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중국 BIT상 최혜국대우조항의 투자자-국가 간 분쟁해결절차에 적용에 관한 연구
장만,하현수,Zhang, Man,Ha, Hyun-Soo 강원대학교 경영경제연구소 2019 Asia-Pacific Journal of Business Vol.10 No.1
This paper examines the most-favored-nation treatment clause on the BITs concluded by China and examines the attitudes of China on the application of the most-favored-nation treatment clause to the ISDs by period as the scope of arbitration increases. Moreover, this study pointed out the problems that would be exposed if the most-favored-nation treatment clause applies to ISDs and then also suggested solutions. The conclusions of this study are as follows; if the Chinese government strictly restricts the applicable expansion of the most-favored-nation treatment clause to the dispute settlement procedure by considering only the position of the capital importing country, it implies a contradiction against the development trend of the arbitration system related to international investment disputes. Of course, in order to protect the rights of Chinese investors investing abroad, expanding the applicability of the most-favored-nation treatment clause to the ISDs procedure unconditionally may have a negative impact under China's dual status of being a capital-importing country and a capital-exporting country. Therefore, China should clearly define the scope of application of the most-favored-nation treatment clause, the completion of the local remedy for the host country in cases of BIT to be concluded in the future or amended, and also clearly define that the most-favored-nation treatment clause should not be retroactively applied into BITs already concluded as an exception of applicability of the most-favored-nation treatment.
Computer Vision-based Basketball Player Training System
Man Zhang(장만),Seung-Soon Shin(신승수) 한국디지털콘텐츠학회 2024 한국디지털콘텐츠학회논문지 Vol.25 No.3
Computer vision-based basketball player training system is a state-of-the-art field that relies on computer vision technologies and deep learning to help the basketball player train. Computer Vision-Based basketball player training systems rely on extensive training data to detect the players movements and poses; the current training datasets are relatively small and vary in quality, affecting the basketball training effect. This paper proposes the human skeleton detection model, basketball and basketball hoop detection model, basketball shot detection model, basketball hit detection model, and basketball hit prediction model. In the proposed model, the basketball hit detection is improved by up to 9.66% over the existing method. The basketball hit prediction model is improved by up to 10.72% over the existing method using mathematical methods.
Man Zhang(장만),Seung-Soon Shin(신승수) 한국디지털콘텐츠학회 2024 한국디지털콘텐츠학회논문지 Vol.25 No.3
Basketball shooting percentage is an important index used to measure a player’s technical skill. This paper presents a basketball shot prediction model based on a convolutional neural network (CNN) and sensor data, to improve the efficiency and accuracy of basketball shot training. First, three sensors were used to collect player motion data during the shooting process, and the CNN was used to analyze and learn these data. The proposed model achieved 98.5% shooting prediction accuracy, which is higher 13.5% than the existing paper method. Recently, the rapid development of artificial intelligence and sensor technology has led to the emergence of deep learning-based shooting hit prediction models, providing new scientific tools for basketball training.
AI-Based Vehicle Damage Repair Price Estimation System
Man Zhang(장만),Seung-Soo Shin(신승수) 한국디지털콘텐츠학회 2023 한국디지털콘텐츠학회논문지 Vol.24 No.12
Artificial intelligence-based estimation of repair costs for damaged vehicles is an emerging field that relies on artificial intelligence and computer vision systems to automatically generate accurate cost estimates. This area of research is becoming increasingly important owing to its potential to streamline the automotive repair industry, enhance overall transparency, improve the accuracy of cost estimation, and expedite insurance claims processing. This paper proposes the identification of the make and model of a vehicle, classification of the damaged vehicle type, and estimation of repair costs based on prices from various vehicle manufacturers. The proposed method for achieving state-of-the-art performance and time-saving in this system is through the use of ResNet50 and transfer learning. We propose a vehicle make and model classification module as well as a damaged vehicle classification module based on ResNet50 and transfer learning to improve the accuracy of the results. The accuracy of vehicle make and model classification module is 88%, which is approximately 11% higher than that of other studies. The accuracy of damaged vehicle classification module in this study is 86%, which is 67% higher than that of other studies.