Measurement最新文献

In this paper, a dual-core fiber optic sensor has been proposed for dynamic monitoring of temperature and humidity. The side core is polished into a D-type optical fiber, and a layer of nano-silver film and a layer of polydimethylsiloxane (PDMS) are deposited to construct a surface plasmon resonance (SPR) temperature compensator. The middle core is constructed as the long-period fiber grating (LPFG) sensing channel. The mode conversion of LPFG is achieved by TiO₂ film, and its refractive index (RI) sensitivity is increased by about 4 times. Meanwhile, a layer of moisture sensitive MIL-96(Al) film is grown outside the TiO₂ film. Due to the particular pore structure for water molecules, it is possible to change the effective RI of MIL-96(Al) film by absorbing water molecules. Research has shown that the LPFG sensor can achieve a relative humidity sensitivity of −0.411 nm/%RH. Finally, the sensor successfully achieved real-time dynamic monitoring of respiratory rate.

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and $10^{-4}$ relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

Airborne synthetic aperture radar (SAR) has the advantages of flexible scanning geometry and high spatial resolution compared with spaceborne SAR. However, the quality and positioning accuracy are affected by platform instability caused by air turbulence and other factors. In this paper, the sources of geometric positioning error in airborne SAR were analyzed, including platform position error, time delay error and tropospheric delay error. To improve the positioning quality of airborne SAR, an imaging parameters calibration model considering velocity constraints (IFP-VC) was proposed to rectify the orbit parameters and enable geometric calibration to improve the positioning accuracy. The proposed IFP-VC model was applied to improve the positioning accuracy of airborne SAR images acquired in Hainan and Rizhao, China. The IFP-VC model demonstrated good positioning performance when using measured ground control points (GCPs) in Hainan, achieving a positioning accuracy of 1.42 m. In Rizhao, the proposed IFP-VC model yielded a positioning accuracy of 7.16–12.71 m and 2.05–6 m higher than the slant range and time calibration model and the unconstrained model when using manually selected GCPs, respectively. The multiple images combination strategy was superior to the single image strategy, and the initial positioning accuracy of 930 m was improved from 14.50 m using a single image to 12.47 m using 5 images. When applying the imaging parameters calibration results to the images in different tracks, the positioning accuracy can be improved to 31 m compared with the initial positioning accuracy. The geometric calibration of airborne SAR images has the potential to enhance its applications in remote sensing mapping, land-use change monitoring and ground target detection.

With the miniaturization of quantum sensors, the fabrication of alkali metal vapor cells based on micro-electromechanical systems (MEMS) has become increasingly important. However, the fabrication of MEMS vapor cells remains a challenge. In this study, a microfabricated millimeter-level vapor cell was fabricated and tested. The silicon cavity was fabricated by dry etching and chemical polishing. After injection of alkali metal and buffer gas, a two-step low-temperature anodic bonding was employed. The first step was non-isothermal to keep the alkali metal in the low-temperature pole plate and the second step was isothermal to improve the bonding strength, enhancing the hermeticity. Then, the vapor cell was tested for sidewall roughness, bonding strength, and leakage rate. Subsequently, Rb D1 line absorption spectroscopy, stability, and the single-beam magnetometer signal were tested. The results show the fabricated vapor cell had high stability, providing a basis for the miniaturization of quantum devices.

The incorporation of Artificial Intelligence (AI) is pivotal in automating intricate technical tasks, significantly enhancing accuracy and efficiency while alleviating the burdens of repetitive monitoring traditionally borne by technicians. This study focuses on developing a customized four-probe station integrated with sophisticated AI models aimed at classifying current–voltage ($I-V$) characteristics and extracting essential parameters. Our methodology encompasses the fabrication of precision-engineered gold-plated probes, meticulously assembled with a three-dimensional (3D) moving head to ensure optimal contact and measurement fidelity across a variety of electronic and optoelectronic devices. Data acquisition is executed via a source meter unit, followed by rigorous post-processing utilizing advanced algorithms, including convolutional neural networks and random forest techniques. Notably, the gold-plated contacts enhance measurement accuracy by providing superior conductivity and minimizing contact resistance, while the movable head allows for dynamic adjustment, facilitating precise probe alignment for consistent data retrieval. The results demonstrate a remarkable capability in classifying $I-V$ characteristics with a root-mean-square (RMS) error of less than 1%, underscoring the system’s reliability and accuracy. Moreover, our predictive models effectively utilize previously recorded measurements to forecast the degradation profiles of devices, thus offering significant insights into device longevity and performance.

We report a novel speech recognition method using a noise-robust acoustic sensor system that integrates a spatially frequency-separating sensor with a nonlinear amplification algorithm, mimicking the cochlea’s basilar membrane and hair cells. The multichannel piezoelectric artificial basilar membrane (ABM) sensor detects specific sound frequencies with high sensitivity over 0.2–6 kHz. The signal processing model of the Artificial Hair Cell inspired by the signal transduction mechanism of inner hair cells, simultaneously enhances the frequency selectivity of ABM sensors and improves noise robustness. In a 0 dB SNR noisy environment, it effectively detected the voice signal with a maximum SNR of 57 dB. Furthermore, we converted the frequency-separated signals for speech sounds in various noisy environments into heatmap images and utilized them as input for a CNN-based speech recognition algorithm. Consequently, our system demonstrated noise-robust recognition performance with 94 % accuracy, even in noisy environments.

This research focuses on developing a denoising filter that effectively enhances subtle non-stationarities within signals. Initially, the spectral kurtosis has been calculated from each frequency bin of the spectrogram (setting initial values) which is further optimized by the flow direction algorithm (FDA) to obtain a vector of optimized spectral kurtosis as the classical spectral kurtosis as a data-driven estimator may be not optimal in the sense of informative signal extraction for some specific signals with complex time–frequency structure. Due to this, the vectors obtained from optimized spectral kurtosis for a given frequency bin could be even zero if they do not support the impulsiveness of the output signal. In this way, it allows not to use any thresholding that is always complicated. The vector of the optimized spectral kurtosis thus obtained has been used to design the filter that can extract the impulsive components from the signal.

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

This study presents composite resistive soft tactile sensors combining conductive fabric and polydimethylsiloxane. The sensors utilize the changes in resistance of the conductive fabric when stretched to sense the pressure, while reducing the volatility of resistance of the conductive fabric by embedding the fabric within the elastomer matrix. The sensors have a compact design, a simple fabrication process using cost-effective materials, and a compliant structure. The performance evaluation of 12 samples was conducted to assess their linearity, sensitivity, time responses, and reliability. A particular sample was chosen for its universal applicability, characterized by a rise time of ∼ 0.38 s, a decay time of ∼ 2.71 s, a settling time of ∼ 851.8 s, hysteresis of 21.04 %, and a sensitivity of 0.0362 %/kPa. Finally, potential applications were proposed using the selected sensor, showcasing its capability to support closed-loop systems in the fields of soft robotics and wearable technology.

Identifying the coal-gangue mixing ratio during top coal caving is essential for automating the coal caving process efficiently. In this article, a block impression reconstruction method is proposed to create 3D models of coal-gangue mixtures with varying gangue ratios and morphological distributions, based on real coal-gangue blocks that reflect actual coal-falling conditions. These 3D models are then input into CST software for electromagnetic forward simulation. The relationship between electromagnetic signal propagation characteristics and gangue ratio is analyzed, resulting in the creation of a coal-gangue mixture electromagnetic signal dataset. An Optuna-XGBoost-based model is then designed to identify the coal-gangue mixing ratio and the recognition performance is firstly verified by using the electromagnetic forward simulation data. Finally, to verify the method’s practicality, a microwave detection test bench for top coal caving is set up and some comparative experiments are conducted. The experimental results indicate that the electromagnetic signals of coal and rock with different gangue contents exhibit significant differences, and the proposed coal-gangue identification model has significant advantages in accuracy and overall performance compared to other competing models.