• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.562-565
• /
• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of The Korean Association For Science Education
• /
• v.28 no.8
• /
• pp.955-963
• /
• 2008

This study was conducted to assess any change in students' views on the nature of science (NOS) after lessons through the activities of model construction and pattern identification. The instrument used to examine NOS views was the Views of Nature of Science questionnaire (VNOS). Four students' responses on VNOS before and after instruction were analyzed. The two levels of their views, novice and expert, were judged by the authors based on criteria set by several science educators. The instruction consisted of six hours of the so-called black box and cube activities developed for model construction and pattern identification, respectively. Students' views were at the novice level in definition of scientific theory, tentativeness of scientific knowledge, difference of hypotheses, theories and laws, model construction, and creativity and imagination in experiments and investigations. Students' views on NOS knowledge such as model and theory have improved for two students after instruction. The improvement seemed to be due to an explicit approach using the activities of model construction and pattern identification. The factors of changes and no-changes of views on NOS were identified and discussed in terms of improvement of the views.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.12
• /
• pp.51-57
• /
• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Journal of Conservation Science
• /
• v.37 no.6
• /
• pp.723-737
• /
• 2021

This study analyzed the composition and structure of materials with Buddhist paper scriptures excavated from architectural sites in 'Sum Tolgoi' of the 17^th century and carried out conservation treatment base on the result of the analysis. The scriptures were covered in dust and foreign sub stances, and were so crumpled that it was impossible to identify the form. The damage, loss, and discoloration have been identified. Buddhist scriptures written in Tibetan used indigo and ink sticks on paper as a result of UV-Vis analysis, and ink sticks as black character materials from scriptures written ancient Mongolian. SEM-EDS and Micro-XRF analyses revealed that the outlines were drawn with red lines using a mix of Minium (Pb₃O₄) and Cinnabar (HgS), or Cinnabar (HgS) alone, and the contents of the scriptures were written with silver paint. Silver chloride (AgCl) and Calcium (Ca) were identified in the silver paint component of the characters, while Calcium and Orpiment (As₂S₃) were identified in the yellow lines. Concerning the paper ground, Buddhist scriptures written in ancient Mongolian were characterized by herbal plant fiber and bast fiber, and those written in Tibetan, by bast fiber. Radiocarbon dating indicates that the paper for the scriptures was produced between the 15^th and 17^th centuries. Conservation treatment of the scriptures was carried out based on the experiment on the production of pre-coated paper and how to coat that to prevent the second damage due to the deformation and fragility of the excavated paper. The scriptures were preserved and mounted, and a neutral box was made to identify the contents of the scriptures recorded on both sides after the treatment. This conservation treatment is the result of a study that applied new conservation treatment materials and methods according to the principle of conservation treatment reversibility.