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Motion tracking method is being issued as essential part of the entertainment, medical, sports, education and industry with the development of 3-D virtual reality. Virtual human character in the digital animation and game application has been controlled by interfacing devices; mouse, joysticks, midi-slider, and so on. Those devices could not enable virtual human character to move smoothly and naturally. Furthermore, high-end human motion capture systems in commercial market are expensive and complicated. In this paper, we proposed a practical and fast motion capturing system consisting of optic sensors, and linked the data with 3-D game character with real time. The prototype experiment setup is successfully applied to a boxing game which requires very fast movement of human character
Motion tracking method is being issued as essential part of the entertainment, medical, sports, education and industry with the development of 3-D virtual reality. Virtual human character in the digital animation and game application has been controlled by interfacing devices; mouse, joysticks, midi-slider, and so on. Those devices could not enable virtual human character to move smoothly and naturally. Furthermore, high-end human motion capture systems in commercial market are expensive and complicated. In this paper, we proposed a practical and fast motion capturing system consisting of optic sensors, and linked the data with 3-D game character with real time. The prototype experiment setup is successfully applied to a boxing game which requires very fast movement of human character
In India, human error and negligence are now the main causes of train accidents. The aim of the paper is to eliminate train crashes through the use of surveillance. An automatic surveillance system is fitted in every locomotive. The locomotive's internal surveillance system reads the distinct track numbers that are assigned to each segment of the railway network's train lines. This track number will be shared with neighbouring trains by the surveillance system via radio frequency communication. Subsequently, the system's track number is cross-referenced with the track numbers of adjacent trains. In order to halt the train and avoid accidents, the surveillance system acts to notify the concerned motorman of the same track numbers. A specific technique for numbering train tracks segment by segment is recommended by the study. In order to guarantee data flow between the systems' radio frequency transceivers operating in half duplex mode, a communication protocol is also suggested. Because they can move a lot of people and cargo at once, railways constitute an efficient mode of transportation. At either end of the branch track, Wireless Monitoring Units (WMUs), also known as nodes, are placed to allow for the detection of train arrival and departure times for that particular branch
Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.
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