Adfreeze bond strength is a primary design parameter, which determines bearing capacity of pile foundation in frozen ground. It is reported that adfreeze bond strength is influenced by various affecting factors like freezing temperature, confining pressure, characteristics of pile surface, soil type, etc. However, several limited researches have been performed to obtain adfreeze bond strength, for past studies considered only few affecting factors such as freezing temperature and type of pile structures. Therefore, there exists a limitation of estimating the design parameter of pile foundation with various factors in frozen ground. In this study, artificial neural network algorithm was involved to predict adfreeze bond strength with various affecting factors. From past five studies, 137 data for various experimental conditions were collected. It was divided by 100 training data and 37 testing data in random manner. Based on the analysis result, it was found that it is necessary to consider various affecting factors for the prediction of adfreeze bond strength and the prediction with artificial neural network algorithm provides enough reliability. In addition, the result of parametric study showed that temperature and pile type are primary affecting factors for adfreeze bond strength. And it was also shown that vertical stress influences only certain temperature zone, and various soil types and loading speeds might cause the change of evolution trend for adfreeze bond strength.
Objectives: This research intended to examine the relationships among social capital, socioeconomic factors, and health-related lifestyles and the effect of these factors on self-rated health in the Republic of Korea. Methods: The data of the social statistics survey that the Korea National Statistical Office conducted in 2006 were chosen and 37,928 people from them, who were 25~59 years old were sampled. This paper made path analysis to examine the relationships among social capital, socioeconomic factors, and health-related lifestyle and the influence of these factors on self-rated health. Results: In relation to the overall influences of socioeconomic factors, social capital, and health-related lifestyle on self-rated health, the following factors had a significant positive direct effect: education(0.069), subjective class(0.108), marriage(0.054), satisfaction with family relationships(0.087), reliability of institutions(0.020), citizens' participation(0.021), exercise(0.037), and refrain from smoking(0.011). However, abstinence from alcohol(-0.067) had a negative direct effect on self-rated health Conclusion: Based on the results, this paper can suggest that the plan of keeping and building up social capital should be considered in the whole aspects of the society and the project promoting drinking moderation is required to consider social culture than individuals.
The purpose of the Convergence study is to analyze the impact of students' satisfaction and educational performance through research on overall educational service quality factors for beauty academies. The subjects of the study were conducted from March 29 to April 12, 2020 for those in their 10s and 40s attending beauty academies in Seoul, Gyeonggi-do and Incheon, and 377 surveys were analyzed and used for research. Multiple regression analysis was performed using the SPSSWIN 21.0 program for research purposes. As a result of the study, the higher the level of empathy, reliability, responsiveness, type, and certainty of educational service quality, the higher the satisfaction level of the class. The higher the satisfaction level, the higher the educational performance. Based on these research results, measures to improve the quality of education services that can enhance the satisfaction and educational performance of beauty academies should be improved by diversifying educational methods and contents and enhancing the quality of education and service expertise. The expertise of education should be strengthened to improve competitiveness. Therefore, follow-up research is needed to develop various programs to enhance future course satisfaction and educational performance and to develop education methods to enhance the quality of beauty education services.
In a situation where there is excessive competition among logistics centers due to the low price of logistics centers for attracting freight volume, the conditions provided by the logistics centers are similar. Therefore, in order to determine the logistics center, we want to find out what kind of differentiated service the shippers desire, as well as what level of service they want in addition to al ow price. There are currently no studies about the service quality of logistics centers. The components of the service quality of the logistics center were extracted by applying them to the logistics center based on the existing service quality theory. Factor analysis revealed five dimensions of service quality: tangibility, reliability, empathy, assurance, and know-how. It was found that service quality had a statistically significant influence on customer satisfaction, from the investigation of the causal effect relationship. In addition, 'Know-How' among these factors has more influence on customer satisfaction, so it is important to accumulate differentiated 'Know-How' only in logistics centers.
KIPS Transactions on Software and Data Engineering
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v.7
no.4
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pp.129-134
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2018
Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.
Journal of Korean Society of Coastal and Ocean Engineers
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v.23
no.3
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pp.258-264
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2011
A MCS technique is represented to stochastically analyze the uncertainties of wave forces exerted on the upright sections of composite breakwaters. A stochastical models for horizontal and uplift wave forces can be straightforwardly formulated as a function of the probabilistic characteristics of maximum wave height. Under the assumption of wave forces followed by extreme distribution, the behaviors of relative wave forces to Goda's wave forces are studied by the MCS technique. Double-truncated normal distribution is applied to take the effects of uncertainties of scale and shape parameters of extreme distribution into account properly. Averages and variances of relative wave forces are quantitatively calculated with respect to the exceedance probabilities of maximum design wave height. It is found that the averages of relative wave forces may be decreased consistently with the increases of the exceedance probabilities. In particular, the averages on uplift wave force are evaluated slightly larger than those on horizontal wave force, but the variations of coefficient of the former are adversely smaller than those of the latter. It means that the uncertainties of uplift wave forces are smaller than those of horizontal wave forces in the same condition of the exceedance probabilities. Therefore, the present results could be useful to the reliability based-design method that require the statistical properties about the uncertainties of wave forces.
This paper focused on the effect of Kansei design on the web in branding as well as its influence factors. As a key of this research, it classified web users' Kansei into five categories; 1) functional Kansei, 2) sensoryKansei, 3) psychological Kansei, 4) relational Kansei and 5) cultural Kansei, and organized relevant factors. Online surveys were conducted on seven websites of the fast food brands in Korea (Lotteria, Mcdonald, BurgerKing, Popeyes, KFC, Pizza Hut, Domino's) which are targeting 463 male and females in 20s. As a result, an average of 58% responded that they had a positive Kansei experience and could enhanceits brand preference. Of the sensory Kansei, visual design factors were the one that gave the greatest effect on brand preference enhancement. Regarding the functional Kansei satisfaction, such as user convenience and access speed were also one of the most crucial variables for the whole Kansei satisfaction. Moreover, the preference enhancement brought not only a positive effect on its reliability but also its brand image and consumers' purchasing desire. Based on the survey results, the additional FGI (Focus Group Interview) had been conducted and determined \circled1 what kind of major Kansei that users wanted to have satisfied, \circled2 what type of design can give strong Kansei appeal to its users, and \circled3what design factors gave an effect on sensory emotion. In the course of this research, Itried to renew the awareness of the web importance as a major channel in non-mass interactive marketing, and suggest the effect and its possibility of emotional branding through Kansei design in the web as well as design principles of strategic Kansei design.
Journal of Korean Society for Geospatial Information Science
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v.24
no.1
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pp.61-68
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2016
Digital aerial images have been commonly used in a large scale map production owing to their excellent geometry, and high spatial and radiometric resolution in recent years. However, a quality verification process for acquired images should be preceded in order to secure the high precision and reliability of produced results. Several experimental studies to verify digital imaging systems have been vigorously researched by constructing permanent test field in abroad. On the other hand, it is urgently necessary to suggest a practical scheme for an image quality verification, because this related study and experiment are still in its early stage at home. Hence, this study aims to present an easy method to measure the spatial resolution of the image in a visual way using a portable Siemens star. The images used in the study were obtained with three different cameras, two frame array sensors of DMC, UltraCamXp and a linear array sensor of ADS80. The Siemens star target appeared in every image is extracted and then the spatial resolution of image is compared with theoretical GSD(Ground Sample Distance) by a visual method. In addition, the change of spatial resolution depending on the location of the Siemens star from image center and flight direction and cross-flight direction is also compared and analyzed. As study results, while the theoretical GSDs of images taken with each camera are about 6~9cm, the visual resolutions are 1.2~1.3 times as great as the theoretical ones.
In the recent e-learning environment, avatars are often used to help learners get familiar with the contents, which is ultimately to motivate them to study more. Therefore, it is important to investigate whether avatars have actually the desirable effect on users of e-learning materials. Surprisingly, however, no extensive study has been conducted on this crucial issue Accordingly, main objectives this study are summarized as follows. First, we need to gain better understanding of how much learners' trust towards avatars (termed as "avatar trust") is transferred to learners' trust towards e-learning contents (termed as "contents trust"). Second, we need to investigate how much learners' personal relationships with avatars as well as learning behaviors change depending on avatar types (attractive vs. professional) and contents complexity (easy vs. difficult). As described in the study objectives, in order for us to analyze empirical data more systematically, we classified avatar types into two: "attractive" and "professional;" the contents are categorized as either "easy" or "difficult." Therefore, it is essential for this study to build a prototype e-learning website on which our research purpose can be realized and tested effectively with proper avatar types and e-learning contents. For this purpose, we built a prototype e-learning website, in which avatars are invited from currently working avatar instructors used in real-world e-learning websites, and e-learning contents are adapted from real-world contents about Java programming topic, which have been proved to have shown high quality and reliability. Our research method includes questionnaire survey by inviting a number of valid respondents comprised of office workers who are believed to have high demands for the e-learning contents as well as those who have previous experience with avatar instructors. Respondents were given one of the four e-learning experiment conditions (2 avatar types x 2 contents types) on a random basis. Each experimental e-learning condition is framed to have the same quality but different avatar type and content complexity. Then the respondents are asked to fill out the survey form which has questions about avatar trust, contents trust, personal relationships with avatar, and learning behavior, among others. Regarding the constructs used in research model, we based them rigorously on previous studies. For example, we used six constructs such as behavior to give information (BGI), behavior to obtain information (BOI), need for inclusion wanted, need for control wanted, contents trust, and avatar trust. To measure them, 7-Likert scales were used in the questionnaire. E-learning performance was measured indirectly through two constructs such as BGI and BOI. Six constructs used in the research model were adopted and revised from the FIRO-B model suggested by Schutz. Empirical results are as follows: First, professional avatars are more effective for difficult contents, while attractive avatars were not as effective for easy contents. Second, our study results ascertained that avatar trust transfers to contents trust regardless of avatar types and contents complexity.
Transactions of the Korean Society of Mechanical Engineers A
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v.41
no.8
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pp.721-728
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2017
High-grade gray cast iron (HCI350) was prepared by adding Cr, Mo and Cu to the gray cast iron (GC300). Their microstructure, mechanical properties and fatigue strength were studied. Cast iron was made from round bar and plate-type castings, and was cut and polished to measure the percentage of each microstructure. The size of flake graphite decreased due to additives, while the structure of high density pearlite increased in volume percentage improving the tensile strength and fatigue strength. Based on the fatigue life data obtained from the fatigue test results, the probability - stress - life (P-S-N) curve was calculated using the 2-parameter Weibull distribution to which the maximum likelihood method was applied. The P-S-N curve showed that the fatigue strength of HCI350 was significantly improved and the dispersion of life data was lower than that of GC300. However, the fatigue life according to fatigue stress alleviation increased further. Data for reliability life design was presented by quantitatively showing the allowable stress value for the required life cycle number using the calculated P-S-N curve.
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