• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.616-625
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• 2011

Ensuring the designed prestress force is very important for the safety of prestressed concrete bridge. The loss of prestress force in tendon could significantly reduce load carrying capacity of the structure. In this study, an automated prestress-loss monitoring system for prestressed concrete girder using PZT-interface and wireless impedance sensor node is presented. The following approaches are carried out to achieve the objective. Firstly, wireless impedance sensor nodes are designed for automated impedance-based monitoring technique. The sensor node is mounted on the high-performance Imote2 sensor platform to fulfill high operating speed, low power requirement and large storage memory. Secondly, a smart PZT-interface designed for monitoring prestress force is described. A linear regression model is established to predict prestress-loss. Finally, a system of the PZT-interface interacted with the wireless sensor node is evaluated from a lab-scale tendon-anchorage connection of a prestressed concrete girder.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.199-206
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• 2010

In this era of globally competitive world market, many business firms have been using ISO 9000 quality management system, whose principles are leadership, customer focus, people involvement, etc., which are expected and desired to be attained for the various long/short term goals of the business firms. This paper studies whether and how those principles are implemented in the areas of service, manufacturing, etc., and researches the regression relations between business firm's performance and those principles. Using hypothesis testing, we found and showed results which can be referenced for the higher performance of business firms and those can be added to the pool of knowledge about the ISO principles implementation in service and manufacturing industries.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.2
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• pp.13-22
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• 2012

This research attempts to analyze communication effects of print ad having pictorial typography. 150 Questionnaires were distributed to respondents staying Daejeun City and 148 copies were retreated for five days from April 22nd to 26th, 2012. Frequency analysis, factor analysis, Cronbach's alpha for reliability analysis were utilized for data analysis with SPSS 12.0. For testing hypothesis, regression analysis was used. As result of testing hypothesis, 'informative, beneficial, creative, reliable' were partially significant to attitude towards print ad having pictorial typography. That means 'creative' and 'reliable' were insignificant, while 'informative' and 'beneficial' are significant. Variable of the most influencing on attitude towards advertising is 'informative.' 'Informative, beneficial, creative, and reliable' were partially significant to brand attitude, too. That means 'beneficial' and 'creative' were insignificant, while 'informative' and 'reliable' were significant. Variable of the most influencing on brand attitude was 'reliable.' Therefore, to enhance communication effect of print ad having pictorial typography, 'informative' and 'reliable' are most significant variables.

• International Journal of Contents
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• v.15 no.1
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• pp.1-9
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• 2019

Since the VR market is expected to have a high growth, this study aimed to investigate the human factor-related determinants of user satisfaction with mobile VR headsets. A pre-study of customer reviews was conducted with the help of semantic network analysis to identify the core keywords for understanding negative and positive predictors of mobile VR headset experiences. Through laboratory testing with three different commercial models, the main study measured and identified the predictors of user satisfaction. From the results, five factors were extracted as valid predictor variables and used for regression analysis. These factors were immersion, VR sickness, usability, wear-ability and menu navigation interface. All the five predictors were proved to be significant determinants of the perceived user satisfaction with mobile VR headsets. Usability was the strongest predictor, followed by VR sickness and wear-ability. Practical and theoretical implications of the results were discussed.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.301-308
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• 2024

Concrete carbonation is a continuous and slow process from the outside to the inside, in which its penetration slows down with the increased depth of carbonation. In this paper, the results of the evaluation of the measurement of concrete carbonation depth using a non-destructive ultrasonic testing method are presented. According to the results, the relative nonlinear parameter caused more sensitivity in carbonation changes compared to Rayleigh's fuzzy velocity. Thus, the acoustic nonlinear parameter is expected to be applied as a quantitative index to recognize carbonation effects. In this research, combo diagrams were developed based on the results of ultrasonic testing and the experiment to determine carbonation depth using a phenolphthalein solution, which could be considered as instructions in the projects involving non-destructive ultrasonic test methods. The minimum and maximum accuracy of this method were 89% and 97%, respectively, which is a reasonable range for operational projects. From the analysis performed, some useful expressions are found by applying the regression analysis for the nonlinearity index and the carbonation penetration depth values as a guideline.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Korean Journal of Adult Nursing
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• v.25 no.5
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• pp.549-558
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• 2013

Purpose: The purpose of this study was to identify impact of stress on depression among university students and the moderating effect of social support in the relationship between stress and depression in university students. Methods: A total of 445 subjects were participated from May to July 2011. Data were collected by self-reported questionnaires and t-test, ANOVA, and stepwise multiple regression with SPSS/WIN 20.0 were used to analyze the data. Results: The results indicate that depression was increased by stress and decreased with social support. Social support included four type of social support behavior; emotional support, informational support, material support, and appraisal support. Multiple regression analysis showed that moderating effect of emotional support was significant when provided by friends but not by parents. Conclusion: These finding indicate the importance of emotional support of friends on depression in university students.

• Journal of Applied Biological Chemistry
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• v.44 no.4
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• pp.177-179
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• 2001

Traditional wet chemical methods for testing of soil properties require extensive time and labor, and cause the discharge of pollutants, making them undesirable for routine soil analyses. This research was conducted to improve the accuracy of soil properties in soil fertility assessments. A total of 140 finely ground soil samples were used to obtain accurate calibrations and validation for estimating soil moisture, OM, and T-N. Finely ground soil samples satisfied the improved accuracy for routine NIR measuring of the field soils. The results indicated that NIR spectroscopy could be used as a routine method for quantitatively determining OM, moisture, and T-N of field soil, although this technique requires many combinations of sample pretreatments and data manipulations to obtain optimal predictions.