• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.231-232
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• 2022

본 논문에서는 직업 교육 분야에 인공지능 융합 교육을 위한 파이썬 블록과 텍스트 동시 코딩 플랫폼을 설계하였다. 플랫폼에 코딩 언어로는 데이터 분석과 머신러닝의 다양한 라이브러리를 지원하고 있는 파이썬으로 하며, 직업 교육의 영역 전문가가 쉽게 직무 기능 파이썬 블록 모듈을 만들어 추가하고 커스터마이징을 할 수 있는 아키텍처를 갖고 있다. 제안한 플랫폼을 활용한 인공지능 융합 직업 분야로 바이오와 기계공학 분야의 블록 모듈을 추가하고 실습 예제를 만드는 과정을 보여 플랫폼의 유용성과 효율성을 보였다.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of vocational education research
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• v.37 no.2
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• pp.77-99
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• 2018

The purpose of this study was to investigate the employment possibility of the humanities college graduates to science and engineering field occupations, and to identify the occupational characteristics related to employability perceived by workers. To do this, basic statistical analysis, correlation analysis, and hierarchical multiple regression analysis were conducted using the data surveyed on 2,600 workers in the science and engineering field in the 'Research on Korean Occupational Index for Career and Employment Service(2017)'. The main results are as follows. First, the employment possibility of the humanities college graduates to science and engineering field was low, except for some occupations in the information communication, manufacturing and processing fields. Second, the occupational characteristics affecting the employment possibilities of the humanities college graduates to science and engineering field are as follows: low importance of the final education, low importance of the major, low importance of qualification, high importance of vocational training, easy to return after the career break, high level of gender equality, high level of pleasant work environment, high employment retention, easy to self-employment or start-up, and increasing number of jobs. Based on the results of this study, to support employment of humanities college graduate from the occupational aspect, it is necessary to find out some detailed jobs or to develop convergence occupations. At this time, it is possible to utilize the occupational characteristics factors that increase the employment possibility of humanities college graduates to science and engineering occupations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2556-2563
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• 2015

This study aims to find out the employment effectiveness of employment preparation activities that college students experience, especially focused on analyzing employment effects of college employment-related activities such as career choice and employment support program, along with individual preparation activities such as qualification, vocational training, etc. It performed binary logistic regression analysis using 2011 Graduates Occupational Mobility Survey data of 3,249 college graduates. The results showed that In college characteristics, the higher grade point average was and the more college was located in non-metropolitan area, the higher employment probability was. In the case of major field, Medicine, Education, Engineering, Social Science, Natural science in highest first order had employment probability higher than the reference group. The results showed that the number of qualification, interview skill & resume description skill program participation, and job search experience before and after graduation among employment preparation activities had an effect on employment. The rest, that is, vocational training, career employment curriculum, work experience program, career counseling program, employment camp, in-school job experience, employment goal status before graduation did not have an direct effect on employment.

• Smart Structures and Systems
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• v.33 no.5
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• pp.333-347
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• 2024

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

• Steel and Composite Structures
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• v.43 no.5
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• Steel and Composite Structures
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• v.48 no.3
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• pp.275-291
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• 2023

This paper has focused on presenting vibration analysis of trapezoidal sandwich plates with 3D-graphene foam reinforced polymer matrix composites (GrF-PMC) core and FG wavy CNT-reinforced face sheets. The porous graphene foam possessing 3D scaffold structures has been introduced into polymers for enhancing the overall stiffness of the composite structure. Also, 3D graphene foams can distribute uniformly or non-uniformly in the plate thickness direction. The effective Young's modulus, mass density and Poisson's ratio are predicted by the rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes' effective aspect ratio as the filler content increases. The First-order shear deformation theory of plate is utilized to establish governing partial differential equations and boundary conditions for trapezoidal plate. The governing equations together with related boundary conditions are discretized using a mapping-generalized differential quadrature (GDQ) method in spatial domain. Then natural frequencies of the trapezoidal sandwich plates are obtained using GDQ method. Validity of the current study is evaluated by comparing its numerical results with those available in the literature. It is explicated that 3D-GrF skeleton type and weight fraction, carbon nanotubes (CNTs) waviness and CNT aspect ratio can significantly affect the vibrational behavior of the sandwich structure. The plate's normalized natural frequency decreased and the straight carbon nanotube (w=0) reached the highest frequency by increasing the values of the waviness index (w).

• Advances in concrete construction
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• v.9 no.5
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• pp.459-467
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• 2020

Mechanical and thermal properties of composite sandwich wall panels are affected by changes in their external environment. Humidity and temperature changes induce stress on wall panels and their core connectors. Under the action of ambient temperature, temperature on the outer layer of the wall panel changes greatly, while that on the inner layer only changes slightly. As a result, stress concentration exists at the intersection of the connector and the wall blade. In this paper, temperature field and stress field distribution of UHPC-RW-RC (Ultra-High Performance Concrete - Rock Wool - Reinforced Concrete) wall panel under high temperature-sprinkling and heating-freezing conditions were investigated by using the general finite element software ABAQUS. Additionally, design of the connection between the wall panel and the main structure is proposed. Findings may serve as a scientific reference for design of high performance composite sandwich wall panels.

• Journal of Engineering Education Research
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• v.21 no.2
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• pp.60-66
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• 2018

This study aims to compare human resource development by industrial and occupational characteristics of science and engineering graduates. To achieve research objectives, this study analyzed whether differences exist in human resource development, such as participation in vocational training, wage levels, and cuture of talened person preference, by industrial and occupational characteristics. The results showed that workers in engineering fields received more benefits and support compared to those in non-science and engineering field occupations at the quantitative statistics of vocational training and wage levels. On the contrary, according to the qualitative statistics, workers of non-science and engineering field have a culture of talented person preference compared to those in science and engineering occupation.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.107-116
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• 2024

This study examined the changes in the mechanical properties of coral concrete under different coconut fiber admixtures. To accomplish this goal, the compressive strength, splitting tensile strength, flexural strength and elastic modulus properties of coral concrete blocks reinforced with coconut fibers were measured. The results showed that the addition of coconut fiber had little effect on the cube and axial compressive strengths. With increasing coconut fiber content, the flexural strength and splitting tensile strength of the concrete changed substantially, first by increasing and then by decreasing, with maximum increases of 36.0% and 12.8%, respectively; additionally, the addition of coconut fibers resulted in a failure type with some ductility. When the coconut fiber-reinforced coral concrete was 7 days old, it reached approximately 74% of its maximum strength. The addition of coconut fiber did not affect the early strength of the coral concrete mixed with seawater. When the amount of coconut fiber was no more than 3 kg/m3, the resulting concrete elastic modulus decreased only slightly from that of a similar concrete without coconut fiber, and the maximum decrease was 5.4%. The optimal dose of coconut fiber was 3 kg/m3 in this study.