Responsible Data Science Trustworthy AI Fairness in Machine. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data. We evaluate our algorithms through extensive experiments on real datasets. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model.
A multistep approach to preventing wrong site surgery in skin lesion excision.pdf. Minimum Viable Product and Validated Learning.pdf. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. The data supports the marine biologist’s claim because there is insufficient evidence at the level of significance. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps.
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