Sensors最新文献

The effective knowledge of emissivity is pivotal to obtain reliable temperature measurements through non-contact techniques like pyrometry and thermal imaging. This is fundamental in high-temperature applications since material emissivity strongly depends on temperature conditions. Given the recent attention in high-temperature applications, especially for replacing fossil-fuel-dependent heating with greener solutions in energy-intensive processes, renewed interest in characterizing materials radiant properties rose. This work presents a measurement procedure for characterizing the total emissivity of high-emissivity materials exploiting microwaves for heating the test material. The procedure grounds on a sequential approach, using a reference material of known emissivity (e.g., high-emissivity coating, already characterized sample holder, etc.) to derive the target material total emissivity. Uncertainty analysis is performed to provide a metrological characterization of the approach. The procedure is validated on target materials of known emissivity, focusing on high-emissivity materials commonly employed in microwave heating processes. Results are compatible with reference literature and material datasheets, demonstrating the validity of the proposed approach.

Terrestrial laser scanners (TLS) are portable dimensional measurement instruments used to obtain 3D point clouds of objects in a scene. While TLSs do not require the use of cooperative targets, they are sometimes placed in a scene to fuse or compare data from different instruments or data from the same instrument but from different positions. A contrast target is an example of such a target; it consists of alternating black/white squares that can be printed using a laser printer. Because contrast targets are planar as opposed to three-dimensional (like a sphere), the center of the target might suffer from errors that depend on the orientation of the target with respect to the TLS. In this paper, we discuss a low-cost method to characterize such errors and present results obtained from a short-range TLS and a long-range TLS. Our method involves comparing the center of a contrast target against the center of spheres and, therefore, does not require the use of a reference instrument or calibrated objects. For the short-range TLS, systematic errors of up to 0.5 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) and resolutions (30 points-per-degree (ppd) and 90 ppd) considered for this TLS. For the long-range TLS, systematic errors of about 0.3 mm to 0.8 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) at low resolution (28 ppd). Errors of under 0.3 mm were observed in the target center as a function of the angle for the two distances at high resolution (109 ppd).

Frequency-domain electromagnetic induction (EMI) is routinely used to detect the presence of seawater due to the inherent electrical conductivity of the seawater. This approach is used to infer sea-ice thickness (SIT). A time-domain EMI sensor is presented, which demonstrates the potential for correlating the spectroscopic properties of the received signal with the distance to the sea surface. This is a novel approach to SIT measurement, which differs from existing methods in that it uses measurements from 10 kHz to 93 kHz rather than a single frequency. The sensor was tested at a tidal pool containing seawater and measured to have a conductivity of 57.3 mS/cm. Measurements were performed at a range of heights between 0.2 m and 1.9 m above the sea surface and for inclinations from 0° to 45°. These measurements were correlated with Finite Element Modeling (FEM) simulations performed in COMSOL. The measured and simulated datasets are presented along with a proposed form of post-processing, which establishes a correlation between the distance to the sea surface and the measured EMI response. This forms a proxy measurement for the absolute distance from the EMI sensor to the sea surface. Because the air gap between the sensor and the seawater is indicative of the properties of sea ice, this study demonstrates a novel approach to non-destructive measurement of sea-ice thickness. The measurements show that this distance to the sea surface can be estimated to within approximately 10% of the known value from 0.2-1.5 m and 15% from 1.5 to 1.9 m.

An eight-element MIMO antenna with a neutralization line was utilized for future 5G mm-wave applications. The MIMO configuration was designed for two ports, four ports and eight ports to validate the desired impedance and radiation characteristics. The measured results in terms of MIMO and scattering parameters correlate well with the simulated one. The printed eight-port antenna was a miniaturized size of 44 × 70 × 0.8 mm³. Roger RT/duroid 5880 substrate was used to print antennas. The presented antenna produced a vast bandwidth of 18 GHz, varying from 28 to 46 GHz, and achieved a reduced mutual coupling of 30 dB with 6.8-8.5 dBi gain. The eight-port antenna is compared with contemporary antennas considering size, isolation, impedance bandwidth, diversity characteristics and radiation properties, confirming that the presented antenna is a promising candidate for future 5G mm-wave applications.

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)-passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn-dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk-dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn-dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62-0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34-3.71 K under all-sky conditions. In addition, compared with the dawn-dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study's findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications.

The present study proposes an L-shaped coplanar strip dipole antenna for sensing the presence of adulterants in liquid food samples. The proposed antenna dimensions are optimized using ANSYS HFSS, and a prototype is fabricated and validated. The sensing region is optimized based on the current distribution and measured reflection coefficients. Adulterant detection is performed by monitoring the variation in the reflection coefficient and resonance frequency of the antenna sensor. To verify the effectiveness of the proposed planar dipole as a sensor, an adulterant, which is hydrogen peroxide, is added to various liquid samples - milk, pineapple juice, and mango juice. The reflection coefficient of the antenna sensor is found to vary with various concentrations of the samples in the study. The sensitivity analysis of the antenna sensor and the repeatability of the results is also analyzed in the work. The experimental analysis assures the use of the proposed antenna as a sensor for the detection of adulterants in liquid food samples.

Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage but lower accuracy. LIDARs, on the other hand, have higher accuracy and longer range but much less coverage. LIDARs are also more expensive. The research question examines whether incorporating LIDARs can significantly improve stereo camera accuracy. Current sensor fusion methods use LIDARs' raw measurements directly; thus, the improvement in estimation accuracy is limited to only LIDAR-scanned locations The main contribution of our new method is to construct a reference ground plane through the interpolation of LIDAR data so that the interpolated maps have similar coverage as the stereo camera's point cloud. The interpolated maps are fused with the stereo camera point cloud via Kalman filters to improve a larger section of the topography map. The method is tested in three environments: controlled indoor, semi-controlled outdoor, and unstructured terrain. Compared to the existing method without LIDAR interpolation, the proposed approach reduces average error by 40% in the controlled environment and 67% in the semi-controlled environment, while maintaining large coverage. The unstructured environment evaluation confirms its corrective impact.

Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R² value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data.

Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions. To tackle this limitation, we propose a multi-feature fusion model leveraging a dual-attention mechanism. Initially, convolutional neural networks (CNNs) and channel attention mechanisms are employed to preliminarily extract spatial features. Subsequently, a layer combining a Gate Recurrent Unit (GRU) and self-attention mechanisms is used to further extract and integrate temporal features. Finally, RUL values are predicted via regression. The effectiveness of the proposed method was validated on C-MAPSS datasets, and its superior performance in RUL prediction was demonstrated through comparative analysis with other methods.