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Efficiency and accuracy are major bottlenecks in conducting ecological surveys and acquiring statistical data concerning grassland desertification. Traditional manual ground-based surveys are inefficient, and aerospace-based remote sensing surveys are limited by low spatial resolution and accuracy. In this study, we propose a low-altitude unmanned aerial vehicle (UAV) hyperspectral visible near-infrared (vis-NIR) remote sensing hardware platform, which combines efficiency and accuracy for high-precision remote sensing-based ecological surveys and statistical data collection on grassland desertification. We use the classical deep learning network models VGG and ResNet and their corresponding improved 3D convolutional kernels: 3D-VGG and 3D-ResNet, respectively, to classify the collected data into features. The results show that the two classical models yield good results for vegetation and bare soil in desertified grasslands, and the 3D models yield superior classification results for small sample features. Our results can serve as benchmarks for hardware integration and data analysis for remote sensing-based grassland desertification research and lay the foundation for further finer classifications and more accurate statistics of features., Desertification is a major global environmental problem directly affecting approximately a quarter of the global population, with an estimated total of 10 million hectares of arable land being lost to [...]

High-precision statistics on desertification of grassland features are an important part of grassland ecosystem research. The traditional manual survey is inefficient, and satellite remote sensing has very limited statistical precision, so high-spectral remote sensing by lowaltitude drones is preferable. Here, we report a hyperspectral remote-sensing system for unmanned aerial vehicles (UAVs). We used the vegetation and soil of typical desertified grassland in Inner Mongolia as research objects to collect vis-NIR hyperspectral data on desertification using a deep belief network (DBN), 2D convolutional neural network (2D-CNN), and 3D convolutional neural network (3D-CNN). The results show that these typical deep learning models can effectively classify hyperspectral data on desertified grassland features. The highest classification accuracy was achieved by 3D-CNN, with an overall accuracy of 86.36%. This study enriches the spatial scale of remote sensing research on grassland desertification, and provides a basis for further high-precision statistics and inversion of remote sensing of grassland desertification., The term desertification was first proposed by the French scientist A. Aubreville in 1949. At the 1977 United Nations conference on desertification, it was defined as the decline or destruction [...]

Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology. [ABSTRACT FROM AUTHOR]

A calculation model of radial electromagnetic force wave for permanent magnet assisted reluctance motor (PMaSynRM) with rotor slots is proposed in this paper. By introducing complex air gap relative permeance, the improved magnetic potential permeability method is applied to the calculation of electromagnetic force wave of the PMaSynRM. Considering the influence of both stator and rotor slots, the characteristics of electromagnetic force wave are analyzed theoretically. A multi‐physical coupling model of PMaSynRM is established. The natural frequency of the stator core is obtained by modal finite element analysis and modal hammer experiment. The harmonic response is analyzed by loading electromagnetic force wave into the structure field and the vibration on the housing surface is calculated. The results show that although the amplitude of electromagnetic force is small at zero‐order slot frequency, the vibration caused by electromagnetic force at frequency of stator slots and rotor slots, especially at the rotor slot frequency, is the biggest vibration source. The effectiveness and regularity of the calculation model are verified by the prototype vibration experiments. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation. [ABSTRACT FROM AUTHOR]

Identifying grass species in grasslands based on unmanned aerial vehicle hyperspectral remote sensing is the basis and premise of hyperspectral remote sensing when applied to grassland degradation monitoring and research. The small targets and mixed pixels involved grass species identification in grasslands creates problems, making identification cumbersome and classification accuracy difficult. This study involved the construction of an unmanned aerial vehicle hyperspectral remote sensing system using hyperspectral data of grass species in desert habitats that had been collected under natural light. A multi‐resolution combined with a 1 × 1 feature map was formed by multiscale convolution, and grass species data were extracted from hyperspectral fine‐grained feature data from grasslands. A recognition and classification model for degradation indicator species CNN was constructed using max pooling to retain the maximum amount of feature detail and up‐sampling, reconstructing the feature space and feature fusion to smooth the edge texture of the data and enhance the weak data to alleviate the imbalance among samples. The results showed that the overall identification accuracy of the model for grassland species reached 98.78%, and the kappa coefficient reached 0.92, realizing the high‐precision identification of grassland species, which laid the foundation for grassland species detection and research based on unmanned aerial vehicle hyperspectral imagery. In addition, the proposed degradation indicator species CNN model provides a useful reference for the identification and classification of small targets with mixed pixels. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging remote sensing can obtain abundant spectral, radiation and spatial information of ground objects, which has been widely used in various fields of national economy. But its narrow band spacing brings not only rich spectral information, but also information redundancy and the difficulty of data processing. Therefore, before the practical application of hyperspectral remote sensing data, band selection is needed to extract spectral features and reduce the data dimension. This review summarizes the research progress of band selection for hyperspectral remote sensing images. Based on the analysis and summary of band selection strategies, the related technology and the latest research status are expounded from six aspects: the evaluation criteria of band selection, the band selection based on the combination of spatial and spectral features, the band selection based on semi-supervised learning, the band selection based on sparse representation, the band selection based on intelligent search and the band selection based on deep learning. Then, the current problems and challenges faced by hyperspectral image band selection are discussed. Finally, the future development direction of hyperspectral image band selection is predicted. [ABSTRACT FROM AUTHOR]

An inertial particle separator (IPS) mounted before a helicopter engine faces the danger of ice accretion caused by the impingement of super-cooled water droplets. This paper shows the results of a numerical study on the flow and impingement of water droplets in the IPS using the Eulerian–Eulerian method. Cases for different medium volume diameters (MVDs = 5 μm, 10 μm, and 20 µm), liquid water contents (LWCs = 0.5 g/m3, 2 g/m3, and 4 g/m3), and incoming velocities (U0s = 40 m/s, 65 m/s, and 90 m/s) are examined to study their effects on the flow and impingement of water droplets in the IPS. The results indicate that the IPS can reduce the water that enters the core flow path by trapping the water on the inner walls and separating it into the scavenge flow path. The ratio between the flow rate of water at the outlet of the core flow path and that at the inlet ( m ̇ c / m ̇ i n l e t ) decreases with an increased MVD or incoming velocity, which, however, does not change with the LWC. The results also show that the water collection coefficient (β) on the hub and shroud walls increases with a greater MVD, while the β on the splitter wall reaches its maximum at an MVD of 10 µm in the studied cases. The LWC does not have any effect on β for any of the IPS walls, while β increases on all IPS surfaces with the incoming velocity. [ABSTRACT FROM AUTHOR]

A type of sealed steel box was designed to simulate sulfate attack and hydraulic pressure on concrete. Cup-shaped concrete specimens with water/cement (w/c) ratios of 0.4, 0.5, and 0.6 were fabricated. During the 270 days corrosion time, corrosion behavior on concrete surfaces was observed every 30 days. The extent of damage on concrete was calculated on the basis of the results of periodic ultrasonic testing. After the completion of experiments, X-ray fluorescence analysis was conducted on the element contents at different depth locations of partial specimens. Experimental results showed that hydraulic pressure accelerated the sulfate attack on concrete, and high pressure yielded serious damage. In comparison with zero pressure, the accelerating coefficients of 0.1, 0.2, and 0.3 MPa hydraulic pressures were 1.48, 1.75, and 2.35, respectively. Hydraulic sulfate corrosion can bring additional sulfate ions into concrete and cause chemical and physical sulfate corrosions on concrete simultaneously, thereby resulting in serious damage on concrete. The reduction of w/c ratio and incorporation of fly ash are beneficial for concrete to resist hydraulic sulfate attack. [ABSTRACT FROM AUTHOR]