• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.223-232
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• 2023

The Work-Study parallel program has been promoted as a key policy to resolve the mismatch between industrial sites and school education and realize a competency-centered society, and as of December 2022, 16,664 companies participated in the training. Learning companies play a very important role as education and training supply organizations that conduct field training. In this study, for the evaluation of learning companies participating in work-study program, the authors derive important factors that determine the quality of on-site education and training by analyzing the cognitive structure of experts in charge of the company and present evaluation indicators for learning enterprises. Therefore, it is intended to lay the foundation for promoting the quality of work-study parallel program.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.183-192
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• 2023

This study analyzes the performance of flipped learning, an offline class method conducted in a degree-linked work-learning parallel system. Training in the work-study parallel system, which is conducted as part of job competency improvement, has thoroughly adhered to the offline method, but in line with COVID-19, unlike before, it is changing in the direction of using the online method more actively. However, educational methods such as flipped learning are not new because the degree-linked operation is applied to the academic system and education method of universities and is practically the same form as general university education. Therefore, it is necessary to analyze the educational performance and complementary points of flipped learning, which has recently been expanded in the degree-linked work-study parallel system, considering the characteristics of this system, in which classes are held only on weekends. As a result of statistical analysis based on the survey, some of the outcomes of flipped learning have been confirmed, and in order to increase the performances, it is necessary to continuously seek out specific measures to encourage learning and expand communication between instructors and students.

• Industry Promotion Research
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• v.5 no.1
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• pp.47-52
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• 2020

In this study, parallel work-learning training, which was started in 2013 as a pre-employment promotion policy, is an important factor that determines the success or failure of training. As a time when various institutional supplementation is needed to encourage company participation, this study is to identify the factors of participation of companies participating in work-learning parallel. To this end, a questionnaire survey was conducted of companies participating in parallel work-learning in Chungnam, and the results were analyzed using the structural equation model. As a result of the study, the reason for the company's participation in parallel work-learning was firstly, 84% of government subsidy received education and training expenses. Second, 66% of workers were able to pay less than regular workers, and thirdly, it was easy to hire new employees in the field. 26%, 17% of them were invited by acquaintances for no particular reason. Therefore, the study suggests that participation in the work-learning parallel training contributes to the management costs, management of employee turnover, and human resource development. In future research, it is necessary to subdivide tests and estimates by conducting studies on regions, occupations, gender, wages, and years of service in Korea.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.671-679
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• 2023

This study aims to establish an evaluation model by quantifying the evaluation index as a follow-up study to the development of evaluation index for work-study parallel learning companies. An evaluation model was established by verifying the 2nd level components based on the quantitative factors of the learning company, the qualitative factors, the competency factors of the person in charge, and the competency factors of the learning workers, which are the highest-level components derived from previous study. For the evaluation of a learning company, an AHP survey was conducted with experts in charge of the company consulting to derive important factors that determine the quality of on-site education and training, and the evaluation model of the learning company was completed and grouped by calculating the weight between evaluation items proceeded. Work-study parallel program was promoted as a key policy to resolve the mismatch between industrial sites and school education and realize a competency-centered society, and as of December 2022, 16,664 companies participated in the training. Learning companies play a very important role as education and training supply organizations that conduct field training. It is expected that the support and consulting plan for each level of learning companies according to the evaluation model presented in this study will be used as basic data to improve the quality of work-study parallel program.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.331-337
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• 2020

This study investigated the perception of the learning-workers' organizational effectiveness. They participate in the degree-linked work-learning parallel system. As a result, the turnover intention and job performance were higher than those of general office workers, and organizational commitment was lower. In addition, the higher scholastic year and grades, the higher job satisfaction. the higher grades, the lower burnout. specially the 2nd, 3rd, and 4th graders showed higher turnover intentions than the first grader. The results of the survey of (dis)satisfaction factors of learning workers also show that OJT management in the field should be strengthened rather than OFF-JT education in universities. This paper suggests that companies participating in the work-learning parallel system need higher-level management in aspects such as educational supports, personnel management, and OJT management as well as competency development of learning-workers. It shows that supplementary measures are needed for learning time and learner management of participating companies.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Journal of Practical Engineering Education
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• v.5 no.2
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• pp.163-168
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• 2013

The vicious cycle of over-education has been made. That is, higher education enrollment rate is high, but university graduate employment rate is low. To eliminate this cycle and relieve youth unemployment and young people to enter the labor market early, dual education and training system is needed. This dual system can support working and learning in parallel. So, worker can get the opportunity pre-employment and post-learning and improve his/her job skills. Recent MOOC (Massive Open On-line Course), a new form of online education system, has emerged. MOOC combines education, entertainment and social networking, and emphasize the interaction between faculty and student and between students. The educational contents of MOOC are available free of charge. Using newly changed online education environments we can effectively provide knowledge and skills. In technology and engineering education hands-on training is necessary. In order to support work based learning dual system for worker to work and learn in parallel, we should build the multi-learning system to combine the online education and campus hands-on practice.

• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.181-193
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• 2019

A workplace trainer is required to have profound theoretical and technological knowledge, sufficient experience and problem-solving ability in order to play the role of a competent trainer and assessor for the apprentices and experienced worker students who are participating in the Work-Learning Dual System of the company. This study has introduced a Degree-linked Highly Skilled professional Course in Korea and proposed an optimized standard education model for the training of high-quality workplace trainers. It is expected that this study will contribute greatly to the training of highly skilled professionals for efficient implementation of the Work-Learning Dual System and stable institutional settlement of the Highly Skilled professionals Course as the first empirical study on the training of highly skilled professionals.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.