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The fault vibration signals extracted from defective bearings are generally non-stationary and non-linear. Besides, such signals are extremely weak and easily buried by inevitable background noise and vibration interferences. Thus, the development of methods capable of detecting their hidden information in a fast and reliable way is of high interest in bearing fault detection. An alternative bearing fault extraction method based on fast iterative filtering decomposition (FIFD) and symmetric difference analytic energy operator (SD-AEO) is proposed in this work. The FIFD method performs excellently in suppressing mode mixing and produce a meaningful decomposition for a higher level of noise. More importantly, unlike other mode decomposition techniques, the FIFD has high computational efficiency, so we can speed up the calculations significantly. After decomposing the signal into a group of intrinsic mode functions (IMFs), a criterion based on the product of kurtosis and permutation entropy (PeEn) is proposed to choose the IMFs embedding richer bearing fault impulses. Subsequently, an enhanced demodulation technique, SD-AEO, is employed to detect the bearing fault signatures from the selected IMF. The simulated and real signals verify the efficiency of the proposed method.

Magnesium (Mg) alloy sheets have attracted considerable attention as one of the most promising lightweight structural materials for weight reduction–oriented industries owing to their excellent properties compared with conventional materials. However, basal-textured Mg alloy sheets exhibit extremely inferior formability at room temperature due to their hexagonal close-packed structure and the limited number of active slip systems. Herein, an innovative warm incremental sheet forming assisted with oil bath heating approach to form difficult-to-form metal materials is proposed. To examine the forming quality of the approach, using springback as an evaluation index, a sequence of tests was conducted while forming AZ31B Mg alloy sheets according to a central composite design including response surface methodology and analysis of variance. The results indicated the approach was able to form Mg alloy sheets with great feasibility. The forming temperature (A), forming angle (B), step depth (C), and sheet thickness (D) are substantial factors that affect the springback, whereas the tool diameter (E) has a much less influential role compared with the individual effects of the other parameters, and the reasons for these results are explained. All the remaining interactive terms are substantial interactive factors except the AB, AE, BE, and CD terms, and a quadratic regression model gives the best fit with a 95% confidence level for springback. It was also indicated from the optimization results that to achieve a minimum springback value, the 166.3 °C forming temperature, 50.4° forming angle, 0.22-mm step depth, 1.18-mm sheet thickness, and 11.5-mm tool diameter should be selected.

Smoking in public places not only brings about some safety hazards, but also does harm to people’s lives, property and living environment. A smoking behavior detection model based on deep learning is trained for the concern of environment and safety. First, a vertical rotation data enhancement method is adopted in the preprocessing stage to extend the dataset and increase the objects of detection. Then, the channel attention module is introduced in backbone network to calibrate the feature response. Finally, added a small target detection layer to the YOLOv5 algorithm. This paper analyzes the network structure of the YOLOv5s, and the model is trained and tested by utilizing the YOLOv5s network. Experimental results show that the mAP value of the algorithm is improved by 5.3% over the original algorithm.

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.

The application of biped robots is always trapped by their high energy consumption. This paper makes a contribution by optimizing the joint torques to decrease the energy consumption without changing the biped gaits. In this work, a constrained quadratic programming (QP) problem for energy optimization is formulated. A neurodynamics-based solver is presented to solve the QP problem. Differing from the existing literatures, the proposed neurodynamics-based energy optimization (NEO) strategy minimizes the energy consumption and guarantees the following three important constraints simultaneously: (i) the force-moment equilibrium equation of biped robots, (ii) frictions applied by each leg on the ground to hold the biped robot without slippage and tipping over, and (iii) physical limits of the motors. Simulations demonstrate that the proposed strategy is effective for energy-efficient biped walking.

Aiming at the virtual movement emulation's design scheme of the jumbolter , the jumbolter's digital model is built by the 3D modeling software PRO/E5.0,the research combined the jumbolter with virtual reality technology. Jumbolter simulation virtual environment was simulated by means of 3 dimentional simulation platform OSG. This study used OSG: : Node as node type to give movements to each part accordingly,then it updated command callback parameter pointer through the OSG: : Node under the class of OSG: : NodeCallback to monitor the jumbolter movement currently. Finally made the operator feel operating vividly in the real environment in the nVisionR9.0 software operated by an external device.