Search Results (136)

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale. Full article

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article

The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15°), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency—typically by 30% to 50%—along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability ≥ 25°, lateral tilt stability > 30°) is a major research priority for China’s agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems—power systems, traveling systems, and leveling systems—and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China’s “Dual Carbon” strategy and the unique operational requirements of hilly–mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance ≥400 mm, obstacle-crossing height ≥ 250 mm) and traction stability (slip ratio < 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate < ±10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25° slopes) through power–drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy ± 0.5°, response time < 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities—including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling—this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China’s hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization. Full article

The acoustically actuated antenna technology enables a significant reduction in antenna dimension, facilitating miniaturization of ground-penetrating radar systems in the very high-frequency (VHF) band. However, the current acoustically actuated antennas suffer from narrow bandwidth and low range resolution. To address this issue, this paper proposed a three-dimensional (3D) localization method for underground targets, which combined two-dimensional (2D) array direction-of-arrival (DOA) estimation with continuous spatial sampling without relying on range resolution. By leveraging the small dimension of acoustically actuated antennas, a 2D uniform linear array was formed to obtain the target’s angle using DOA estimation. Based on the variation pattern of 2D angles in continuous spatial sampling, the genetic algorithm was employed to estimate the 3D coordinates of underground targets. The numerical simulation results indicated that the root mean square error (RMSE) of the proposed 3D localization method is 1.68 cm, which outperforms conventional methods that utilize wideband frequency-modulated pulse signals with hyperbolic vertex detection in theoretical localization accuracy, while also demonstrating good robustness. The gprMax electromagnetic simulation results further confirmed that this method can effectively localize multiple targets in ideal homogeneous underground media. Full article

Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems. To address the challenge that traditional single-end positioning methods experience when identifying the reflected wave head and that the adaptability of wave head calibration methods is typically limited, a single-end location method of modulus wave velocity differences based on marine predator algorithm optimized multivariate variational mode decomposition (MVMD) and morphological gradient is proposed. Firstly, the minimum comprehensive entropy kurtosis ratio is used as the fitness function, and the marine predator algorithm is used to realize the automatic optimization of the mode number and penalty factor of the multivariate variational mode decomposition. Therefore, with the goal of decomposing the traveling wave characteristic signals with the most significant traveling wave characteristic information and the lowest noise component, the line-mode traveling wave and the zero-mode traveling wave are accurately decomposed. Secondly, the intrinsic mode function component with the smallest entropy kurtosis ratio is selected as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively, and the arrival time of the wave head is accurately calibrated by combining the morphological gradient value. Finally, the fault distance is calculated by the modulus wave velocity difference location formula and compared with the variational mode decomposition-Teager energy operator (VMD-TEO) method and the empirical mode decomposition _first-order difference method. The results show that the proposed method has the highest accuracy of positioning results, and the algorithm time is significantly reduced compared with the VMD-TEO method, and it has strong adaptability to different line types of faults, different fault initial conditions, and noise interference. Full article

The deployment of wireless devices has increased exponentially in recent years, not only for mobile applications but also for IoT. Typically, these IoT devices exchange data with other devices by means of wireless connections, where battery consumption depends on the antenna system’s efficiency. In applications where long battery life and reliable transmission are essential, improving the efficiency of the antenna is crucial. This study aims to investigate how shaping the ground plane of a wireless device can enhance bandwidth and antenna efficiency, specifically in low-frequency bands of 824–960 MHz, a common frequency band used in IoT where transmitting a small amount of data provides long battery life. Specifically, this work shows that by adding a slot in the ground plane, the current distribution is enlarged, which enables the excitation of its fundamental mode and, consequently, enhances the bandwidth and antenna efficiency by 2 dB. This approach is assessed using three different printed circuit boards (PCBs) that aim to characterise different form factors of IoT devices. A physical prototype is built to validate the results obtained in simulations. Full article

To reduce the environmental impact of airborne transportation, the aeronautic community investigates smaller aircraft with short-range operations (such as training aircraft, air taxis, or commuter aircraft) as technology incubators. This paper contributes to this effort by presenting an analysis framework and a detailed case study for integrating an auxiliary solar power system for air taxi operations. The solar power system conceptual design and analysis framework is improved to capture important effects for more realistic analysis for smaller aircraft, such as allowing the solar power system’s efficiency to be estimated as a function of aircraft mission parameters (temperature, speed, cloudiness) and providing a detailed view of the new system’s weight estimation considering potential physical integration scenarios. A detailed analysis of Harbour Air’s seaplane air taxi operations and the DHC-2 Beaver is performed using this enhanced design framework. The results show that the solar power system output exceeds the required secondary electrical power for 86% of the mission in one season; hence, it provides the potential to supplement a hybrid electric propulsion system. Secondly, the authors designed experiments to investigate the sensitivity of technology uncertainties for one critical mission. The results show that a small fuel burn reduction can be achieved with current technologies, with a promising trend of more savings with increasing system efficiency. Also, the results show that accumulated over a season’s operation, the CO₂ emissions from the aircraft can be reduced. The findings indicate that integrating solar power systems can supplement traditional power sources and improve ground operations: specifically, solar energy could power a zero-emission and autonomous air-conditioning system while parked. Overall, integrating solar power into seaplane air taxi operations, even as a retrofit, presents a viable strategy for achieving more sustainable air transportation. Full article

Rock bolting in underground environments is used for different fundamental reasons, including suspending potentially loosened blocks, clamping small wedges together, inducing a protective pressure arch along the contour of excavated voids to improve the self-supporting capacity of the ground, and providing passive pressure in integrated support systems. In this study, we describe a testing procedure that was developed to investigate the grouted annulus of a rock bolt using a low-cost investigation method. This diagnostic technique was based on the dynamic response of the system, where mechanical vibrations were induced within the rock bolt and the response was recorded by using geophones/accelerometers on the protruding element of the bolt (the collar and head). The collected signal was then processed to estimate the spectral response, and the amplitude spectrum was analyzed to detect the resonance frequencies. A 3D finite element model of the rock bolt and grouting was established to simulate the quality of the coupling by varying the mechanical properties of the grouting. The model’s response for the studied geometry of the rock bolt suggested that a poor quality of grouting was usually associated with flexural modes of vibration with a low resonance frequency. Good-quality grouting was associated with a frequency higher than 1400 Hz, where the axial vibration was mainly excited. Our analyses referred to short rock bolts, which are usually adopted in small tunnels. The interpretation of the experimental measurements assumed that the spectral response was significantly affected by the quality of the grouting, as demonstrated by the modeling procedure. The resonant frequency was compared with the results of the model simulation. The method was used to test the quality of rock bolts in a small experimental tunnel carved from andesite rock in Chile. Low-cost shock sensors (piezoelectric geophones) with low sensitivity but a wide frequency band were used. The main research outcome was the development of a reliable method to model the dynamic response of rock bolts in mines or for experimental applications in tunnels. Albeit limited to the current specific geometries, the modeling and testing will be adapted to other anchor/bolt options. Full article

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly gaining popularity, and their consistent prevalence in various applications such as surveillance, search and rescue, and environmental monitoring requires the development of specialized policies for UAV traffic management. Integrating this novel aerial traffic into existing airspace frameworks presents unique challenges, particularly regarding safety and security. Consequently, there is an urgent need for robust contingency management systems, such as Anti-UAV technologies, to ensure safe air traffic. This survey paper critically examines the recent advancements in ground-to-air vision-based Anti-UAV detection and tracking methodologies, addressing the many challenges inherent in UAV detection and tracking. Our study examines recent UAV detection and tracking algorithms, outlining their operational principles, advantages, and disadvantages. Publicly available datasets specifically designed for Anti-UAV research are also thoroughly reviewed, providing insights into their characteristics and suitability. Furthermore, this survey explores the various Anti-UAV systems being developed and deployed globally, evaluating their effectiveness in facilitating the integration of small UAVs into low-altitude airspace. The study aims to provide researchers with a well-rounded understanding of the field by synthesizing current research trends, identifying key technological gaps, and highlighting promising directions for future research and development in Anti-UAV technologies. Full article

A commercially available 100 kW PEM fuel cell system designed for efficient operation on ground-level was tested at low ambient pressures between 750 mbar and 940 mbar in a low-pressure chamber. The current–voltage characteristics at 940 mbar and 900 mbar showed only small differences, while the system performed worse at lower ambient pressures. To enable operation at these low pressures, an additional current-limiting strategy had to be implemented, as it was found that the compressor could not deliver sufficient mass flow at ambient pressures below 867 mbar to reach the maximum current allowed by the system (420 A). The results show that the fuel cell system, which was designed for ground-level applications, can be operated at lower pressures if the proposed current-limiting strategy is implemented, although at the cost of a lower maximum current output at low ambient pressures. Based on the results, suggestions for further hardware measures to optimise the system for flight conditions are made. Full article

Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis. Full article

In China’s Yangtze River and Yellow River basin cotton-growing regions, the complex terrain, scattered planting areas, and poor adaptability of the existing machinery have led to a mechanized cotton harvesting rate of less than 10%. To address this issue, we designed a crawler chassis for a brush-roller cotton harvester. It is specifically tailored to meet the 76 cm row spacing agronomic requirement. We also conducted a theoretical analysis of the power transmission system for the crawler chassis. Initially, we considered the terrain characteristics of China’s inland cotton-growing regions and the current cotton agronomy practices. Based on these, we selected and designed the power system and chassis; then, a finite element static analysis was carried out on the chassis frame to ensure safety during operation; finally, field tests on the harvester’s operability, stability, and speed were carried out. The results show that the inverted trapezoidal crawler walking device, combined with a hydraulic continuously variable transmission and rear-drive design, enhances the crawler’s passability. The crawler parameters included a ground contact length of 1650 mm, a maximum ground clearance of 270 mm, a maximum operating speed of 6.1 km/h, and an actual turning radius of 2300 mm. The maximum deformation of the frame was 2.198 mm, the deformation of the walking chassis was 1.0716 mm, the maximum equivalent stress was 216.96 MPa, and the average equivalent stress of the entire frame was 5.6356 MPa, which complies with the physical properties of the selected material, Q235. The designed cotton harvester crawler chassis features stable straight-line and steering performance. The vehicle’s speed can be adjusted based on the complexity of the terrain, with timely steering responses, minimal compaction on cotton, and reduced soil damage, meeting the requirements for mechanized harvesting in China’s inland small plots. Full article

Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) motion model are established according to the characteristics of a mining environment. Secondly, an online search area path-planning method is proposed based on the gray system theory and the reinforcement learning theory to handle multiple constraints. By adopting the multi-attribute intelligent (MAI) gray decision process, the action selection decision can be dynamically adjusted based on the current environment, ensuring the stable convergence of the model. Finally, experimental verification is conducted in different small-scale mine SAR simulation scenarios. The experimental results show that the proposed search planning method can capture the target in the search area with a smoother convergence effect and a shorter path length than other path-planning algorithms. Full article

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level. Full article