• Journal of Distribution Science
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• v.14 no.8
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• pp.23-33
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• 2016

Purpose - The purpose of this study was to analyze the effects of education & training and organizational culture on job satisfaction and organizational commitment and then examine the difference that would appear depending on the job title in the case of Korean employees in the manufacturing industry. Research design, data, and methodology - Our sample consisted of 1,013 administrative employees in the manufacturing industry with data extracted from the 4th Human Capital Corporate Panel (2011) survey. Descriptive statistics and multiple regression analysis were conducted. Results - Results showed that education & training had positive effect only on job satisfaction while organizational culture had positive effect on both job satisfaction and organizational commitment. Components under education & training (duration of the formal learning, efficacy of formal learning and efficacy of informal learning) and organizational culture (equity, communication, talent recognition, innovation) that were found to be affecting job satisfaction and organizational commitment revealed to be different depending on the hierarchical position of the employee. Conclusions - Different approach and policy for each group of job title could be considered in order to increase job satisfaction and enhance organizational commitment of the employees.

• Design & Manufacturing
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• v.15 no.3
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.9 s.174
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• pp.93-98
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• 2005

The purposes of this paper are the review and derivation of design characteristics for the new construction of the practical flexible manufacturing system. The basic ideas to analyze the manufacturing system which is the automatically operated are dependant on the various manufacturing procedures in factory. The practical flexible manufacturing systems have various mechanical subsystems appropriated fur the final manufacturing products. Therefore the systems have the various kinds of hardwares as well as softwares. We study the software for the practical flexible manufacturing system designed and developed in the Halla University with the related company. Specially the design concepts and using specifications of all subsystems which are composed of mechanical and electronic movements of the product are analyzed and introduced in this dissertation.

• Safety and Health at Work
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• v.9 no.3
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• pp.257-264
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• 2018

Background: This paper presents the findings of a pilot research survey which assessed the degree of balance between safety and productivity, and its relationship with awareness and communication of human factors and safety rules in the aircraft manufacturing environment. Methods: The study was carried out at two Australian aircraft manufacturing facilities where a Likert-scale questionnaire was administered to a representative sample. The research instrument included topics relevant to the safety and human factors training provided to the target workforce. The answers were processed in overall, and against demographic characteristics of the sample population. Results: The workers were sufficiently aware of how human factors and safety rules influence their performance and acknowledged that supervisors had adequately communicated such topics. Safety and productivity seemed equally balanced across the sample. A preference for the former over the latter was associated with a higher awareness about human factors and safety rules, but not linked with safety communication. The size of the facility and the length and type of employment were occasionally correlated with responses to some communication and human factors topics and the equilibrium between productivity and safety. Conclusion: Although human factors training had been provided and sufficient bidirectional communication was present across the sample, it seems that quality and complexity factors might have influenced the effects of those safety related practices on the safety-productivity balance for specific parts of the population studied. Customization of safety training and communication to specific characteristics of employees may be necessary to achieve the desired outcomes.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.117-125
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• 2020

According to the government's smart factory promotion project for small and medium-sized enterprises, more than 10,000 intelligent factories are scheduled or already built in the country and the government-led goal is to nurture 100,000 skilled workers by 2022. Smart Factory introduces numerous types of education and training courses from the supplier's point of view, such as training institutions belonging to local governments, some universities, and public organizations, in the form of an efficient resource management system and ICT technology convergence in the automated manufacturing equipment. The lack of linkage with the NCS, the standard for training, seems to have room for rethinking and direction. Results of survey is provided for the family companies of K-University in the metropolitan area and Chungnam area, and analyzes job demands by identifying whether or not they want to introduce smart factories. Defining the practitioners who will serve as a window for the introduction of smart factory technology within the company, setting up a training goal in consideration of the career path, and including the level of training required competency units, optional competency units, and training time suitable for introducing and operating smart factories. Author would like to present an NCS-based qualification design plan.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.171.2-171
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• 2001

This paper presents a force control of a arm of walking training robot. The current gait training apparatus in hospital are ineffective for the difficulty in keeping constant unloading level and constraining patients to walk freely. The proposed walking training robot is designed to unload body weight effectively during walking. The walking training robot consists of unloading manipulator and mobile platform. The manipulator driven with a electro-mechanical linear mechanism unloads body weight in various level. The mobile platform is wheel type, which allows to patients unconstrained walking. Unloading system with electro-mechanical linear mechanism has been developed, which has advantages such as low noise level, light weight, low manufacturing cost and low power consumption. A system model for the manipulator ...

• IE interfaces
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• v.16 no.spc
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• pp.7-13
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• 2003

This paper is concerned with a six sigma green belt training program. Comparative studies of existing training programs for three major companies (Samsung Electronics Company, Hyundai Motor Company, and LG Chemical Ltd.) and two consulting firms (Korean Standards Association and Korea Management Association) are made. Based on the comparative studies, a new green belt training program is proposed. The main focus of this program is on manufacturing, specially on cost and waste reductions, yield improvement, and operations with opportunity to improve capacity without major capital expenditure. The green belts take up to 4 or 5 days of intensive, highly quantitative training, roughly corresponding to the five macro phases of the six sigma methodology: define, measure, analyze, improve, and control. The six sigma tool sets for each phase are also specified.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.3
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• pp.1-5
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• 2014

The purpose of this study is to develop a manpower training program of technological development. The types of developed manpower training models are education about the technological development process, literacy education about technological development, and field experience. The education about the technological development process is accomplished through industry-university cooperation based on shared technical concerns. Literacy education about technological development includes information about essential methods for manpower education. Field experience involves the development of effective teaching plans.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.3
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• pp.57-62
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• 2005

Neural networks, which have learning and self-organizing abilities, can be advantageously used in the pattern recognition. Neural network techniques have been widely used in monitoring and diagnosis, and compare favourable with traditional statistical pattern recognition algorithms, heuristic rule-based approaches, and fuzzy logic approaches. In this study the fault diagnosis of the face-milling using the artificial neural network was investigated. After training, the sample which measure load current was monitored by constant output results.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.430-435
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.