• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.43-55
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• 2021

Recently, as the domestic job problem has become serious, government ministries are investing a lot of budgets to encourage startups by prospective entrepreneurs. What is important to the success of startups is the recognition of various startup opportunities before starting a startup, and the experience through trial. However, in reality, prospective entrepreneurs recognize and seek various startup opportunities through support such as startup education and initial commercialization funds through various government supported projects, but it is difficult to actually start a business. Previous studies have revealed that the recognition of entrepreneurial opportunities affects entrepreneurial intentions by various variables such as gender, but research is insufficient on what kind of black box exists between the recognition of entrepreneurial opportunities and entrepreneurial intentions. The purpose of this study is to emphasize the importance of positive psychological capital as a major method for improving the entrepreneurial intention, and to analyze exploratorily whether positive psychological capital plays a mediating role between the recognition of entrepreneurial opportunities and the entrepreneurial intention. As a result of the study, it was confirmed that the recognition of startup opportunities affects the intention to start a business, and positive psychological capital has a mediating effect between the recognition of the startup opportunity and the intention to start a business. This means that positive psychological capital is important in order to lead to actual entrepreneurial intentions after recognizing a startup opportunity. Therefore, in order to revitalize the startups of prospective entrepreneurs in the current startup ecosystem, it is necessary to prepare a plan to improve the recognition of startup opportunities and positive psychological capital.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 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 Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 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
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• v.37 no.6
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• pp.723-737
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• 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.