Search Results (7,440)

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

This study develops a deep learning procedure able to identify a planar coil geometry, given the desired magnetic field map. This approach demonstrates its capability to discover suitable coil designs that produce desired field characteristics with high accuracy and efficiency. The generated coils show strong agreement with target magnetic fields, enabling manufacturers to achieve simpler structures and improved performance. This method is suitable for inductive proximity sensors, wireless power transfer systems, and electromagnetic compatibility applications, offering a powerful and flexible tool for advanced planar coil design. Full article

Recently, with the development of automation technology in various fields, much research has been conducted on infrared photodetectors, which are the core technology of LiDAR sensors. However, most infrared photodetectors are expensive because they use compound semiconductors based on epitaxial processes, and they have low safety because they use the near-infrared (NIR) region that can damage the retina. Therefore, they are difficult to apply to automation technologies such as automobiles and factories where humans can be constantly exposed. In contrast, short-wavelength infrared photodetectors based on PbS QDs are actively being developed because they can absorb infrared rays in the eye-safe region by controlling the particle size of QDs and can be easily and inexpensively manufactured through a solution process. However, PbS QDs-based SWIR photodetectors have low chemical stability due to the electron/hole extraction layer processed by the solution process, making it difficult to manufacture them in the form of patterning and arrays. In this study, bulk NiO and ZnO were deposited by sputtering to achieve uniformity and patterning of thin films, and the performance of PbS QDs-based photodetectors was improved by optimizing the thickness and annealing conditions of the thin films. The fabricated photodetector achieved a high response characteristic of 114.3% through optimized band gap and improved transmittance characteristics. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

Hybrid materials based on porous organic polymers (POPs) and metal–organic frameworks (MOFs) are increasing attention for advanced separation processes due to the possibility to combine their properties. POPs provide high surface areas, chemical stability, and tunable porosity, while MOFs contribute a high variety of defined crystalline structures and enhanced separation characteristics. The combination (or hybridization) with PIMs gives rise to mixed-matrix membranes (MMMs) with improved permeability, selectivity, and long-term stability. However, interfacial compatibility remains a key limitation, often addressed through polymer functionalization or controlled dispersion of the MOF phase. MOF/COF hybrids are more used as biochemical sensors with elevated sensitivity, catalytic applications, and wastewater remediation. They are also very well known in the gas sorption and separation field, due to their tunable porosity and high electrical conductivity, which also makes them feasible for energy storage applications. Last but not less important, hybrids with other POPs, such as hyper-crosslinked polymers (HCPs), covalent triazine frameworks (CTFs), or conjugated microporous polymers (CMPs), offer enhanced functionality. MOF/HCP hybrids combine ease of synthesis and chemical robustness with tunable porosity. MOF/CTF hybrids provide superior thermal and chemical stability under harsh conditions, while MOF/CMP hybrids introduce π-conjugation for enhanced conductivity and photocatalytic activity. These and other findings confirm the potential of MOF-POP hybrids as next-generation materials for gas separation and carbon capture applications. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that resonant VS150-RIC sensors outperform broadband sensors in defect detection, showing greater energy response at characteristic frequencies for inner race defects. The RMS parameter emerges as a robust diagnostic indicator, with defective bearings exhibiting periodic peaks and higher mean RMS values. Field tests reveal progressive RMS escalation preceding visible damage, enabling predictive maintenance. Furthermore, we develop a novel Paligemma LLM model for automated wear detection using AE time-domain images. The research validates the AE technology’s superiority over conventional vibration methods for low-speed bearing monitoring, providing a scientifically grounded approach for safety-critical ropeway maintenance. Full article

Leveraging the sensitivity of long-period fiber grating (LPFG) to changes in the environmental refractive index, an LPFG-based seawater concentration monitoring sensor is proposed. Considering the highly saltine and alkali characteristics of the sensor’s operating environment, the proposed sensor is packaged by basalt fiber-reinforced polymer (BFRP), and the sensor’s sensitivities were studied by sodium chloride and calcium chloride solution concentration experiments and one real-time sodium chloride solution concentration monitoring experiment. The test results show the wavelength of LPFG, a 3 dB bandwidth and a peak loss of LPFG’s spectrogram change with changes in the concentration of sodium chloride or calcium chloride solutions, but only the wavelength has a good linear relationship with the change in solution concentration, and the sensing coefficient is −0.160 nm/% in the sodium chloride solution and −0.225 nm/% in the calcium chloride solution. The real-time monitoring test further verified the sensor’s sensing performance, with an absolute measurement error of less than 1.8%. The BFRP packaged sensor has good corrosion resistance and a simple structure, and it has a certain application value in the monitoring of salinity in the marine environment and coastal soil. Full article

Ammonia (NH₃) is one of the characteristic gases used to detect food spoilage. In this study, the 10 wt% Ag-NiFe₂O₄ nanocomposite was synthesized via the hydrothermal method. Characterization results from SEM, XRD, and XPS analyzed the microstructure, elemental composition, and crystal lattice features of the composite, confirming its successful fabrication. Under the optimal working temperature of 280 °C, the composite exhibited excellent gas-sensing properties towards NH₃. The 10 wt% Ag-NiFe₂O₄ sensor demonstrates rapid response and recovery, as well as high sensitivity, towards 30 ppm NH₃, with response and recovery times of merely 3 s and 9 s, respectively, and a response value of 4.59. The detection limit is as low as 0.1 ppm, meeting the standards for food safety detection. Additionally, the sensor exhibits good short-term repeatability and long-term stability. Additionally, density functional theory (DFT) simulations were conducted to investigate the gas-sensing advantages of the Ag-NiFe₂O₄ composite by analyzing the electron density and density of states, thereby providing theoretical guidance for experimental testing. This study facilitates the rapid detection of food spoilage and promotes the development of portable food safety detection devices. Full article

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Reliable, data-driven identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In such geological settings, magnetic susceptibility, radioactive heat production, and seismic wave characteristics play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this methodology to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the statistical and AI-based models evaluated, Density-Based Spatial Clustering of Applications with Noise and Autoencoder-Based Deep Clustering identified the most promising and spatially confined subregions with high geothermal production potential. The potential geothermal sites identified by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region. Full article

Traditional motor temperature rise testing generally uses temperature sensors. To solve problems such as sensor detachment, aging, and space occupation, this study takes a three-phase asynchronous motor as an example to propose a method for building a temperature rise monitoring model driven by a multi-physics field model based on the digital twin framework of power equipment. A twin monitoring model with defined input–output parameters is constructed to solve the problems of measurement inconvenience in traditional methods. Firstly, the losses of the iron core and the winding copper in the motor were obtained through electromagnetic field simulation. Secondly, the temperature distribution of the motor stator was obtained based on the bidirectional coupling characteristics of the magnetic and thermal fields. Subsequently, a temperature field reduced-order model based on the proper orthogonal decomposition method was built in Twin Builder, achieving fast calculation of the motor stator temperature. Finally, using the YE3-80M1-4 motor as the experimental subject, the model’s output results were compared with and validated against the experimental results. The results indicate that the simulation time of the reduced-order model is 2.1 s, and the relative error compared with the test values is within 5%, which confirms the practical applicability of the proposed method. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

With the acceleration of industrialization, deformable mechanisms that can adapt to complex environments have gained widespread applications. Joints serve as carriers for transmitting forces and motions between components, and their stiffness significantly influences the static and dynamic characteristics of deformable mechanisms. A variable stiffness joint is crucial for ensuring the safety and reliability of the system, as well as for enhancing environmental adaptability. However, existing variable stiffness joints fail to meet the requirements for miniaturization, lightweight construction, and fast response. This paper proposes a piezoelectric-actuated variable stiffness miniature rotary joint featuring a compact structure, monitorable loading state, and rapid response. Given that the piezoelectric stack expands and contracts when energized, this paper proposes a transmission principle for stiffness adjustment by varying the pressure and friction between active and passive components. This joint utilizes a flexible hinge mechanism for displacement amplification and incorporates a torque sensor based on strain monitoring. A static model is developed based on piezoelectric equations and displacement amplification characteristics, and simulations confirm the feasibility of the stiffness adjustment scheme. The mechanical characteristics of various flexible hinge structures are analyzed, and the effects of piezoelectric actuation capability and external load on stiffness adjustment are examined. The experimental results demonstrate that the joint can adjust stiffness, and the sensor is calibrated using the least squares algorithm to monitor the stress state of the joint in real time. Full article

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using the DJI Matrice 300 RTK equipped with a Zenmuse L2 sensor, enabling high-density point cloud generation (98 points/m²). The point clouds were processed to remove non-ground points and generate a 0.25 m resolution DEM using TIN interpolation. A total of seven dolines were detected and delineated, and their morphometric characteristics—including area, perimeter, major and minor axes, and elevation—were analyzed. These results were compared with a 1:5000-scale DEM derived from the 2013 National Basic Map. Visual and numerical comparisons highlighted significant improvements in spatial resolution and feature delineation using UAV LiDAR. Although the 1:5000-scale DEM enables general doline detection, UAV LiDAR facilitates more precise boundary extraction and morphometric analysis. The study demonstrates the effectiveness of UAV LiDAR for detailed topographic mapping in complex karst terrains and offers a foundation for future automated classification and temporal change analysis. Full article