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Head Pose Estimation (HPE) has a wide range of applications in computer vision, but still faces challenges: (1) Existing studies commonly use Euler angles or quaternions as pose labels, which may lead to discontinuity problems. (2) HPE does not effectively address regression via rotated matrices. (3) There is a low recognition rate in complex scenes, high computational requirements, etc. This paper presents an improved unconstrained HPE model to address these challenges. First, a rotation matrix form is introduced to solve the problem of unclear rotation labels. Second, a continuous 6D rotation matrix representation is used for efficient and robust direct regression. The RepVGG-A2 lightweight framework is used for feature extraction, and by adding a multi-level feature fusion module and a coordinate attention mechanism with residual connection, to improve the network’s ability to perceive contextual information and pay attention to features. The model’s accuracy was further improved by replacing the network activation function and improving the loss function. Experiments on the BIWI dataset 7:3 dividing the training and test sets show that the average absolute error of HPE for the proposed network model is 2.41. Trained on the dataset 300W_LP and tested on the AFLW2000 and BIWI datasets, the average absolute errors of HPE of the proposed network model are 4.34 and 3.93. The experimental results demonstrate that the improved network has better HPE performance.

Steam coal is the blood of China industry. Forecasting steam coal prices accurately and reliably is of great significance to the stable development of China’s economy. For the predictive model of existing steam coal prices, it is difficult to dig the law of nonlinearity of power coal price data and with poor stability. To address the problems that steam coal price features are highly nonlinear and models lack robustness, Laplacian kernel–log hyperbolic loss–Ridge regression (LK-LC-Ridge-Ensemble) model is proposed, which uses ensemble learning model for steam coal price prediction. First, in each sliding window, two kinds of correlation coefficient are employed to identify the optimal time interval, while the optimal feature set is selected to reduce the data dimension. Second, the Laplace kernel functions are adopted for constructing kernel Ridge regression (LK-Ridge), which boosts the capacity to learn nonlinear laws; the logarithmic loss function is introduced to form the LK-LC-Ridge to enhance the robustness. Finally, the prediction results of each single regression models are utilized to build a results matrix that is input into the meta-model SVR for ensemble learning, which further develops the model performance. Empirical results from three typical steam coal price datasets indicate that the proposed ensemble strategy is reliable for the model performance enhancement. Furthermore, the proposed model outperforms all single primitive models including accuracy of prediction results and robustness of model. Grouping cross-comparison between the different models suggests that the proposed ensemble model is more accurate and robust for steam coal price forecasting.

The TLD-100H readout system performance under various radioactive I-131 exposure doses was optimized by four key factors via the revised Taguchi dynamic quality loss function. Taguchi dynamic analysis and the orthogonal array reorganizing the essential factors are crucial for the optimization of the thermoluminescent dosimeter (TLD) readout system given strict criteria of multiple irradiated environments and long-term exposure for calibrated TLDs. Accordingly, 96 TLD-100H chips were selected and randomly categorized into three batches with eight groups (four TLD chips in each group). Four factors, namely (1) initial temperature, (2) heating rate, (3) maximal temperature, and (4) TLD preheat time before reading were organized into eight combinations according to Taguchi suggestion, whereas each factor was preset at two levels. All 96 $(3\times 8\times 4=96)$ chips were put in three concentric circles with 30, 60, and 90 cm radii for 48 h, surrounding the radioactive 150mCi ($5.55\times 10^3$MBq) I-131 capsule and exposed to the cumulative doses of 88.2, 18.6, and 8.6mSv for the respective radii, accordingly. The TLD readings obtained from each group were analyzed to derive the sensitivity, coincidence, and reproducibility, then those were reorganized to draw four fish-bone-plots for the optimization. The optimal option for the TLD readout system implied the combination of A1 (a $135^{\circ }$C initial temperature), B1 (a $10^{\circ }$C/s heating rate), C1 (a $240^{\circ }$C maximal temperature), and D2 (a 15s preheat time), which was further verified by the follow-up measurements. The dominant factors were A (initial temperature) and B (heating rate), whereas C (maximal temperature) and D (preheat time) were minor and provided negligible contributions to the system performance optimization.

The dynamic recognition of human posture has very broad application prospects in fields such as human–computer interaction and virtual reality. A new method for dynamic recognition of human posture is proposed within the theoretical framework of deep learning. In our method, historical image information of human posture, current image information of human posture, and association information between each image are included as inputs in the deep learning process. Afterwards, the input information is formed into time series information and feature series information, which are then fused by the attention mechanism module. Finally, the dynamic recognition results of human posture are obtained through convolution operation. Experimental research was conducted on the AMASS dataset, and the results showed that our method can achieve better results in dynamic recognition of human posture, with both indicators superior to the other three methods. At the same time, our method has a fast convergence speed and the loss function remains low and continuously decreases during the deep learning process.