• Advances in concrete construction
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• v.8 no.1
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• pp.55-64
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• 2019

Chloride corrosion has become the main factor of reducing the service life of reinforced concrete structures. The object of this paper is to propose a theoretical model that predicts the service life of chloride-corrosive concrete under fatigue load. In the process of modeling, the concrete is divided into two parts, microcrack and matrix. Taking the variation of mcirocrack area caused by fatigue load into account, an equation of chloride diffusion coefficient under fatigue load is established, and then the predictive model is developed based on Fick's second law. This model has an analytic solution and is reasonable in comparison to previous studies. Finally, some factors (chloride diffusion coefficient, surface chloride concentration and fatigue parameter) are analyzed to further investigate this model. The results indicate: the time to pit-to-crack transition and time to crack growth should not be neglected when predicting service life of concrete in strong corrosive condition; the type of fatigue loads also has a great impact on lifetime of concrete. In generally, this model is convenient to predict service life of chloride-corrosive concrete with different water to cement ratio, under different corrosive condition and under different types of fatigue load.

• Korean Journal of Social Welfare Studies
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• v.48 no.2
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• pp.303-329
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• 2017

This study aimed to explore the trajectories of computer game use of school age children and to identify the related predictors. The data for this study used Korean Children and Youth Panel data covering from the second year to the sixth year of elementary school. A total of 1,959 participants were analyzed. Latent class growth model was employed to explore the trajectories of computer game use and multinomial logistic regression was conducted to identify the significant predictors. Main results indicated that three types of trajectories were identified: low game using group, high initial using-fluctuating group, and high increasing game using group. Each group was found to be associated deferentially with sex, aggression, attention deficit, main caregiver's education, siblings, parent absence after-school, neglecting, family income, family trip, school grades, and peer relationship. Based on these findings, this study emphasized the importance of predictive intervention for the game user among early school age children and suggested useful practical strategies.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.127-142
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• 2023

The management of size and weight, which are the growth information of aquaculture fish in fish-farms, is the most basic goal. In this study, the epoch is defined in fish-farms from the time of stocking or dividing to the time of shipment, and the growth data for a total of three epoch is analyzed from a time series perspective. Growth information such as the size and weight of aquaculture fish that occur over time in fish-farms is compared and analyzed with water quality environmental information and feeding information, and a model is presented using the analysis results. In this study, linear, exponential, and logarithmic regression models are presented using the Box-Jenkins method for size and weight by epoch using data obtained in the field.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2399-2408
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• 2002

A prediction procedure has been developed to evaluate the microtructures and material properties of heat affected zone (HAZ) in pressure vessel steel weld, based on temperature analysis, thermodynamics calculation and reaction kinetics model. Temperature distributions in HAE are calculated by finite element method. The microstructures in HAZ are predicted by combining the temperature analysis results with the reaction kinetics model for austenite grain growth and austenite decomposition. Substituting the microstructure prediction results into the previous experimental relations, the mechanical material properties such as hardness, yielding strength and tensile strength are calculated. The prediction procedure is modified and verified by the comparison between the present results and the previous study results for the simulated HAZ in reactor pressure vessel (RPV) circurnferential weld. Finally, the microstructures and mechanical material properties are determined by applying the final procedure to real RPV circumferential weld and the local weak zone in HAZ is evaluated based on the application results.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.403-411
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• 2021

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries' economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.635-644
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• 2024

Although the business model(BM) patents act as a creative bridge between technology and the marketplace, limited scholarly attention has been paid to the content analysis of BM patents. This study aims to contextualize converging BM patents by employing topic modeling technique and clustering highly marketable topics, which are expressed through a topic-market impact matrix. We relied on BM patent data filed between 2010 and 2022 to derive empirical insights into the commercial potential of emerging business models. Subsequently, nine topics were identified, including but not limited to "Data Analytics and Predictive Modeling" and "Mobile-Based Digital Services and Advertising." The 2x2 matrix allows to position topics based on the variables of topic growth rate and market impact, which is useful for prioritizing areas that require attention or are promising. This study differentiates itself by going beyond simple topic classification based on topic modeling, reorganizing the findings into a matrix format. T he results of this study are expected to serve as a valuable reference for companies seeking to innovate their business models and enhance their competitive positioning.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.9-18
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• 2015

Software Reliability Growth Models (SRGMs) are useful for determining the software release date or additional testing efforts by using software failure data. It is not appropriate for a SRGM to apply to all software. And besides a large number of SRGMs have already been proposed to estimate software reliability measures. Therefore selection of an optimal SRGM for use in a particular case has been an important issue. The existing methods for selecting a SRGM use the entire collected failure data. However, initial failure data may not affect the future failure occurrence and, in some cases, it results in the distorted result when evaluating the future failure. In this paper, we suggest a method for selecting a SRGM based on the evaluation goodness-of-fit using partial data. Our approach uses partial data except for inordinately unstable failure data in the entire failure data. We will find a portion of data used to select a SRGM through the comparison between the entire failure data and the partial failure data excluded the initial failure data with respect to the predictive ability of future failures. To justify our approach this paper shows that the predictive ability of future failures using partial data is more accurate than using the entire failure data with the real collected failure data.

• Korean Journal of Food Science and Technology
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• v.52 no.6
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• pp.661-669
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• 2020

Samgak-Kimbap is a popular ready-to-eat (RTE) food at convenience stores, in Korea. Although Samgak-Kimbap is distributed through the cold chain supply system, inappropriate temperature storage conditions prior to consumption are a cause of concern. The objective of this study was to evaluate the risk of Staphylococcus aureus growth in Samgak-Kimbap in the retail market. The prevalence and contamination levels of S. aureus in Samgak-Kimbap (n=170) were monitored, and the predictive growth model of a five-strain cocktail of enterotoxin-producing S. aureus (SEA, SEB, SEC, SED, and SEE) was developed in Samgak-Kimbap as a function of temperature (4, 10, 11, 20, 25, and 37℃). We could not observe the growth of S. aureus and enterotoxin-producing S. aureus in Samgak-Kimbap at temperatures below 10℃. The probability of illness with S. aureus per serving of Samgak-Kimbap was 1.44×10-10 per day. The most influential factor in increasing the risk of foodborne illnesses was the contamination level of S. aureus in Samgak-Kimbap.