• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.227-239
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• 2024

This study aims to analyze the impact of vocational training received by learning workers through the degree-linked work-study program on their learning outcomes. Specifically, we explore the causal relationship between various factors considered during university degree program admission and selection, and the average GPA (Grade Point Average) after admission. To achieve this, we conducted regression analysis and variance analysis using historical admission data and GPA records of 976 students from three undergraduate programs at a domestic K university that implements the degree-linked work-study model. Additionally, we included company information from publicly available databases that could potentially influence the academic performance of learning workers. Our analysis revealed significant causal relationships across various factors, including the classification of the high school attended, gender, family background, subject-specific grades in high school, duration of employment at the company, and age at the time of admission. Based on these findings, we anticipate that universities operating similar degree programs can enhance their selection procedures for learning workers. Furthermore, the results of this study can serve as foundational data for future policy recommendations related to degree-linked work-study programs.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.607-612
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• 2002

An automatic design method of behaviors in behavior-based robotics is proposed. With this method, a robot can design its behaviors by itself without aids of human designer. Automating design procedure of behaviors can make the human designer free from somewhat tedious endeavor that requires to predict all possible situations in which the robot will work and to design a suitable behavior for each situation. A simple reinforcement learning strategy is the main frame of this method and the key parameter of the learning process is significant change of reward value. A successful application to mobile robot navigation is reported too.

• Journal of Engineering Education Research
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• v.17 no.6
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• pp.77-83
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• 2014

This paper deals with the practical experience and lessons to develop the criteria for evaluating projects in Computer Science & Information Engineering. In engineering education, project-based learning is different from problem-based learning. Project tasks are closer to professional reality. And self-direction is stronger in project work. In this paper, the development of project evaluation criteria is introduced with consideration of these differences. It is explained through running examples of basic project courses in Computer Science & Information Engineering.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.195-200
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• 2023

The main purpose of the article is to study the peculiarities of the work of the Ukrainian language in the upper grades of the lyceum based on the activity approach. Despite the fact that a number of scientific studies and applied developments on teaching Ukrainian as a foreign language have recently appeared in Ukrainian linguistics, significant problems in this area should be recognized (organization of the educational process when learning a language as a foreign language, general methodological principles, psycho- and sociolinguistic foundations, communicative approaches), the non-resolution of which leads to methodologically unreasonable teaching of the Ukrainian language as a foreign language, the use of methods of teaching the language as a native language or the study of the language as a subject (linguistic aspect). In addition, due attention is not paid to the development of communication skills, which, firstly, worsens the quality of teaching and learning. Based on the results of the analysis, the key aspects of the work on the Ukrainian language in the senior classes of the lyceum were analyzed on the basis of an activity approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.322-328
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• 2018

The purpose of this study is to examine the performance of Problem-Based Learning (PBL) based on Google Classroom(GC). GC is an easy and free online learning that plays an important role in education and training recently. Students become increasingly independent in self-directed learning while sharing ideas and resources, transferring knowledge actively across domains and researching for solutions to the given problem in PBL. Students can check the submission date on the task page and start the task with a single click, and teachers can quickly check if the task is complete, and score it directly from GC. Designed The Evaluation of Problem -Based Learning based on GC in this study. The students read the given materials and identify the purpose of the subjects and selected the learning issues, investigated them. After then they discuss the subjects and make the reports. The students work to study them with research papers, books and internet materials. The research findings showed that PBL based on GC was effective in learning together. Students had positive attitude in their PBL learning environment. This study suggests it is possible that the development of PBL Evaluation on the GC.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• Journal of Korean Physical Therapy Science
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• v.9 no.3
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• pp.179-186
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• 2002

This study addresses the need to adopt teaching-learning approaches in physical therapy education that develop links between theory and clinical practice in a meaningful way. Problem-based learning (PBL) is presented as a useful way to educate physical therapy for the future. The essential characteristics of problem-based learning include: curricular organization around problems rather than disciplines; an integrated curriculum rather than one separated into clinical and theoretical components; and an inherent emphasis on cognitive skills as well as on knowledge. PBL as implemented in the health sciences, is an educational method in which the focus of learning is a small-group tutorial in which students work through health care scenarios. The goals of the health care scenarios are to provide a context for learning, to activate prior knowledge, to motivate students, and to stimulate discussion. Learning is student-centered rather than faculty-centered, and self-directed learning is emphasized. Whereas the former focuses on critical thinking and clinical judgement, the latter's emphasis is on clinical competency. The physical therapist (PT) program at Cheju Halla college is a partial integrated problem-based curriculum. The history and process of PBL in general and in the PT program are reviewed. Long-term advocates of PBL stress that it is the only known method for preparing future professionals to be able to adapt to change, learning how to reason critically, enabling a holistic approach to health.