• Journal of the Korea Institute of Building Construction
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• v.19 no.3
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• pp.255-261
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• 2019

Poor working conditions in the construction industry have made young people reluctant to enter it, resulting in supply/demand unbalance of construction skilled worker. Currently, most construction workers in Korea acquire skills through apprenticeships. Against this background, this study aimed to identify problems in apprenticeship-based methods of technical education, and to suggest measures that could improve these methods. As a measure to improve current apprenticeship-based methods of technical education, technical education needs to be regularly provided on construction sites, and each construction company needs to issue a certificate to workers confirming their completion of technical education. It is also necessary to establish a system in which this certificate is recognized as education experience. In addition, it is recommended to form partnerships with construction technology education institutes supported by the government in order to provide construction workers with opportunities to receive new technologies and construction methods free of charge, and it is urgently necessary to establish and implement a policy to adequately support their cost of living during this period.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.22-33
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• 2021

In the case of open phase faults caused by the disconnection of distribution feeders interconnected to a PV system, many problems can occur depending on the core type and wiring method of the grid-connected transformers. Moreover, open phase faults are difficult to detect because the open phase voltage of the existing protection relay (Open Phase Relay (47)) can be maintained, even though a disconnection fault occurred, depending on the wiring method and the iron core type of the grid-connected transformer for a PV system. Therefore, this paper proposes a novel algorithm to detect open phase faults by comparing the currents and phases between the primary and secondary sides of a grid-connected transformer. In addition, this paper presents the modeling of a distribution system and protection devices for detecting open phase faults using PSCAD/EMTDC S/W, and implements a test protection device for detecting open phase faults based on the above-mentioned modeling. The simulation and test results confirmed that the proposed algorithm is useful for detecting open phase faults according to the wiring method and iron core type of grid-connected transformer for a PV system because operation slope and unbalance rate of the primary current exceed the setting value (30[%]) of the protection device.