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The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor–stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics. [ABSTRACT FROM AUTHOR]

The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance. [ABSTRACT FROM AUTHOR]

In the operation of MEMS gyroscopes, the feedthrough signal is inevitably introduced through the feedthrough capacitance and significantly affects the performance of MEMS gyroscopes. The common feedthrough cancellation methods highly depend on the microfabrication process and the structural symmetry of MEMS gyroscopes, which cannot effectively eliminate the feedthrough signal. In order to solve this problem, a new feedthrough cancellation method based on a fully differential configuration is proposed in this paper. The influence of the feedthrough effect is analyzed by establishing an equivalent circuit model including the feedthrough capacitance and described by the admittance diagram. Different drive and detection configurations are compared for the problem of feedthrough mismatch on different paths. Based on the theoretical analysis, a fully differential feedthrough cancellation scheme combined with two inverse feedthrough cancellation circuits is proposed. The deviation of feedthrough signal caused by the asymmetry of two differential paths can be adjusted by the two inverse feedthrough cancellation circuits. The fully differential configuration can further increase the drive force and suppress the common mode errors. The experimental results indicate that the feedthrough signal is suppressed in a large degree by the designed feedthrough cancellation method. The feedthrough level is reduced by 50.53 dB, and the amplitude of the effective signal increases from 4.10 to 9.46 dB. The signal-to-noise ratio has an improvement of 212.48% than that before feedthrough cancellation. The proposed feedthrough cancellation method can significantly reduce the interference of feedthrough signals on the effective signal, effectively improving the signal quality of MEMS gyroscopes. [ABSTRACT FROM AUTHOR]

A sampling oscilloscope is an important instrument for evaluating the quality of optical communication signals. Since its working principle is equivalent-time sampling, the data obtained for digital signals with random characteristics do not have continuity, which makes it impossible to use methods such as filtering and averaging to equalize the signal. For this reason, this paper proposes a signal equalization method based on reservoir computing. Through training of the reservoir model, an equivalent-time equalizer is established to solve the problem that the sampling oscilloscope cannot equalize random digital signals. Compared with the continuous-time equalizer, the coincidence degree exceeds 95%. The eye height and eye width are increased by 7 and 1.6, respectively, while the jitter in the eye diagram is reduced by 2.3 times, which solves the problem that the sampling oscilloscope cannot equalize random digital signals. [ABSTRACT FROM AUTHOR]

To solve the problem of low quality rate for one-time assembly of high-pressure compressors, an improved genetic algorithm (GA) is used to adjust and optimize the imbalance after assembly. This paper takes the post-assembly imbalance of a multi-stage rotor of a high-pressure compressor as the objective function, to reduce the post-assembly imbalance by adjusting the arrangement order of rotor blades and the assembly phase between rotors. We used a four-sector staggered distribution method to generate high-quality initial populations and added an elite retention strategy. The crossover and mutation probabilities are adaptively adjusted according to the fitness function values. The threshold termination condition is added to make the algorithm converge quickly so as to achieve fast, stable, and efficient search. The simulation results show that the imbalance is reduced by 99.46% by using the improved genetic algorithm, which is better than the traditional GA. The experimental results show that the imbalance of the two correction surfaces can be reduced to 640 and 760 g·mm, respectively, which is 86.7% and 87.1% better than the zero-degree assembly. [ABSTRACT FROM AUTHOR]

The harmonization of international temperature measurements requires the high-accuracy realization of many different temperature reference points. This results from the feature of the intensive measurand temperature that temperatures cannot simply be divided or multiplied. Thus, the points must cover the whole range of interest, at present from 1 mK to a few 1000 K. Furthermore, instruments are necessary for the interpolation between the non-continuous guide values. This led to the establishment of International Temperature Scales (ITS). The ITS prescribe interpolation instruments and assign fixed temperature values to suitable phase transitions without uncertainty. The large temperature range can only be covered by applying very different phase transitions. This includes the classical transitions, namely triple, melting, and freezing points, but also second-order transitions, as superfluid and superconducting ones, and the very new eutectic or peritectic points of metal-carbon compositions. A high-accuracy realization requires a reliable uncertainty estimation. This is, therefore, the central topic of this review. Since a given non-ideal condition of a sample, especially the impurity content, cannot be reproduced as accurate as necessary, the fixed- and reference-point temperatures are defined for ideal substances under ideal conditions. Thus, the estimation of the uncertainty of the realizations must be based on estimating the magnitude of all physical effects influencing the observed phase-transition temperature. The application of this methodology is discussed in the paper as unifying topic independent of the individual problems to be solved. Furthermore, recommendations of the Consultative Committee for Thermometry are summarized, and own experiences are supplemented. [ABSTRACT FROM AUTHOR]

In this paper, an image-based visual servoing (IBVS) controller with a 6 degree-of-freedom robotic manipulator that tracks moving objects is investigated using the proposed Deep Q-Networks and proportional–integral–derivative (DQN-PID) controller. First, the classical IBVS controller and the problem of feature loss and large steady-state error for tracking moving targets are introduced. Then, a DQN-PID based IBVS method is proposed to solve the problem of feature loss and large steady-state error and improve the servo precision, as the existing methods are hard to use for solve the problems. Specifically, the IBVS method is inherited by our controller to build the tracking model, and a value-based reinforcement learning method is proposed as an adaptive law for dynamically tuning the PID parameters in the discrete space, which can track the moving target and keep the servo feature in the field of the camera. Finally, compared with the different existing methods, the DQN-PID based IBVS method has merits of higher accuracy and more stable tracking, or generalization. [ABSTRACT FROM AUTHOR]

Three-dimensional magnetic null points (3D nulls) are sites of dynamic activity in a wide range of naturally-occurring and laboratory plasma environments. The topology of a 3D null is defined by a two-dimensional fan plane of radial field lines and a one-dimensional, collimated spine axis. Here, we build on previous work that was able to form an extended 3D null topology using an assembly of circular conducting coils, with each coil carrying a constant current. While that magnetic field design decayed from the mathematically pure form away from the central null, this paper introduces an algorithm for modulating the current through each coil to form a more mathematically pure spine axis along the entirety of the coil assembly. By the method of solving an inverse problem, we demonstrate that unique currents exist for any arbitrary distribution of axially-aligned circular coils for creating an accurate spine axis in a 3D null topology. Tests of this algorithm are performed on spherical, cylindrical, and cone-shaped coil assemblies. Vector magnetic field mapping of these small-scale demonstrators verifies that an accurate spine axis is maintained along the entire central axis of the coil assemblies. The magnetic field accuracy is roughly maintained along the fan plane but decays strongly toward the outer extents of the coils. The inverse method presented here is not limited to 3D null topologies but can be adapted to match any theoretical form of the magnetic field along a single axis. [ABSTRACT FROM AUTHOR]

In this paper, a measurement system based on stereo photogrammetry is proposed for position and attitude measurements of test models in hypersonic wind tunnel testing. Several adaptations were made to an existing stereo photogrammetry system to promote its routine use as a primary wind tunnel measurement system. First, cameras were installed in the wind tunnel test section to decrease the measurement distance and increase the base length, which helped improve the measurement accuracy. To deal with camera vibration caused by the freestream, a camera orientation method is proposed to estimate the exterior parameters of each frame relative to the tunnel coordinate system. Second, a scheme to determine the model coordinate system is proposed, which does not require placement of the targets at known locations of the model. Third, to solve the recognition problem of motion-blurred target images, a network was adopted for stereo deblurring and a stereo blur dataset simulating the wind tunnel test environment was generated for network training. To develop the novel stereo photogrammetry system, experiments were conducted in both the laboratory and a hypersonic wind tunnel at the China Aerodynamics Research and Development Center. The experimental results suggest that the proposed measurement system is practicable for position and attitude measurements of test models in hypersonic wind tunnel testing. [ABSTRACT FROM AUTHOR]

With the miniaturization of inertial instruments, sensors mounted inside are vulnerable to interference. In a complex thermal transmission environment, temperature drift is the main factor restricting the precision of high-performance inertial sensors. To solve this problem, a new method for compensating the time-related cold starting temperature drift of the inertial sensors is introduced in this paper. Based on the perspective that temperature drift can be regarded as the response curve of the sensor system to temperature and temperature gradient, temperature compensation models of first-order, second-order, and higher-order are proposed. Meanwhile, the particle swarm optimization algorithm is used to solve the model parameters. Under various practical circumstances, the method can be used to flexibly compensate the temperature drift and reduce the standard deviation of the output signal by about four times. Compared to other models or algorithms, the simulation and experimental results indicate that the proposed model is superior in adaptability, stability, and reliability. [ABSTRACT FROM AUTHOR]

The autonomous navigation and positioning system is one of the key equipment in industrial automation production. Due to slippage, the odometer (OD) data cannot reflect the real rotation of the wheeled robot, which may cause the unmanned robot to deviate from the preset route. To solve this problem, the integration of the strap-down inertial navigation system (SINS) and the OD is used, and a novel slipping recognition algorithm based on neural network learning and an error compensation method is proposed to improve the navigation accuracy when skidding. First, the output data of the SINS and OD are studied and features are extracted for neural network learning. After training and modeling, the state of the robot slipping can be identified quickly and effectively. Second, different models are designed to compensate the errors of the different slip patterns. Experimental results show that the comprehensive recognition rate of five modes within 1 s is better than 98.9%. After compensation, the positioning errors of the left and right slip modes have been reduced to 15 mm and the positioning errors of the both side and locked slip modes have been reduced to 40 mm. Compared with before compensation, the errors are reduced to less than 6%. [ABSTRACT FROM AUTHOR]

Hydrogen has a wide range of energy applications, but hydrogen energy systems can suffer from high-concentration leaks that pose security risks, therefore making the measurement of high-concentration hydrogen very important. Traditional ultrasonic gas-detection methods are based mainly on ultrasonic time-of-flight measurements and can be divided into threshold-detection and phase-difference techniques. Threshold detection suffers from low resolution and a complex structure in gas detection, while the phase-difference technique has high resolution and a simple structure but can only measure the time of flight within one period of the ultrasonic signal. In this study, a dual-frequency phase-difference technique is proposed that solves the problem of multi-period phase detection with the phase-difference technique and can be used to detect high-concentration hydrogen. Simulation analysis and an experiment show that the proposed technique can measure the multi-period phase difference accurately. The maximum hydrogen concentration can reach 50% with an uncertainty of less than 5%, which meets commercial requirements. [ABSTRACT FROM AUTHOR]

Valveless piezoelectric pumps usually have less flow than valve piezoelectric pumps, but the valve piezoelectric pumps have some limitations, such as high cost, complex structure, and difficult installation. In order to solve the problem of the low flow rate of the valveless piezoelectric pump, a valveless piezoelectric pump with multi-stage fluid guiding bodies is proposed. Based on the structure and working principle of the piezoelectric pump, the forward energy loss equation is established, and analysis on parameters affecting the energy loss of the pump is then conducted. COMSOL Multiphysics is adopted to construct a two-dimensional model of velocity and von Mises stress distribution. The valveless piezoelectric pumps were then fabricated based on 3D printing technologies, and the prototypes were tested. The results show that the output performance of the pump is the best when the working voltage is 220 V, the frequency is 95 Hz, the length of the oblique arm of the fluid guiding body is 3.5 mm, the spacing is 9.05 mm, and the thickness is 0.1 mm. The maximum flow rate is 520.6 ml/min. In addition, the experimental results prove that the asymmetric fluid guiding body placed in the center of the pump chamber can improve the performance of piezoelectric pumps. This study extends the application of piezoelectric valveless pumps in micromechanical cooling. [ABSTRACT FROM AUTHOR]

Robot grasping has become a very hot research field so that the requirements for robot operation are getting higher and higher. In previous research studies, the use of traditional target detection algorithms for grasping is often very inefficient, and this article is dedicated to improving the deep reinforcement learning algorithm to improve the grasping efficiency and solve the problem of robots dealing with the impact of unknown disturbances on grasping. Using the characteristic that deep reinforcement learning actively explores the unknown environment, a Gaussian parameter Deep Deterministic Policy Gradient (Gaussian-DDPG) algorithm based on the Importance-Weighted Autoencoder (IWAE) is proposed to realize the robot's autonomous learning of the grasping task. Traditional coordinate positioning methods and deep learning methods have poor grasping effects for disturbed situations (such as the movement of the target object). The IWAE algorithm is used to compress the high-dimensional information of the original visual input to the hidden space and pass it to the deep reinforcement learning network as part of the state value. Based on the classic DDPG algorithm, it smoothly adds Gaussian parameters to improve the exploratory nature of the algorithm, dynamically sets the robot grasping space parameters to adapt to the workspace of multiple scales, and finally, realizes the accurate grasping of the robot. Relying on the possible position information deviation of the visual information, the control of the grasping position by the manipulator torque information is further optimized to improve the grasping efficiency of disturbed objects. [ABSTRACT FROM AUTHOR]

We propose an improved point cloud global descriptor for recognition and grasping of similar workpieces. In the industry, different types of workpieces need to be recognized precisely in some intelligent systems. Deep learning requires a lot of preparation work, and it is difficult to adapt to the variety of workpieces. Furthermore, traditional descriptors based on point pairs cannot meet the requirements of identification. To solve this problem, the Outline Viewpoint Feature Histogram (Outline-VFH) descriptor remains part of the recognition ability of the Viewpoint Feature Histogram (VFH) descriptor and contains an extra outline description, which is established based on the oriented bounding box theory. To validate the effectiveness of the proposed descriptor, experiments were conducted on public dataset and some physical workpieces. The results show that the Outline-VFH is much better than VFH and some other descriptors on recognition and has great potential in vision-based robot grasping applications. [ABSTRACT FROM AUTHOR]

A novel spherical multi-degree-of-freedom ultrasonic motor is designed, which can achieve three-degree-of-freedom motion through the cooperative drive of three built-in traveling wave stators. The novel ultrasonic motor has the characteristics of compact structure and high power density. Additionally, in order to solve the problem of impedance mismatch between the ultrasonic motor and the driving power supply, a semi-analytical method of impedance matching based on parallel resonance is proposed according to the impedance characteristics of ultrasonic motor. Through the combination of theoretical derivation and finite element simulation, the ultrasonic motor impedance matching on the impedance circle is analyzed. By this method, the optimum matching inductor of the motor is determined. After matching, the motor obtains a larger vibration amplitude and higher transmission efficiency. Finally, the reliability of the method is verified by simulation and experiment, which provides a reference for further improving the motor performance. [ABSTRACT FROM AUTHOR]

This study proposes a temperature model for the relaxation of magnetic nanoparticles and a phase measurement method under a mixing-frequency excitation field, which can improve the accuracy of temperature measurements in magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, phases at mixing frequencies are used to solve the problem of a delay in the relaxation phase of the magnetic field at a high frequency. This method can improve the signal-to-noise ratio of the response of the magnetic nanoparticles and weaken the phase shift of the detection coils caused by the changes in temperature. The results of experiments show that the proposed method can achieve static temperature measurement error less than 0.1 K and dynamic temperature measurement error less than 0.2 K. [ABSTRACT FROM AUTHOR]

The traditional aeromagnetic compensation model does not consider the negative effects of the gradient of the geomagnetic background field. Especially in emergency aeromagnetic surveys, this gradient effect is obvious and can reduce the performance of magnetic compensation. To solve this problem, we propose a method of reducing the gradient effect of the magnetic sensor for aeromagnetic compensation. A magnetic gradient model of the Earth's background field is established for the aeromagnetic compensation, and the gradient effect is then suppressed using altitude data. An experimental platform was built, and flight tests were conducted. Comparison results show that the adoption of the proposed method increased the improvement ratio (IR) of our compensator on two flights from 20.4 and 21.3 to 25.9 and 26.0, with the growth rates of the IR, respectively, reaching 27% and 22%. [ABSTRACT FROM AUTHOR]

Neutron penumbral imaging is a significant diagnostic technique in laser-driven inertial confinement fusion experiment. It is very important to develop a new reconstruction method to improve the resolution of neutron penumbral imaging. A new nonlinear reconstruction method based on total variation (TV) regularization is proposed in this paper. A TV-norm is used as regularized term to construct a smoothing functional for penumbral image reconstruction in the new method, in this way, the problem of penumbral image reconstruction is transformed to the problem of a functional minimization. In addition, a fixed point iteration scheme is introduced to solve the problem of functional minimization. The numerical experimental results show that, compared to linear reconstruction method based on Wiener filter, the TV regularized nonlinear reconstruction method is beneficial to improve the quality of reconstructed image with better performance of noise smoothing and edge preserving. Meanwhile, it can also obtain the spatial resolution with 5 μm which is higher than the Wiener method. [ABSTRACT FROM AUTHOR]

There is a strong coupling effect between the body and propulsion system of the integrated hypersonic vehicle. The scaled-down model cannot reflect the laws of physics. Therefore, carrying out hypersonic aerodynamic tests using large-scale aircraft models is an urgent difficulty and the key link in the research of hypersonic aircraft. This Note presents a design scheme of large-scale and heavy-load three-component gauge strain balance. The balance innovatively uses the design of a "herringbone" force measuring element and a support plate to ensure the sensitivity of the main channel and solve the problem of serious coupling between channels. In order to solve the problem of insufficient stiffness caused by the large scale of the model, two convex plates are added to the contact surface between the model and the balance. Therefore, the difficulties encountered by large-scale aircraft models in a pulsed wind tunnel can be overcome effectively. The balance is verified by static calibration and wind tunnel test to meet the requirements. The results show that the balance meets the requirements of the aerodynamic load test of the large-scale and heavy-duty hypersonic vehicle model in a pulse combustion wind tunnel. [ABSTRACT FROM AUTHOR]

The magnetic field calculation and correction for a 10 MeV positron emission tomography cyclotron is presented. 3D TOSCA analysis results are compared with the measured data, and the calculation error is used to calibrate the B-H curve to obtain a very precise finite element method estimator, which is used to predict the correction of the magnet pole for achieving the isochronous field. The isochronous field error is approximated with the effects of a set of standard patches. On the assumption that the effect of each small patch is proportional to its surface, the correction of the magnet pole is found by solving a system of equations using the least square scheme. The magnet shimming is performed and the measured magnetic field is found in good agreement with the prediction, with an error less than 2 G. [ABSTRACT FROM AUTHOR]

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to leam the solution to a problem from a set of examples. The majority of practical applications of neural networks currently make use of two basic network models. We describe these models in detail and explain the various techniques used to train them. Next we discuss a number of key issues which must be addressed when applying neural networks to practical problems, and highlight several potential pitfalls. Finally, we survey the various classes of problem which may be addressed using neural networks, and we illustrate them with a variety of successful applications drawn from a range of fields. It is intended that this review should be accessible to readers with no previous knowledge of neural networks, and yet also provide new insights for those already making practical use of these techniques. [ABSTRACT FROM AUTHOR]

Atomic Force Microscopes (AFM) are used for generating surface topography of samples at micro to atomic resolutions. Many commercial AFMs use piezoelectric tube nanopositioners for scanning. Scanning rates of these microscopes are hampered by the presence of low frequency resonant modes. When inadvertently excited, these modes lead to high amplitude mechanical vibrations causing the loss of accuracy, while scanning, and eventually to break down of the tube. Feedback control has been used to damp these resonant modes. Thereby, enabling higher scanning rates. Here, a multivariable controller is designed to damp the first resonant mode along both the x and y axis. Exploiting the inherent symmetry in the piezoelectric tube, the multivariable control design problem is recast as independent single-input single-output (SISO) designs. This in conjunction with integral resonant control is used for damping the first resonant mode. [ABSTRACT FROM AUTHOR]

To predict the performance of flux trapping cascaded flux compression generators, a calculation model based on an equivalent circuit is investigated. The system circuit is analyzed according to its operation characteristics in different steps. Flux conservation coefficients are added to the driving terms of circuit differential equations to account for intrinsic flux losses. To calculate the currents in the circuit by solving the circuit equations, a simple zero-dimensional model is used to calculate the time-varying inductance and dc resistance of the generator. Then a fast computer code is programmed based on this calculation model. As an example, a two-staged flux trapping generator is simulated by using this computer code. Good agreements are achieved by comparing the simulation results with the measurements. Furthermore, it is obvious that this fast calculation model can be easily applied to predict performances of other flux trapping cascaded flux compression generators with complex structures such as conical stator or conical armature sections and so on for design purpose. [ABSTRACT FROM AUTHOR]