Analyzing Imprecise Data from Wireless Temperature Sensor
Usama Afzal · Muhammad Aslam · Muhammad Ahmed Shehzad · Florentin Smarandache
Infinite Study
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Various sensors play an important role in the monitoring and development of robotic technology. We present a contemporary statistical analysis method for evaluating datasets generated by robotic systems. Specifically, this data set originates from the physical structure of the robot and is acquired by a wireless temperature sensor. The data collection process spans a temporal period of 1 to 10 hours during the operational period of the robot. The collected data is subjected to a rigorous analysis using neutrosophic methodology. To facilitate this, a modern neutrosophic formula has been devised, drawing on definitions established within the field. To benchmark the effectiveness of the proposed approach, a conventional formula is also used for comparison purposes. Our results clearly indicate that the proposed method achieves higher levels of information richness and adaptability compared to classical methods. This indicates the improved utility of the proposed approach in solving the complexities of robotic data analysis.
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