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In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method, the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter (EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m. Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

The induction magnetometers are widely applied for magnetotelluric detection due to the characteristics of wide frequency band, large detection depth range and small size. However, the key part of the induction magnetometers – the magnetic core has eddy current loss and hysteresis loss, which significantly affects the sensitivity of the induction magnetometers. In order to improve the sensitivity of the induction magnetometers at high frequencies, this paper investigates various parameters related to the performance of the induction magnetometers working at 10 kHz to 100 kHz. Moreover, optimization method is proposed to realize the development of a wide-band, high-sensitivity, and low-noise induction magnetometer. First of all, the parameters related to the sensitivity of the sensor are investigated according to the law of electromagnetic induction. A three-dimensional finite element (3D-FE) simulation model was established to study the influence of various parameters of induction magnetometers. In addition, an analysis method combining orthogonal experiment and response surface method is adopted to reduce the quantity of computations and improve the efficiency of analysis. The orthogonal experiment is able to obtain preliminary optimal parameters with only a small amount of computation results. Based on the results of the orthogonal experiment, the response surface method is used to illustrate the relationship between the sensor parameters and losses, and hence the optimal sensor parameters can be obtained. Finally, the model is verified by other sets of simulations, and the results show the regression coefficient of the model $R^{2}=0.9735$ , indicating the effectiveness of the proposed model.

Coiled tubing technology is widely used in drilling and repairing operations, and the quality of bias weld directly determines the fatigue life of the coiled tubing. The microstructures consistency between the bias weld and the base metal determines that whether the fatigue resistance of the weld reaches the level of the base metal. In this paper, multi-frequency eddy current testing technique is used to detect the reactance of the bias weld zone and the base material zone within coiled tubing steel strip, which can be used to determine the microstructure consistency between the base metal area and the weld area, and in turn to improve the welding technology and heat treatment process. In this work, CT100 and CT90 materials with the bias weld for coiled tubing were used as the test samples, and the on-line real-time non-destructive testing technology was used to judge whether welding technology and heat treatment technology have been improved. The basis for determination is whether the mechanical properties and fatigue properties of the welding site reach the base material level. The weld zone and base metal zone can be identified by analyzing the characteristic cloud map of the detection signal, and then the quality of partial weld can be evaluated according to the difference of microstructure between them, which is of guiding significance for quality control and improvement of coiled tubing production process.

Gait analysis has been proved to be a powerful and efficient means for health monitoring. Variety of nervous system diseases and emergencies can be detected by interpreting plantar stress distributions. Among gait analysis techniques, piezoelectric insole architectures receive boosting attentions due to its convenience for users to wear and its long-term and real-time monitoring ability. However, the complex integration of piezoelectric insole architecture limits its successful use for massive production for the Internet-of-health things (IoHT). Hence, in this article, we present a flexible printed circuit board and lamination-associated technique, which presents high detection sensitivity at 0.1 N, satisfying the need for assisting nervous system disease diagnosis, and showing strong potential for commercialization.

Gait analysis has become a hot spot in recent years, because it is proven that the status of a vast number of chronic diseases can be reflected by changes in gait. Furthermore, gait analysis can also help in improving the performance of athletes. Among the diverse gait analysis techniques, the piezoelectric-based insole technique has received broad attention due to its merits such as passive detection, high sensitivity, and low power consumption. However, the key coefficient of detecting plantar normal stress, the piezoelectric d 33 coefficient, relies on the force frequency, which occupies a relatively wide bandwidth (1 Hz–1 kHz) during walking events. In order to get the frequency information of the signal, in this work, empirical mode decomposition is used to separate the gait signal into several intrinsic mode functions, and then the frequency information of each function is interpreted using the normalized Hilbert transform. In this way, the piezoelectric d 33 coefficient is calibrated at every moment, obtaining higher accuracy (2.65% maximum improvement) in gait signal detection, promoting the development of gait analysis–based disease diagnosis and treatment.

This paper presents a review on the biomedical applications of electromagnetic detection in recent years. First of all, the thermal, non-thermal, and cumulative thermal effects of electromagnetic field on organism and their biological mechanisms are introduced. According to the electromagnetic biological theory, the main parameters affecting electromagnetic biological effects are frequency and intensity. This review subsequently makes a brief review about the related biomedical application of electromagnetic detection and biosensors using frequency as a clue, such as health monitoring, food preservation, and disease treatment. In addition, electromagnetic detection in combination with machine learning (ML) technology has been used in clinical diagnosis because of its powerful feature extraction capabilities. Therefore, the relevant research involving the application of ML technology to electromagnetic medical images are summarized. Finally, the future development to electromagnetic detection for biomedical applications are presented.

Titanium alloy is widely used in the area of aerospace and aviation due to its excellent properties. Eddy current testing (ECT) is among the most extensively used non-destructive techniques for titanium alloy material inspection. However, most previous research has focused on inspecting defects far from the edge of the material. It is a challenging task for edge crack detection because of edge effect. This study aims to investigate the influences of sensor parameters on edge effect and defect detection capability, and in the meantime, optimize sensor parameters to improve the capability of edge defect detection. The simulation method for edge effect evaluation is proposed including the 2k factorial design used for factor screening, and the regression model is fitted and validated for sensor design and optimization for edge defect detection. A simulation scheme is designed to investigate the defect detection capability. An approach comprehensively analyzing the influence of coil parameters on edge effect and defect detection capability is applied to determine the optimal coil parameters for edge defect detection.

In this paper, a novel method for accelerating eddy currents calculation on a cell model using the finite element method (FEM) is presented. Due to the tiny thickness of cell membrane, a full-mesh cell model requires a large number of mesh elements and hence intensive computation resources and long time. In this paper, an acceleration method is proposed to reduce the number of mesh elements and therefore reduce the computing time. It is based on the principle of replacing the thin cell membrane with an equivalent thicker structure. The method can reduce the number of mesh elements to 23% and the computational time to 17%, with an error of less than 1%. The method was verified using 2D and 3D finite element methods and can potentially be extended to other thin shell structures. The simulation results were validated by measurement and analytical results.

A triboelectric nanogenerator-based self-powered resonant sensor is proposed and investigated. By integrating an inductor and a microswitch with a triboelectric nanogenerator, a new type triboelectric nanogenerator is obtained, the pulse voltage output is converted to an oscillating signal with a very stable modulated resonant frequency, immune to the cross disturbance of contact-related variation (force, frequency, distance) and environmental variation, such as humidity and temperature. This is utilized for non-destructive defect detection. When the coil inductor scans the surface of a specimen with defects, varying resonant frequencies are obtained for different types of defects, showing excellent consistency between the experimental and simulated results. The results demonstrate the potential of the self-powered TENG-based resonant sensor to be a highly stable and sensitive magnetic sensor for the non-destructive defect detection applications.

Previous methods for modelling Rayleigh waves produced by a meander-line-coil electromagnetic acoustic transducer (EMAT) consisted mostly of two-dimensional (2D) simulations that focussed on the vertical plane of the material. This paper presents a pseudo-three-dimensional (3D) model that extends the simulation space to both vertical and horizontal planes. For the vertical plane, we combines analytical and finite-difference time-domain (FDTD) methods to model Rayleigh waves’ propagation within an aluminium plate and their scattering behaviours by cracks. For the horizontal surface plane, we employ an analytical method to investigate the radiation pattern of Rayleigh waves at various depths. The experimental results suggest that the models and the modelling techniques are valid.

Metallic samples are widely applied in modern industrial production. Due to non-uniformities in the stress load, such samples may become damaged and produce defects, which can cause unnecessary economic losses. In this paper, an online defect detection method is proposed for the quality monitoring of metallic plates. The research involves the design and optimisation of an electromagnetic tomography (EMT) sensor and the development of a fast tomography algorithm. Specifically, a planar array eddy current sensor is designed for in-situ structural health monitoring of metallic specimens. The parameters of the sensor are optimised using an orthogonal methodology and a response surface methodology to improve the uniformity of the sensitivity field. In addition, a second-order iterative Bregman reconstruction algorithm is investigated to reconstruct the defect image, which can improve the reconstruction speed for this ill-posed problem. Simulation and experimental results indicate that the proposed method can be applied to effectively evaluate the locations and sizes of defects in metallic specimens. The correlation coefficients of the reconstructed images using the proposed method are larger than 0.8. Compared with traditional reconstruction algorithms, the method proposed in this paper shows fast convergence speed and smaller estimation errors. [ABSTRACT FROM AUTHOR]