• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.121-131
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• 2009

BcN provides a wide variety of high-quality multimedia services such as broadcasting and communication convergence services. But the quality degeneration is observed in BcN when we use broadcasting and communication convergence service via more than one network of different internet service providers. In this paper, a QoS management system which is able to measure and maintain objectively the quality-related information in overall networks is proposed. The proposed QoS management system is tested on the pilot networks of BcN consortiums by measuring the quality of voice and video experienced by the actual users of the commercial video phone services. The result of the experiment shows that it is possible to figure out service qualify between a user and a service provider by analyzing the information from agents. The per-service traffic information collected by probes is proved to be useful to pinpoint the party responsible for the loss of the service qualify in case of the services including different service providers. As the result of the experiment, it is shown that the proposed QoS management system would play a key role of resolving the quality dispute, which is one of the important issues of QoS-guaranteed BcN.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of The Korean Ophthalmological Society
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• v.58 no.6
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• pp.706-711
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• 2017

Purpose: To analyze blinking patterns when watching an ultra-high definition (UHD) television and to compare the results between normal eyes and dry eyes. Methods: A total of 59 participants aged from 13 to 69 years were instructed to watch a colorful and dynamic video on a UHD television for 10 minutes. Before and after watching the UHD television, we measured the best corrected visual acuities, autorefraction, tear-break-up-time, degree of corneal erosion and conjunctival hyperemia via slit lamp biomicroscopy. In addition, questionnaires for the evaluation of eye fatigue and symptoms of a dry eye were completed. The definition of dry eye syndrome was that the tear-break-up-time of one of the eyes was less than 5 seconds, conjunctival injection, or marked corneal erosion. The number of blinks and the duration of blinking were both measured and analyzed at the early and late phases of video-watching. Results: After watching the UHD television in the normal eye group, the tear-break-up-time was significantly decreased (p < 0.001) and the degree of corneal erosion was significantly increased (p = 0.023). However, the subjective symptoms of participants were not aggravated (p = 0.080). There were no significant differences in blinking patterns in the dry eye group. On the other hand, in the normal eye group, the mean blinking time was significantly increased (p = 0.030). Conclusions: Watching an UHD television changes the tear-break-up-time, degree of corneal erosion, and blinking pattern in normal eyes, which may increase the risk of dry eye syndrome.