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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.

This paper introduces an innovative warm incremental sheet forming approach with global heating through using an oil bath set-up as a heat source to form Mg alloy sheets or other hard-to-form metallic materials. To examine the effectiveness of the developed oil bath heating approach and investigate the forming ability of incremental sheet forming, a series of tests have been conducted while forming AZ31B Mg alloys according to Box-Bhenken design including response surface methodology and analysis of variance. The experimental results indicated that the most influential factor affecting the maximum forming angle is the forming temperature followed with vertical step depth and then sheet thickness.

A decentralized adaptive control based on human linguistic is investigated to learn human behaviors for multiple robotic manipulators. Many experts’ words or sentences can be transferred into the control actions by employing membership functions in robot systems, which can be synthesized fuzzy controller by employing reasoning mechanism. For the unknown model dynamical robot manipulators, one adjustable parameter that relates to the approximation accuracy of fuzzy logic systems is introduced at first, which be utilized to deal with the unknown dynamics of robot manipulators. Switching fuzzy adaptive controller is designed to overcome the limitation of logic structure that the number of adaptive laws only focus on fuzzy rules in conventional fuzzy logic systems. Another advantage of this design method is that the control with human linguistic extend the semi-global stability to global stability. Finally, effectiveness of the developed control design scheme has been shown in simulation example.

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.

The noble metal nanoparticles-based colorimetric detection can simply determine the concentration of the substances by naked eyes, but difficultly resolve the conflict between sensitivity and anti-interference ability. Herein, a triangular silver nanoplates (TSNPs)-based anti-etching colorimetric detection method for analyzing thiram was proposed by regulating the antagonistic action between the protection of thiram and the etching of I− on TSNPs. The internal mechanism of etching induced peak shift of TSNPs was revealed by the discrete dipole approximation method. The effect of thiram and I− on the color of TSNPs was systematically studied. As a consequence, this method not only shows a limit of detection of 19.7 nM in the linear range of 0.025−0.35 μM by absorption spectroscopy, but also produces sensitive color changes for the detection by naked eyes. Especially, it shows good selectivity that can resist the interference of other substances including other pesticides and halide ions in the specimens. It also shows excellent applicability in the detection of thiram residues in soil. This work provides a potential device-free strategy for the application of colorimetric detection in specimens with lots of interfering substances.

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.

This paper introduces an innovative warm incremental sheet forming approach with global heating through an oil bath setup as a heat source to form Mg alloy sheets. An attempt was made to evaluate and optimize the formability of the proposed approach using the maximum forming angle as a measure of process viability. Then, a series of experiments were conducted according to a Box-Bhenken design, including response surface methodology and an analysis of variance. Next, the results indicated the innovative approach was able to form AZ31B Mg alloy sheets with great feasibility. The most influential factor affecting the formability was the forming temperature, followed by the step depth, sheet thickness and tool diameter. The reasons for those results were also explained. Finally, the experiments validated the predictive model by evaluating an optimal combination of considered parameters for improving the formability of the AZ31B Mg alloys.

The estimation of direction-of-arrival (DOA) using sensor array has been widely used in radar, wireless communication systems and navigation, the vector sensor array can make full use of the polarization information of the impinging sources. This paper presents a quaternion-based ESPRIT method to estimate arrival angles and polarization parameters, the corresponding data model of array signal processing is also established by using the quaternion representation. This procedure of estimating the array steering vectors are realized via the eigenvalue decomposition of a quaternion covariance matrix. The quaternion method can better maintain the quaternion vector characteristics, which is very suitable for vector sensor structure. The results of simulation indicate that the performance of the proposed quaternion method has good performance. This method is applicable to the incomplete electromagnetic vector sensor, which extracts two or more (but less than six) components from the six component electromagnetic vector sensor.

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.

In reverse technology, the key step in front of importing model is the data preprocessing. The purpose of de-nosing is to remove the needless curved surface on the three-dimension (3-D) model by scanning. The stereo lithography (STL) data obtained by three-dimensional scanning will be leaded into MATLAB by writing a program, and then a triangular grid pattern can be got. Using the improved subdivision wavelet, the reconstitution and de-noising of the multi-resolution grid can be completed. To import the three-dimensional figure data into SolidWorks, and a better surface is constituted. In comparison with and research the new graph with the previous that has noise and the model after Geomagic Studio, this method has a better effect.

In this paper, the master-slave synchronization of chaotic Lur'e systems is presented by means of quantization measurement of synchronization errors. The quantizer adopted takes finitely many values, which possess a common adjustable time-varying parameter. By using the adaptive laws for the time-varying parameter, and estimating boundary error values of quantization, a new adaptive quantized state controller design method with the quantized state measurements is proposed for chaotic Lur'e systems. Finally, numerical example is given to demonstrate the effectiveness of the proposed the method.

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.