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Purpose – The supercritical design of tail rotor drive shaft has attracted more attention in helicopter design due to its high power–weight ratio and low maintenance cost. However, there exists excessive vibration when the shaft passes through the critical frequency. Dry friction damper is the equipment applied to the drive shaft to suppress the excessive vibration. In order to figure out the damping mechanism of the dry friction damper and improve the damping efficiency, the dynamic model of the shaft/damper system is established based on the Jeffcott rotor model. Design/methodology/approach – The typical frequency response of the system is studied through bifurcation diagrams, amplitude-frequency characteristic curves and waterfall frequency response spectrum. The typical transient responses under frequency sweeps are also obtained. Findings – The results show that the response of the system changes from periodic no-rub motion to quasi-periodic rub-impact motion, and then to synchronous full annular rub-impact, and finally, back to periodic no-rub motion. The slip of the rub-impact ring improves the stability of the system. Besides, the effects of the system parameters including critical dry friction force, rub-impact friction coefficient, initial clearance on the stability and the vibration damping capacity are studied. It is observed that the stability changes significantly varying the three parameters respectively. The vibration damping capacity is mainly affected by the critical dry friction force and the initial clearance. Originality/value – Presented results provide guidance for the design of the dry friction damper.

The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling.

Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.

Abstract Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation.

Abstract Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods.

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel.

Abstract Background Community-acquired lower respiratory tract infections (CA-LRTIs) are the primary cause of hospitalization among children globally. A better understanding of the role of atypical pathogen infections in native conditions is essential to improve clinical management and preventive measures. The main objective of this study was to detect the presence of 7 respiratory viruses and 2 atypical pathogens among hospitalized infants and children with community-acquired lower respiratory tract infections in Luzhou via an IgM test. Methods Overall, 6623 cases of local hospitalized children with 9 pathogen-IgM results from 1st July 2013 to 31st Dec 2016 were included; multidimensional analysis was performed. Results 1) Out of 19,467 hospitalized children with lower respiratory tract infections, 6623 samples were collected, for a submission ratio of 33.96% (6623 /19467). Of the total 6623 serum samples tested, 5784 IgM stains were positive, for a ratio of 87.33% (5784 /6623). Mycoplasma pneumoniae (MP) was the dominant pathogen (2548 /6623, 38.47%), with influenza B (INFB) (1606 /6623, 24.25%), Legionella pneumophila serogroup 1 (LP1) (485 /6623, 7.32%) and parainfluenza 1, 2 and 3(PIVs) (416 /6623, 6.28%) ranking second, third and fourth, respectively. 2) The distribution of various pathogen-IgM by age group was significantly different (χ 2 = 455.039, P

Objective: We conducted a meta-analysis of randomized controlled trials to explore whether vitamin D supplementation is beneficial for symptom improvement in children with autism spectrum disorder. Methods: We systematically searched the PubMed database, EMBASE, Cochrane Library, Web of Science, Sino-Med, Wanfang Data, and China National Knowledge Infrastructure mainly up to September 2019. Using a fixed effects model, we calculated the standard mean difference with 95% confidence interval. Furthermore, we analyzed baseline serum 25-hydroxyvitamin D levels and outcome scores including the Social Responsiveness Scale and Child Autism Rating Scale scores after vitamin D supplementation. Results: There was no significant difference in baseline serum 25-hydroxyvitamin D levels among 203 children included from three studies in the meta-analysis. After vitamin D supplementation, the outcome scores in the experimental group were dramatically elevated compared with those in the control group (p = 0.03). Conclusion: Vitamin D supplementation improves the typical symptoms of autism spectrum disorder, as indicated by reduced Social Responsiveness Scale and Child Autism Rating Scale scores; thus, it is beneficial for children with autism spectrum disorder. [ABSTRACT FROM AUTHOR]