• STI Policy Review
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• v.8 no.2
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• pp.75-93
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• 2017

Recently, global competition in the manufacturing sector is driving firms in the manufacturing sector to conduct product innovation projects to maintain their competitive edge. The key points of product innovation projects are 1) what the purpose of the project is and 2) what expected results in the target market can be achieved by implementing the innovation. Therefore, this study focuses on the performance of innovation projects with a business viewpoint. In this respect, this study proposes the "achievement rate" of product innovation projects as a measurement of project performance. Then, this study finds the best strategies from various innovation activities to optimize the achievement rate of product innovation projects. There are three major innovation activities for the projects, including three types of R&D activities: Internal, joint and external R&D, and five types of non-R&D activities - acquisition of machines, equipment and software, purchasing external knowledge, job education and training, market research and design. This study applies decision tree modeling, a kind of data-mining methodology, to explore effective innovation activities. This study employs the data from the 'Korean Innovation Survey (KIS) 2014: Manufacturing Sector.' The KIS 2014 gathered information about innovation activities in the manufacturing sector over three years (2011-2013). This study gives some practical implication for managing the activities. First, innovation activities that increased the achievement rate of product diversification projects included a combination of market research, new product design, and job training. Second, our results show that a combination of internal R&D, job training and training, and market research increases the project achievement most for the replacement of outdated products. Third, new market creation or extension of market share indicates that launching replacement products and continuously upgrading products are most important.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.60-70
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• 2020

Artificial intelligence (AI) is expected to bring about a wide range of changes in the industry, based on the assessment that it is the most innovative technology in the last three decades. The manufacturing field is an area in which various artificial intelligence technologies are being applied, and through accumulated data analysis, an optimal operation method can be presented to improve the productivity of manufacturing processes. In addition, AI technologies are being used throughout all areas of manufacturing, including product design, engineering, improvement of working environments, detection of anomalies in facilities, and quality control. This makes it possible to easily design and engineer products with a fast pace and provides an efficient working and training environment for workers. Also, abnormal situations related to quality deterioration can be identified, and autonomous operation of facilities without human intervention is made possible. In this paper, AI technologies used in smart factories, such as the trends in generative product design, smart workbench and real-sense interaction guide technology for work and training, anomaly detection technology for quality control, and intelligent manufacturing facility technology for autonomous production, are analyzed.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1104-1111
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• 2024

In recent years, the development of digital design methods and structural analysis has made it possible to design complex building shapes. However, the construction of such buildings requires advanced knowledge of production, interaction with actual craftsmen, and verification of construction methods through mock-ups. Despite this, however, design education in Japanese universities is often limited to proposals at the predesign and schematic design stages, and students have few opportunities to study the design development and construction stages. However, in recent years, computer numerical control (CNC) machine tools have become relatively inexpensive and can be handled easily, even by beginners. Therefore, practical training programs intended to familiarize students with manufacturing techniques using such machines are becoming increasingly popular at Japanese universities. This paper reports on the content of a practical training program intended to provide students with practical experience from production to design in the field of manufacturing by using CNC machine tools and other digital fabrication equipment to create a mock-up of a complex curved surface for a building and simulate the process of construction production in the design development and construction management. It is difficult to create curved surfaces using only digital fabrication, and manual processes, such as bending wooden boards along curved surfaces, are required. Therefore, students were required to combine digital fabrication knowledge with hands-on manufacturing skills, including hand-bending and experiential learning, which helped students think about the necessary processes for constructing the shapes they designed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.819-825
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• 2010

For rehabilitation of stroke patients the repeat training is needed. However it is difficult to keep up the training for the patients because it is boring. Even if the patients continue the training there is no objective way to evaluate the remedial value. Recently there is a new attempt to apply the haptic system to the rehabilitation of the stroke patients. In this study a haptic system is applied to the rehabilitation of the stroke patients. Through the comparative analysis of the experimental data for the normal people and patients, the validity of the proposed rehabilitation was verified. In conclusion, the patient's condition at 8 days after the experiment has demonstrated the level of ADL.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.53-57
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• 2012

Modern manufacturing needs a great number of advanced engineers. China has the world's second largest equipment manufacturing and electronic information industry, and in 2020, the shortage of talented personnel in key industries will be more than 5 million in China. Universities and colleges are the main places to cultivate engineering talents. In this paper, we will introduce a multi-level engineering talents cultivating system we have applied in Dali University, China for more than 4 years. Under this training system, we have achieved some gratifying results.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.423-433
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• 2010

The presence of musculoskeletal burden tasks and work related musculoskeletal disorders (WMSDs) at Industrial workers was not well-known until 2000 in Korea. Since The Occupational Safety & Health Law was registered a business of proprietor duty in preventing work-related MSDs of workers In July of 2003 WMSDs became a big issue in Korea. A social previous interest was focused on the manufacturing industry just like auto and shipping industry in manufacturing sectors but nowadays it is spreading out to non-manufacturing fields gradually. Nevertheless, we have WMSD prevention Law and System in Korea to reduce WMSDs effectively and systematically we recognized some mistakes and problems of WMSD Law and System. In this paper we study these recent problems in Korea from about 10 years experience and proposed some proposals as discussion.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.175-184
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• 2021

The purpose of this study is to investigate the effect of the strategic role of the human resources department in the manufacturing industry on organizational effectiveness through education and training satisfaction. Among the data from the 7th human capital company panel survey, data from the manufacturing industry were used and analyzed through a structural equation model. The results of this study are as follows: First, the strategic role of the HR department has a positive effect on satisfaction with education and training. Second, satisfaction with education and training and the strategic role of HR departments have a positive effect on job satisfaction among organizational effectiveness. Third, it was confirmed that education and training satisfaction has a mediating effect on the relationship between the HR department's strategic role and job satisfaction. The results of this study are expected to become the basis for expanding the authority and responsibilities of the HR department, which is an important factor in overcoming the crisis faced by SMEs as well as innovation, and redefining their role.