The Rapid Upper Limb Assessment method depends mainly on the subjective perception of the assessor, resulting in inconsistent results and a low sensitivity to changes in input variables. In this study, a new scoring s...
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The Rapid Upper Limb Assessment method depends mainly on the subjective perception of the assessor, resulting in inconsistent results and a low sensitivity to changes in input variables. In this study, a new scoring system is developed using the fennec fox optimization algorithm and the Generalized Regression Neural Network approach to overcome the drawbacks of traditional method. First, the deep convolutional neural network was used to identify the keypoints of the human working posture in an image and calculate the joint angle. Second, the new model was used to improve the traditional method, and the prediction results for different postural risk scores were output. The proposed network was trained and tested, and the data were analyzed for comparison. Finally, the correlation between the top 15 predictions in the dataset and the scores was verified. The comparison results show that the proposed method performed better than the other methods in terms of the mean absolute error, mean square error, root-mean-square error, mean absolute percentage error, coefficient of determination, runtime, and spatial complexity. Additionally, the proposed method is more sensitive to small variations in inputs, reducing the likelihood of obtaining the same assessment scores for different postures. This increased sensitivity makes the scoring method more conservative, resulting in a more accurate risk assessment, minimizing potential oversights, and effectively reducing occupational risk. These results underscore the effectiveness of the proposed method in improving the traditional assessment.
A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based...
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A reliable and accurate estimation of the state-of-health (SOH) of lithium batteries is critical to safely operating electric vehicles and other equipment. This paper proposes a state-of-health estimation method based on fennec fox optimization algorithm-mixed extreme learning machine (FFA-MELM). Firstly, health indicators are extracted from lithium-battery-charging data, and grey relational analysis (GRA) is employed to identify highly correlated features with the state-of-health of the battery. Subsequently, a state-of-health estimation model based on mixed extreme learning machine is constructed, and the hyperparameters of the model are optimized using the fennec fox optimization algorithm to improve estimation accuracy and convergence speed. The experimental results demonstrate that the proposed method has significantly improved the accuracy of the state-of-health estimation for lithium batteries compared to the extreme learning machine. Furthermore, it can achieve precise state-of-health estimation results for multiple batteries, even under complex operating conditions and with limited charge/discharge cycle data.
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