• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.35-43
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• 2023

Along with the increase in Multi-unit housing defect disputes, the importance of defect management is also increased. However, previous studies have mostly focused on the Multi-unit housing's 'common part'. In addition, there is a lack of research on the system for the 'management office', which is a part of the subject of defect management. These resulted in the lack of defect management capability of the management office and the deterioration of management quality. Therefore, this paper proposes a machine learning-based defect data management system for management offices. The goal is to solve the inconvenience of management by using Optical Character Recognition (OCR) and Natural Language Processing (NLP) modules. This system converts handwritten defect information into online text via OCR. By using the language model, the defect information is regenerated along with the form specified by the user. Eventually, the generated text is stored in a database and statistical analysis is performed. Through this chain of system, management office is expected to improve its defect management capabilities and support decision-making.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.165-193
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• 2022

It can be said that digital technology is at the center of the change in the modern work environment. In particular, in general public institutions that prove their work with records produced by business management systems and document production systems, the record management system is also the work environment itself. Gimpo City applied for the 2021 public cloud leading project of the National Information Society Agency (NIA) to proactively respond to the 4th industrial revolution technology era and implemented a public cloud-based AI-OCR technology enhancement project with 330 million won in support of 330 million won. Through this, it was converted into data beyond the limitations of non-electronic records limited to search and image viewing that depend on standardized index values. In addition, a 98% recognition rate was realized by applying a new technology called AI-OCR. Since digital technology has been used to improve work efficiency, productivity, development cost, and record management service levels of internal and external users, we would like to share the direction of enhancing expertise in the record management and implementation of work environment innovation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.389-394
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• 2023

In recent years, use of machine automation is rising in the industry. Ships also obtain machine condition information from sensor as digital information. However, on ships, crew members regularly surveil the engine room to check the condition of equipment and their information through analog gauges. This is a time-consuming and tedious process and poses safety risks to the crew while on surveillance. To address this, engine room surveillance using an autonomous mobile robot is being actively explored as a solution because it can reduce time, costs, and the safety risks for crew. Analog gauge reading using an autonomous mobile robot requires digitization for the robot to recognize the gauge value. In this study, image processing techniques were applied to achieve this. Analog gauge images were subjected to image preprocessing to remove noise and highlight their features. The center point, indicator point, minimum value and maximum value of the analog gauge were detected through image processing. Through the straight line connecting these points, the angle from the minimum value to the maximum value and the angle from the minimum value to indicator point were obtained. The obtained angle is digitized as the value currently indicated by the analog gauge through a formula. It was confirmed from the experiments that the digitization of the analog gauge using image processing was successful, indicating the equivalent current value shown by the gauge. When applied to surveillance robots, this algorithm can minimize safety risks and time and opportunity costs of crew members for engine room surveillance.