• Computers and Concrete
• /
• v.32 no.6
• /
• pp.577-594
• /
• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of Korean Society for Quality Management
• /
• v.52 no.3
• /
• pp.459-478
• /
• 2024

Purpose: This study aims to identify protective factors enabling emotional workers to manage stress and cope proactively. By enhancing their internal resources, it aims to provide a theoretical foundation for fostering positive outcomes and offering a basis for integrated human resource management and employee welfare. Methods: The data analysis utilized SPSS 22.0 and Smart PLS 4.0. After conducting tests for normality, exploratory factor analysis, confirmatory factor analysis, reliability analysis, measurement model validation, and structural model validation were performed. Relationships between variables were examined, and the significance and suitability of hypothesis paths were verified. Results: Firstly, it was confirmed that self-compassion and positive self-talk positively influence resilience, self-control, work engagement and subjective well-being. Secondly, resilience positively influences self-control, work engagement and subjective well-being. Thirdly, self-control positively influences work engagement but does not statistically significantly influence subjective well-being. Fourthly, work engagement positively influences subjective well-being. Fifthly, work engagement was found to mediate between self-control and subjective well-being. Conclusion: The study confirmed that self-compassion and positive self-talk serve as antecedents to enhancing emotional workers' resilience, self-control, work engagement, and subjective well-being. Additionally, by analyzing the structural relationships between these factors, it established a theoretical framework.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.6
• /
• pp.2437-2445
• /
• 2013

The objective of this study is finding the relationship between interstate highway accident frequencies and geometrics using Random Parameter Negative Binomial model. Even though it is impossible to take account of the same design criteria to the all segments or corridors on the road in reality, previous research estimated the fixed value of coefficients without considering each segment's characteristic. The drawback of the traditional negative binomial is not to explain the integrated variations in terms of time and the distinct characters specific segment has. This results in under-estimation of the standard error which inflates the t-value and finally, affects the modeling estimation. Therefore, this study tries to find the relationship of accident frequencies with the heterogeneous geometrics using 9-years and 7-interstate highway data in Washington State area. 16-types of geometrics are used to derive the model which is compared with the traditional negative binomial Model to understand which Model is more suitable. In addition, by calculating marginal effect and elasticity, heterogeneous variables' effect to the accidents are estimated. Hopefully, this study will help to estiblish the future policy of geometrics.

• Journal of Korean Society of Transportation
• /
• v.34 no.5
• /
• pp.465-475
• /
• 2016

Since inaccurate demand estimation for recent urban rail construction may result in financial burden to cities, precise prediction for operating cost as well as construction costs is necessary to avoid or reduce budget loss of the local or central government. The operating cost is directly related to the public fare and affect a policy to determine the rate system. Therefore, there is a pressing need to develop an estimating model for reliable operating cost of urban railway. This study introduces a new model to estimate the operating cost with new variables. It provides a better prediction in accuracy and reliability compared to the existing model, considering the feature of urban railway. For verification of our model, railway operation data from a few cities for the last five years were comprehensively examined to determine variables that affect the operating cost. The operating cost was estimated in a dummy regression model using five independent variables, which were average distance between stations, daily trains distance, total passenger capacity of a train in a train, driving mode(manned/unmanned), and investment type(financial/private).

• Journal of Climate Change Research
• /
• v.2 no.2
• /
• pp.79-91
• /
• 2011

In order to study interactions between climate change and air quality, a modeling system including the downscaling scheme has been developed in the integrated manner. This research focuses on the development of a downscaling method to utilize CCSM3 outputs as the initial and boundary conditions for the regional climate model, MM5. Horizontal/vertical interpolation was performed to convert from the latitude/longitude and hybrid-vertical coordinate for the CCSM3 model to the Lambert-Conformal Arakawa-B and sigma-vertical coordinate for the MM5 model. A variable diagnosis was made to link between different variables and their units of CCSM and MM5. To evaluate the dynamic downscaling performance of this study, spatial distributions were compared between outputs of CCSM/MM5 and NRA/MM5 and statistic analysis was conducted. Temperature and precipitation patterns of CCSM/MM5 in summer and winter showed a similar pattern with those of observation data in East Asia and the Korean Peninsula. In addition, statistical analysis presented that the agreement index (AI) is more than 0.9 and correlation coefficient about 0.9. Those results indicate that the dynamic downscaling system built in this study can be used for the research of interaction between climate change and air quality.

• Economic and Environmental Geology
• /
• v.55 no.3
• /
• pp.309-316
• /
• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.517-524
• /
• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.135-135
• /
• 2017

The Gramene database (http://www.gramene.org) is a powerful online resource for agricultural researchers, plant breeders and educators that provides easy access to reference data, visualizations and analytical tools for conducting cross-species comparisons. Learn the benefits of using Gramene to enrich your lectures, accelerate your research goals, and respond to your organismal community needs. Gramene's genomes portal hosts browsers for 44 complete reference genomes, including crops and model organisms, each displaying functional annotations, gene-trees with orthologous and paralogous gene classification, and whole-genome alignments. SNP and structural diversity data, available for 11 species, are displayed in the context of gene annotation, protein domains and functional consequences on transcript structure (e.g., missense variant). Browsers from multiple species can be viewed simultaneously with links to community-driven organismal databases. Thus, while hosting the underlying data for comparative studies, the portal also provides unified access to diverse plant community resources, and the ability for communities to upload and display private data sets in multiple standard formats. Our BioMart data mining interface enable complex queries and bulk download of sequence, annotation, homology and variation data. Gramene's pathway portal, the Plant Reactome, hosts over 240 pathways curated in rice and inferred in 66 additional plant species by orthology projection. Users may compare pathways across species, query and visualize curated expression data from EMBL-EBI's Expression Atlas in the context of pathways, analyze genome-scale expression data, and conduct pathway enrichment analysis. Our integrated search database and modern user interface leverage these diverse annotations to facilitate finding genes through selecting auto-suggested filters with interactive views of the results.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.36 no.6
• /
• pp.1011-1021
• /
• 2016

This study performs the potential flood hazard analysis by applying elevation data, soil data and land use data. The susceptibility maps linked to elevation, soil and land use are combined to develop the new types of flood hazard map such as runoff production map and runoff accumulation map. For the development of the runoff production map, land use, soil thickness, permeability, soil erosion and slope data are used as runoff indices. For the runoff accumulation map, elevation, knick point and lowland analysis data are used. To derive an integrated type of flood potential hazard, a TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) technique, which is widely applied in MCDM (Multi-Criteria Decision Making) process, is adopted. The indices applied to the runoff production and accumulation maps are considered as criteria, and the cells of analysis area are considered as alternatives for TOPSIS technique. The model is applied to Gamcheon watershed to evaluate the flood potential hazards. Validation with large scale data shows the good agreements between historical data and runoff accumulation data. The analysis procedure presented in this study will contribute to make preliminary flood hazard map for the public information and for finding flood mitigation measures in the watershed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.6
• /
• pp.275-285
• /
• 2020

This study aimed to track changes in students' perception about schools over time, to analyze how participation in the Education Welfare Priority Support Project(hereafter "the Project") explains the changes, and to determine how the results of changes affect students' learning engagement, self-confidence, and peer relationship. Data were collected from 103 schools nation-wide(51 elementary and 52 middle schools) from 2015, 2016 and 2017 from 820, 911, and 837 students, respectively. The data were analyzed by applying a latent growth model with two stages: unconditional and conditional. The findings are that first, the perception about schools by the students who participated in the Project increased over time; and second, that the improvement of students' perception of schools has a positive influence on their learning engagement, self-confidence, and peer relationship. In the future, it was suggested that programs aimed at improving positive perception about schools should be provided to all students led by teachers, and a customized integrated support program should be arranged to students in need of intensive support by the educational welfare specialists.