, it was found that this difference remains the same for all generations.

Relationship between bone mineral density (BMD) and type 2 diabetes is still inconsistent. Recently, many epidemiologic data show that fracture risk is increased in type 2 diabetic patients regardless of BMD status. In this study, we used nation-wide data from 2008 to 2011 Korea National Health and Nutrition Examination Survey (KNHANES) to analyze the BMD status in patients with type 2 diabetes compared to non-diabetics. We included subjects aged 50 years or older in men (N=2,959, 2,430 without diabetes, 529 with type 2 diabetes) and postmenopausal women (N=2,902, 2,479 without diabetes, 423 with type 2 diabetes). Subjects with history of medication for osteoporosis or with illness or malignancy affecting bone metabolism were excluded. Data of anthropometric measurements and demographic characteristics were collected by trained examiner. Serum was separated from peripheral venous blood samples obtained after 8 hours of fasting. BMD was measured at lumbar spine and femur using dual-energy X-ray absorptiometry (DXA). There was a significant positive association between lumbar spine BMD and type 2 diabetes after adjusting age, gender, body mass index, monthly house income, education level, physical activity, daily calcium intake and vitamin D concentration by multiple regression analysis in all subjects. In the subgroup analysis by gender, this association was maintained both in male and female after adjusting those confounding factors. However, femur BMD was not different between type 2 diabetic and non-diabetic subjects. In conclusion, lumbar spine BMD was significantly higher in type 2 diabetic patients aged 50 years or more in men and postmenopausal women compared to non-diabetic subjects.

The effect of body composition such as lean mass and fat mass on bone mineral density (BMD) is complex and still controversial. In this study, we investigated the relationship between body composition and bone mineral density using nation-wide data from 2008 to 2011 Korea National Health and Nutrition Examination Survey (KNHANES) in 2,139 men and 2,193 postmenopausal women aged 50 years or older. Subjects with history of medication for osteoporosis or with diseases or malignancy affecting bone metabolism were excluded. Data of anthropometric measurements and demographic characteristics were collected by trained examiner. Fasting blood sample was obtained for blood chemistry analysis. BMD of the lumbar spine, total femur, and femoral neck, and body composition such as total lean mass (TLM), total fat mass (TFM), truncal fat mass (TrFM) were measured using dual-energy X-ray absorptiometry (DXA). There were significant positive correlations between body composition indices such as lean mass and fat mass with BMD. In multiple regression analysis, TLM was positively associated with BMD after adjusting age, body mass index, monthly house income, education level, physical activity, daily calcium intake and vitamin D concentration in both men and postmenopausal women. BMD at lumbar spine and femur in lowest quartile of TLM was significantly lower than other quartiles after adjusting those confounding factors in both gender. TrFM was negatively associated with total femur BMD in male and femur neck BMD in postmenopausal women after adjusting confounding factors. In conclusion, TLM is very important factor in maintaining BMD in subjects aged 50 years or older in men and postmenopausal women.

Since there are almost no scales for measuring the size of We-Chat applets, and most of the existing We-Chat applets are grafted through the original APP application, At present, the application scope of We-chat applets which is mainly in /shopping/life/food application. Thus, the purpose of this research is to focus on the iPhone app store, collect data on the top five of APP-STORE through users' comments and The high-frequency words will be obtained for statistics, and the variables of this study will be set up. Last, develop relevant Empirical research on the size and measurement scale of the We-Chat applet. Therefore, how to use We-Chat applets to improve user experience, we can create their own user private domain traffic for We-Chat applets and achieve long-term market competitiveness.

This paper aims to study the using motivation and influencing factors of Chinese users’ participation in live stream shopping through theoretical and empirical analysis, so as to evaluate the change in users’ needs and improve marketing strategies. In doing so, I conducted a questionnaire survey for Chinese live stream shopping users and collected the required data. For empirical analysis, I used SPSS and AMOS software to carry out descriptive analysis, reliability and validity analysis and structural equation model analysis (SEM) to test the hypothesis. The results of the analysis showed that core competency and brand personality of the short video industry have a significant impact on user and customer perceived value, thus influencing users’ using motivation. That is, users do not blindly follow live stream shopping but make their active choice. Therefore, it is suggested that live stream shopping platform should strengthen the e-commerce attributes and eradicate “the sense of false satisfaction”, in order to achieve the effective communication of information. On the other hand, to stimulate the purchase motivation of users, the brand should build up its personality, and enhance user and customer perceived value in cyber marketing.