• Tunnel and Underground Space
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• v.32 no.2
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• pp.93-106
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• 2022

This study developed applications that allows truck drivers to record ore production data using smart devices at mine sites and to create a daily work report (operation report) in a PC environment. For this, four operating mines in Korea were selected as study areas, and daily work reports used there were investigated. The information elements included in the daily work report of each mine were analyzed. Because the information to be collected for writing ore production data and format of report are different for each mine, four types of applications were developed for the study areas. Ore production data could be recorded by receiving a signal from a Bluetooth beacon and by operating the application directly by the truck driver. The collected data files are uploaded to the cloud server, and the uploaded data files can be converted into a daily work report using the developed applications in a PC environment.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.457-464
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• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.