• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.327-335
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• 2021

The purpose of this study is to find out the use and inconvenience of informatization devices according to the disability of the elderly. A total 10,097 data from the elderly survey were used to analyze the differences in the possession and use of information devices, usage capabilities, and injustice according to disability. Descriptive analysis was conducted to find out general characteristics and disability-related characteristics. chi-square tests were conducted to find out the difference in informatization and communication device usage ability and information device usage inconvenience according to disability. The p-value was set at 0.001. As a result of the study, the elderly with disabilities in Korea had lower smartphones and tablet PCs than the non-disabled elderly. In terms of daily inconvenience, the elderly with disabilities often responded that they had no experience or did not know about the inconvenience of online reservations for trains/high-speed buses/intercity buses, ordering kiosks at restaurants, using ATMs, or reducing bank stores. Taken together, the ability of the elderly with disabilities in Korea to possess and use informatization devices is very low and they feel more inconvenience than the non-disabled elderly. It is necessary to improve the ability to possess and use informatization devices for the elderly with disabilities in the rapidly progressing aging of the disabled and the informatization of our society.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.250-257
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• 2019

Recently, 357 water-friendly space were established near the main streams of the country through the Four Major Rivers Project, which was used as a resting and leisure space for the citizens, and the river environment and ecological health were improved. We are working hard to reduce the number of points and plan and manage the water-friendly space. In particular, attempts are being made to utilize mobile big data to make more scientific and systematic research on the number of users. However, when using mobile big data compared to the existing method of conducting field surveys, it is possible to easily identify spatial user movement patterns, but it is different from the actual amount of use, so various verifications are required to solve this problem. Therefore, this study evaluated the accuracy of estimating the number of users using mobile big data by comparing the number of visitors using mobile big data and the number of visitors using drone for Samrak ecological park located in the mouth of Nakdong River. As a result, in the river hydrophilic district, it was difficult to accurately estimating the usage pattern of each facility due to the low precision of pCELL, and it was confirmed that the usage patterns in the park could be distorted due to the signals stopped at roads and parking lots. Therefore, it is necessary to improve the number of pCELLs in the water-friendly space and to estimate the number of visitors excluding facilities such as roads and parking lots in future mobile big data processing.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.