• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.242-248
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• 2019

This study was conducted to examine the effects of a smart home (electronic control unit, ECU) on the performance and satisfaction of activities of daily living of wheelchair users. A total of 15 wheelchair users (10 patients with spinal cord injury and 5 patients with stroke) were investigated. Smart homes were equipped with ECU technology, which consisted of automation of furniture and products. The products and facilities were integrated and controlled by a smart device or voice. Performance and satisfaction of activities of daily living were measured by the Canadian Occupational Performance Measure (COPM) before and after residence in a smart home. All participants showed a higher COPM (performance score ${\geq}3$, satisfaction score ${\geq}4$) during residence in a smart home compared to residence in the current home. In addition, the COPM scores differed significantly before and after residence in a smart home. These results provide evidence of the applicability of smart homes based on high technology. However, additional studies of more smart home participants should be conducted to improve the quality of the results.