• Steel and Composite Structures
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• v.53 no.1
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• pp.103-114
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• 2024

This study proposes a new hybrid approach that utilizes post-earthquake survey data and numerical analysis results from an evolving finite element routing model to capture vulnerability processes. In order to achieve cost-effective evaluation and optimization, this study introduced an online data evolution data platform. The proposed method consists of four stages: 1) development of diagnostic sensitivity curve; 2) determination of probability distribution parameters of throughput threshold through optimization; 3) update of distribution parameters using smart evolution method; 4) derivation of updated diffusion parameters. Produce a blending curve. The analytical curves were initially obtained based on a finite element model used to represent a similar RC building with an estimated (previous) capacity height in the damaged area. The previous data are updated based on the estimated empirical failure probabilities from the post-earthquake survey data, and the mixed sensitivity curve is constructed using the update (subsequent) that best describes the empirical failure probabilities. The results show that the earthquake rupture estimate is close to the empirical rupture probability and corresponds very accurately to the real engineering online practical analysis. The objectives of this paper are to obtain adequate, safe and affordable housing and basic services, promote inclusive and sustainable urbanization and participation, implement sustainable and disaster-resilient buildings, sustainable human settlement planning and management. Therefore, with the continuous development of artificial intelligence and management strategy, this goal is expected to be achieved in the near future.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.79-94
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• 2020

The purpose of this study is to analyze the difference in the effect of presence for the activation of virtual reality sports on participation satisfaction, to suggest continuous screen golf exercise participation, and to provide empirical and academic data for the development of the entire virtual reality sports market. To achieve this purpose, the survey period was from March 13 to May 13, 2020, with five researchers and assistants. The purpose of this study and the questionnaire were fully explained to consumers who experienced screen golf directly, and 247 questionnaires were used as the final valid sample by making a questionnaire with self-administration method. The data processing method was the statistical program Windows SPSS 18.0. First, factor analysis and reliability analysis, second, frequency analysis mean(M) and standard deviation(SD), third, Scheffe analysis among t-test and One-way ANOVA analysis, fourth, correlation analysis between variables and multiple regression analysis were conducted. The results of this study through these methods and procedures are as follows. First, there was a significant difference in participation satisfaction of presence in gender, and participation period of general characteristics. Second, there was a high difference in social presence, social self-reliance, and Ego, which are sub-factors of Presence, in social satisfaction, psychological satisfaction, and physical satisfaction. Third, the sub-factors of Presence, Social Presence, Social Self-Reliance, and Ego, were found to have a high effect on the sub-factors of Participation Satisfaction, Social Satisfaction, and Psychological Satisfaction.

• The Journal of Korean Medicine
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• v.41 no.2
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.20 no.1
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• pp.12-20
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• 2010

Metamictization and color change in zircons from Cambodia and Tanzania were investigated. Elements analysis to detect radioactivity of elements such as U and Th, and spectroscopic analysis using UV-VIS and Fourier transform infrared spectroscopy were performed. According to the UV-VIS spectroscopic analysis, it was perceived that many and high intense absorption peaks appeared in blue and colorless zircons, while less and low intense absorption peaks appeared in uranium contained green and yellow zircons. It was found that those stones have made progress to the metamictization. After heat treatment, we could detect opposite results. As the results of FTIR spectroscopy analysis, in the metamict green and yellow zircon, it is showed that 3-phonon combination mode bands of $[SiO_4]^{4-}$ internal vibration in the region of 3100~3400 $cm^{-1}$ are broad and some of them disappear. However, the disappeared bands are observed again due to restored crystal lattice by the heat treatment. Also, $U^{4+}$ peaks that can detect the uranium content in zircon appears at near 4800 $cm^{-1}$ in the green and yellow samples. From this investigation, we could observe the metamictization effect and color change in uranium-bearing zircon by heat treatment using spectroscopic analysis.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.73-99
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• 2014

Recently, big data have been studied ways to use in various fields. Big data refers to huge amounts of data that could not be addressed by conventional methods. Big data has attracted attention for improving accuracy of decision-making, forecasting in the near future, and creation of new business. In this study, it is an object to develop the utilization plan for big data in the field of technology commercialization. For this reason, we conducted study like case studies, literature review and focus group interview. We have derived the big data utilization plan based on this in the technology commercialization field. It, the data utilization plan, combines with the technology commercialization process of Jolly and it has five sub processes (Imagining, Incubating, Demonstrating, Promoting, Sustaining). In this paper, there is a significance that has emphasized the possibility for big data utilization in the technology commercialization. However, there is a limit to the general interpretation for our study. And we hope to contribute to the expansion of areas of technology commercialization information analysis through this research.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• 대한공업교육학회지
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• v.34 no.1
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• pp.252-273
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• 2009

Due to the importance of biotechnological literacy, the educational community in fields such as technology education, science education, and agricultural education has acknowledged the importance of biotechnology instruction for secondary school. Although recognized as a content organizer in the field of technology education, the actual teaching of biotechnology has not been broadly implemented in technology education classes. In the perspective of expectancy-value theory, technology teachers' motivation is the key factor for affecting the biotechnology instruction. This study investigates Korean technology teachers' motivational beliefs toward biotechnology and its instruction and their perceived ability and value toward biotechnology learning contents. To measure their motivational beliefs and attitudes, a composite on-line survey (fifteen motivational beliefs items, eight biotechnology content items, and related demographic items) was developed. Based on 114 Korean technology teachers' responses the researcher performed a descriptive analysis, independent t-test, and factor analyses (exploratory and confirmatory factor analysis using M-plus 5.0 and SPSS 16.0). Korean technology teachers' abilities toward eight biotechnology contents indicated lowscores while their values were relatively high. Through the independent sample t-test by two demographic variables (gender and professional development), this study found several significant differences in the perceived value. As a preliminary finding of exploratory factor analysis, fifteen items was separated into two motivational constructs of expectancy (6 items) and value (8 items). One item (item #6) was eliminated due to the cross loading. The final findings of this study may have significant implications for professional development regarding biotechnology and its instruction (both in-service and pre-service training) of technology teachers. Also, the confirmatory facctor analysis supported the preliminary finding. Finally, this study recommends that a validity test for other population, investigation for motivational sub-constructs, and in-depth investigation toward biotechnology instruction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.42-47
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• 2017

In this study,we conducted an injection molding analysis of metal powder materials for the development of flanges, which are necessary adapters for optical communication. The metal powder injection molding process is a technique for producing an injection molded article having a complicated shape by mixing ceramic or stainless powder and binders. It is used to produce products which require complex processing technology or for which the productivity is low. The purpose of this study is to minimize the manufacturing processing of products which are manufactured through existing mechanical processing procedures. For the injection molding analysis, we mixed stainless STS316 metal powder with binders at a ratio of 6 to 4 to make molding materials consisting of granular pellets. Then, three-dimensional modeling and meshing were carried out to obtain the optimal injection molding analysis conditions(molding temperature, melting temperature, injection time, injection temperature, injection pressure, packing time and cooling time). As a result of the analysis, it was discovered that the inlet became available 13.29 seconds after the first injection. Also, as the flowing and packing in the melt through the sprue, runner and gate were stable, it is expected that good molds can be manufactured.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.6
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• pp.975-986
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• 2010

College students represent a substantial market for fast food restaurant companies. In this sense, this research aims (1) to identify the attributes that influence college students' fast food restaurant selection, (2) to analyze how fast food restaurants perform with regards to those attributes using importance-performance analysis, and (3) to examine which attributes influence college students' satisfaction in a fast food restaurant context. Based on a literature review, 13 attributes that influence college students' fast food restaurant selection were derived. Then, using importance-performance analysis, it was found that among the 13 attributes, college students highly considered seven of them. Additionally, data analysis indicated that, currently, fast food companies perform well with regards to these seven attributes. More importantly, according to multiple regression analysis, among the seven attributes, value-related attributes (price, speed, location, and friendliness) were significantly related to college students' overall satisfaction.

• The Korean Journal of Food And Nutrition
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• v.36 no.3
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• pp.202-208
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• 2023

FT-IR, GC/MS, and ATR-FT-IR analyses were performed to confirm the physicochemical characteristics of saw palmetto fruit (SPF) extract. FT-IR analysis of the standard product showed that the band corresponding to the carbonyl bond of free fatty acid was stronger than the band of acyl-glyceride. Sample E was identified as having the same trend as the standard sample. Fatty acid composition analysis revealed that the main fatty acids in the standard sample were lauric acid and oleic acid. The content of lauric acid ranged from approximately 30% to 38% in samples B, C, D, and E, while the content of oleic acid ranged from approximately 29% to 34%. The GC/MS analysis confirmed that the standard SPF extract consisted of fatty acids and fatty acid ethyl esters. Sample E demonstrated a similar pattern to the standard samples in terms of oleic acid, lauric acid, and fatty acid esters. ATR-FT-IR analysis indicated that only sample E was predicted to contain 100% saw palmetto extract. Therefore, these study findings can be considered fundamental data for analyzing the physicochemical characteristics of the composition of SPF extract.