Accurate control of excavator trajectory is the foundation for the intelligent and unmanned development of excavators. The excavator operation process requires multiple actuators to cooperate to complete the response action. However, the existing control methods to realize a single actuator of the excavator can no longer meet the practical demand. Based on this, a hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) is proposed to tune the proportional integral derivative (PID) controller parameters for enhancing the trajectory control accuracy of excavator actuators. To increase particle randomization and search speed and avoid the local convergence of QPSO, the QPSO is combined with circle chaotic mapping, Gaussian mutation operators, and adaptive adjustment factors, while the linear transformation of the contraction-expansion coefficient (CE) is improved to the dynamic adjustment mode. Through the interface block, a co-simulation platform for the load-sensitive system excavator is constructed, and trajectory experiments of multiple actuator compound actions are carried out. The simulation results show that—compared with ZN-PID, PSO-PID, and QPSO-PID—the trajectory error accuracy of the boom is improved by 26.59%, 32.95%, and 9.44%, respectively, which proves the high control accuracy of HAQPSO-PID in controlling the trajectory of multiple actuators.

Although the measurement of human sizes and the reconstruction of mannequins have been extensively studied, there has been little research on systems applicable for measurement and reconstruction under clothes. This work presents a method for three-dimensional (3D) mannequin reconstruction and human body size measurement based on information fusion of multiple sensors. For this purpose, a multi-sensor information acquisition system (MIAS) is developed allowing one to acquire 11 key size data of the human body under clothes. The MIAS is based on the proposed parametric mannequin method and the Laplace mesh deformation technique, thus allowing one to reconstruct the mannequin conforming to the MIAS measurements. All mannequins reconstructed by this method have the same topology, which will greatly facilitate subsequent processing, such as garment design, customization and virtual fitting. The results of the reconstruction experiments show that our system can reconstruct a mannequin that matches the size of the feature parts very well. In addition, performed comparison experiments with manual and laser measurements shows that the measurement results of our system are close to those of manual and laser measurements. This provides a viable method for measuring key body sizes under clothes and reconstructing the corresponding 3D mannequin.

High-quality data and high-performance models are two critical factors affecting the accuracy of predicting the mechanical properties of strip steel. This study aims to construct a model for predicting the mechanical properties of hot-rolled strip steel with high generalizability and reliability based on metallurgical mechanisms and deep neural networks. Data features at different scales are extracted and ranked in importance to filter out the features affecting the mechanical properties of the steel strips by mechanism analysis. Three neural network prediction models based on LR, LS-SVM, and XGBoost are constructed, and the hyper-parameters of the three models are tuned using the grid search method to achieve the best performance. The prediction results of the three models show that when the prediction error is less than 30 MPa, the accuracy of the mechanical properties prediction model of hot rolled strip steel constructed based on XGBoost reaches 97.4%; when the prediction error is less than 50 MPa, the accuracy reaches 99.4%. The model’s accuracy is higher than other models and has good generalization.