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• A method of human key-point coordinate representation based on probability distribution perception. • Addressing issues :key-point representation quantization error, algorithm inconsistency. • This method gives a high level of precision in the experiment. Existing human pose estimation methods have some problems, such as key-point representation quantization error, algorithm inconsistency, and easy-to-introduce unequal quantization error. Addressing these issues, this paper proposes a method of human key-point coordinate representation based on probability distribution perception on a depth convolution neural network. In this method, the perceived probability of the key-point coordinate distribution is extracted by a convolutional neural network, and the cross entropy is formed with the prediction probability distribution as the loss function. Under the constraint of minimizing the Kullback-Leibler (KL) distance between the prediction and the ground truth, the convolution neural network is optimized, and the coordinates of the heat map are finally located. The experimental results show that compared with the traditional method based on a heat map, the proposed method can accelerate the reasoning speed of the model in the COCO training set, and at the same time, it can improve the map by 1.7 [ABSTRACT FROM AUTHOR]

Aiming at the problem that the cement production process is inherently affected by uncertainty, time delay, and strong coupling among variables, this paper proposed a novel soft sensor of free calcium oxide in a cement clinker. The model utilizes a dual-parallel integrated structure with an optimized integration of one-dimensional convolutional neural networks, long and short-term memory networks, graphical neural networks, and extreme gradient boosting. The proposed model can mitigate the risks associated with overfitting while incorporating the strengths of each individual model and excels in extracting both local and global features as well as temporal and spatial characteristics from the original time series data, ensuring its stability. The experimental results demonstrate that this dual-parallel integrated model exhibits superior robustness, predictive accuracy, and generalization capabilities when compared to single models or enhancements made to other deep learning algorithms. [ABSTRACT FROM AUTHOR]