Measurement最新文献

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.

The aim of this study is going to develop a monitoring approach for measuring blood urea concentration. This method uses microwave technology and complementary split-ring resonator (CSRR) to assess dialysis adequacy. The robust electric field near CSRR sides produces a high-sensitivity surface to a change in the nearby urea level. The proposed biosensor detection range spans from 1 to 100 mg/dL by modifying resonance frequency and peak attenuation. The biosensor was developed to operate at the main resonance frequency of 2.4 GHz, but all harmonics within the frequency range of 1–10 GHz were analyzed. A container incorporates the dialysate as the material being tested on the top of the sensor. Accurate measurement of blood urea variations using spent dialysate compared to the blood-based method has benefits due primarily to the lack of repetitive blood sampling. The innovative biosensor offers a non-invasive and convenient method to accurately evaluate the quantitative urea concentration in spent dialysate without the necessity of disposable chemicals. The biosensor has been designed to possess noteworthy attributes such as detecting minuscule concentration variations, low-cost fabrication, compact size, high sensitivity, and its ability to provide real-time results. By merging these features, the sensor demonstrates remarkable potential for non-invasively monitoring blood urea levels while seamlessly integrating with other dialysis devices. The initiative purpose of this study is going to offer renal patients methods for assessing dialysis adequacy with minimal impact on human health.

The classification of LiDAR data has achieved promising performance with deep learning. However, the original point cloud data is inevitably corrupted due to the complexity of remote sensing scenes, sensor aging, and inadequate data processing. Deep neural networks show significant performance degradation when working with corrupted point clouds compared to intact ones. The robustness of these networks against corruption remains understudied. This paper presents a Feature Compensated Local Cross (PointFCLC) attention network for handling point cloud classification under corruption. The critical components of PointFCLC are the Feature Compensation (FC) and Local Cross (LC) modules. The FC module compensates for the spatial offset resulting from mapping; it utilizes high-dimensional features to enhance the spatial properties of the point cloud and mitigate the effects of corruption. The LC module adjusts the distribution of high-dimensional features and position information using cross-attention. The LC module improves the model’s ability to capture spatial information in high-dimensional environments, improving its robustness when dealing with corrupted point clouds. On the ModelNet40, ModelNet-C, ModelNet40-C, and ScanObjectNN datasets, extensive experiments demonstrate that PointFCLC achieves competitive results with respect to robustness. On the corrupted point cloud dataset ModelNet40-C, PointFCLC beats the current state-of-the-art by 1.0% error rate (ER) (21.7% vs 22.7%). Furthermore, we propose a lightweight model called PointFC that maintains competitive robustness (23.4% ER on ModelNet40-C) with the fewest parameters (0.54M) and smallest model size (5.9 MB).

Accurate characterization of spectral gain of C-band EDFAs in WDM system design is crucial; however, traditional methods often require expensive WDM equipment. This study presents a cost-effective alternative, validated through both experimental measurements and simulations, that utilizes a weak signal probe and a carefully selected saturation tone. By comparing results with a 32-channel WDM reference, we demonstrate that the proposed method achieves high accuracy. The optimal saturation tone wavelength depends on the pump wavelength (980 nm or 1480 nm) and configuration (forward or bidirectional). Nevertheless, a good fit with the reference can be obtained in all scenarios, especially at higher saturation levels. This work provides valuable insights for developing affordable and efficient EDFA gain measurement techniques.

The widespread use of light emitting diodes (LEDs) in various applications has necessitated accurate measurement of their photometric parameters, particularly luminous flux. While traditional methods employing integrating spheres offer precise measurements, simpler and often cheaper techniques utilizing radiometric detectors and spectrometers present viable alternatives, especially for directional emissions. This article presents a comprehensive methodology for measuring luminous flux using a simple detector and a spectrometer, discussing equipment calibration, uncertainty analysis, and validation of the described method. The study delves into the intricacies of detector and spectrometer calibration, offering insights for traceability to a known reference standard in lumen measurement practices. Additionally, the article addresses uncertainties associated with luminous flux calculations and validates the proposed method against measurements from an independent laboratory. Limitations such as spectral response variations and temperature effects on measurements are mentioned. Moreover the article encompasses formula derivations and calculations, empowering readers to create and validate their own computation tools.

For the challenges faced by upper limb exoskeleton in meeting human motion intention, this article proposes a novel brain-inspired decision-making model based on multi-brain-region information transmission mechanism and multimodal information fusion method to provide accurate prediction of motion trajectory. Firstly, a multimodal information perception and fusion method is proposed. Based on surface electromyographic (sEMG) signals and angle signals collected by sensors. Then a random forest algorithm is applied to screen the key features of sEMG signal for trajectory prediction. A multimodal information hierarchical fusion method based on dual-period mechanism is designed to solve the problem of fusion defect of information with different frequency. Finally, a multi-brain-region decision-making model based on hybrid hierarchical liquid state machine (HHLSM) is established to predict motion trajectory for the exoskeleton to meet human motion intention. Experiments show that the established model has a high computational efficiency and improves the effectiveness of exoskeleton assistance. The brain-inspired decision-making model based on HHLSM established in this paper is of great significance for further research on brain-inspired control and improving the human-robot interaction effect of upper limb exoskeleton.

High-precision manufacturing of key components in aerospace plays a crucial role in determining their operational performance, and shape measurement during machining is essential for controlling component accuracy. This article proposes a laser in-situ high-precision measurement method based on improved ICP registration during robotic machining to resolve the contradiction between measurement accuracy and range. Firstly, a robotic in-situ measurement system based on line laser scanning is constructed. Subsequently, the FM-BiKD-ICP algorithm based on geometric feature matching and bi-directional KD-tree proposed in this article is used to complete the high-precision registration of multi-million-scale point cloud data of complex parts with large surface area. Finally, the standard ball target ruler is measured, with the RMSE is under 0.079 mm after multiple registrations, and the average error of hole center distance is less than 0.037 mm on large-area complex parts. These results indicate the high precision of the proposed robot in-situ measurement system.

Design of optical fiber structure and sensitive material coating are two effective performance improvement approaches for fiber optic surface plasmon resonance (SPR) sensors. Here, a tungsten diselenide (${\text{WSe}}_2$) modified U-shaped fiber optic SPR sensor is proposed. It is found that the sensing performances of the proposed U-shaped probe can be controlled with the thickness of gold film, ${\text{WSe}}_2$ layers, and bending radius of the U-shaped fiber. A sensitivity of 27520 nm/RIU (RIU: refractive index unit) is obtained with the optimized U-shaped probe, which is about 11.56-fold and 12.29-fold improvements over the traditional in-line transmission fiber optic SPR sensors without ${\text{WSe}}_2$ coating and with monolayer ${\text{WSe}}_2$, respectively. These results demonstrate a promising approach for sensitivity improvement of fiber optic SPR sensors, and the proposed ${\text{WSe}}_2$ modified U-shaped fiber optic SPR sensor may have prospective potential applications in biological sensing, chemical sensing, and environmental monitoring.

Cavitation attracts the interest of engineers who target the better understanding of its complex physics and the development of efficient detection tools. This research explores the effectiveness of vibration-based indicators in the prompt and effective diagnosis of cavitation initiating in the rotating flow fields of turbomachinery. The indicators are derived from the envelope spectra estimated with the use of Hilbert Transform, Spectral Kurtosis and Cyclic Spectral Correlation algorithms and data acquired from two semi-open impellers. By utilizing a transparent casing, the onset of cavitation can be observed, enabling the establishment of reliable criteria for evaluating the new indicators. Also, their applicability is assessed across a wide range of flow-rate and suction pressure conditions and in two different geometries. The results demonstrate the consistent ability of the indicators to exploit the high frequency carrier information related with the resonances excited from bubble implosions, to promptly and efficiently detect the phenomenon.