- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Impact paint sensor based on polymer/multi-dimension carbon nano isotopes composites
摘要： We presented a novel impact paint sensor made of piezoresistive nano-carbon composites and studied its characteristics. The paint sensors were fabricated with multi-walled carbon nanotube (MWCNT), exfoliated graphite nano-platelets (xGnP), and a hybrid type of the two nano-carbon fillers and were sprayed onto a carbon fiber reinforced plastic (CFRP) panel for lab testing. In ball drop impact test, the MWCNT-xGnP-based hybrid sensor showed the best characteristics in impact energy sensing within the range 0.07-1.0J. We also studied the piezoresistive mechanism due to dimensional variations of nano carbon isotopes for sensor design. Piezorestivity of nano-carbon sensor was significantly dominated the electrical contact variation of the electrical fillers in a matrix. This study is expected to provide a feasibility test for designing impact paint sensors with optimized sensitivity for a composite structural health monitoring (SHM).
关键词： Carbon nanotube (CNT),Exfoliated graphite nanoplate (xGnP),Structural health monitoring (SHM),Unmanned aerial vehicle (UAV),Impact paint sensor
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA) - Concepcion, Chile (2018.10.17-2018.10.19)] 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA) - Comparison of vegetation indices acquired from RGB and Multispectral sensors placed on UAV
摘要： This manuscript presents a comparison of Normalized Difference Vegetation Index (NDVI) obtained with multispectral cameras versus four indices obtained from RGB sensors for the identification of soil and vegetation in images captured with an unmanned aerial vehicle. This comparison was made using the NDVI as ground truth, obtaining 2 classes of data that would be compared later to the other indexes by counting the pixels corresponding to each class. In the case of the RGB indices, the average was defined as the center of the data and as the cut-off point of both classes. The results of this investigation indicated that it is possible to identify the same spatial patterns using RGB indices, where the TGI index shows the best behavior. However, despite the fact that the pixel count showed similar results, the visual inspection of the results indicated that the RGB indices presented errors when identifying the vegetation, especially in the zone of the row. This indicates that to delimit with precision the areas corresponding to vegetation and soil it is necessary to use more complex clustering techniques.
关键词： Image processing,Agricultural engineering,Unmanned aerial vehicles (UAV)
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia) - Chengdu, China (2018.10.26-2018.10.30)] 2018 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia) - Wireless Power Transfer for 3D Printed Unmanned Aerial Vehicle (UAV) Systems
摘要： Unmanned aerial vehicles (UAVs) have attracted a lot of attention for various applications such as service delivery, pollution mitigation, farming, and rescue operations over the past few years. However, the short duration of battery and the inconvenience of changing it is always a problem. Basically, small UAVs can only carry very limited payloads otherwise the battery will be drained more frequently. This project presents an automatic and high-efficient wireless power transfer (WPT) to supply a 3D printed UAV. A UAV has been 3D printed with wireless power transfer kit implemented to charge 3S 1500 mAh Li-Po battery with up to 1000 mAh automatically once it is landed, without manual operation. 24V DC is supplied to the transmitting side of WPT with the operating frequency at 180kHz and once the battery is fully charged, the charging process will also stop automatically.
关键词： Unmanned Aerial Vehicle (UAV),Wireless Power Transfer,3D Printing
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE Conference on Antenna Measurements & Applications (CAMA) - Va?ster?s (2018.9.3-2018.9.6)] 2018 IEEE Conference on Antenna Measurements & Applications (CAMA) - UAS-Based Antenna Pattern Measurements and Radar Characterization
摘要： This paper presents an update of the current in-situ antenna characterization and calibration of a radar system using an Unmanned Aircraft System (UAS) developed by the Advanced Radar Research Center (ARRC) at The University of Oklahoma. A large multirotor platform was customized for long endurance (～30 minutes), high positioning accuracy (<3 cm), and high stability, and was integrated with a high precision 3-axis gimbal that holds an antenna array and pulse generator-transmitter. The platform was designed to support measurements from 2 GHz to 10 GHz, however, the current setup described in this article includes an S-band array probe of 3x3 elements. The RF probe beamwidth was optimized to minimize reflections from the UAS frame and to provide accurate antenna measurements in flight conditions.
关键词： In-situ antenna measurements,UAV,dual-polarized radar,radar calibration,DGPS,UAS,antenna measurements,RTK
更新于2025-09-23 15:22:29
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[IEEE 2018 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF) - Saint-Petersburg, Russia (2018.11.26-2018.11.30)] 2018 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF) - On-Board Unlimited Aircraft Complex of Environmental Monitoring
摘要： the problems of the creation and use of autonomous UAVs for environmental monitoring of the earth and sea surfaces, as well as the state of ice fields are considered. Technical features of power supply, navigation system, wireless transmission channel and encryption of transmitted data are evaluated. A variant of the equipment of an autonomous complex for monitoring subsurface layers based on six-engine drone with the use of an ultra-wideband radar for implementing the functions of monitoring hazardous objects (forest fires, areas of man-made and natural emergencies), the detection of people and physical objects in the review of marine and coastal zones, as well as ice covers and subsurface layers.
关键词： UAV,autonomy,wireless communication,power supply,navigation
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Road Segmentation of UAV RS Image Using Adversarial Network with Multi-Scale Context Aggregation
摘要： Semantic segmentation using adversarial networks has been approved to produce the better artificial results in image processing fields. Focused on current Deep Convolutional Neural Networks (DCNNs), since the convolutional kernel size has been fixed in every convolutional operation, the small objects would be ignored with large convolutional kernel size, and the segmentation result of large objects is not continuous with small convolutional kernel size. The paper developed a semantic segmentation model that combined the adversarial networks with multi-scale context aggregation. Further, the model was applied to road segmentation of UAV RS images. The experimental results of this semantic segmentation model with multi-scale context aggregation has a better performance for road segmentation and fit well with the reference standard results. It can improve the road segmentation accuracy obviously in the situation where there are other small regions whose shape or color is similar to road regions in UAV RS images.
关键词： Road Segmentation,Adversarial Network,UAV image,Image processing,multi-scale context aggregation
更新于2025-09-23 15:22:29
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Application research of image recognition technology based on CNN in image location of environmental monitoring UAV
摘要： UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer vision. Convolution neural network model is the most commonly used image processing model. Compared with the traditional artificial neural network model, convolution neural network has more hidden layers. Its unique convolution and pooling operations have higher efficiency in image processing. It has incomparable advantages in image recognition and location and other forms of two-dimensional graphics tasks. As a new deformation of convolution neural network, residual neural network aims to make convolution layer learn a kind of residual instead of a direct learning goal. After analyzing the characteristics of CNN model for image feature representation and residual network, a residual network model is built. The UAV remote sensing system is selected as the platform to acquire image data, and the problem of image recognition based on residual neural network is studied, which is verified by experiment simulation and precision analysis. Finally, the problems and experiences in the process of learning and designing are discussed, and the future improvements in the field of image target location and recognition are prospected.
关键词： Residual network,CNN,Image recognition,UAV
更新于2025-09-23 15:22:29
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Target Detection and Classification of Small Drones by Boosting on Radar Micro-Doppler
摘要： Small drones, also called mini-UAVs (Unmanned Aerial Vehicles), have become very wide-spread. They have many positive applications. However, they also have negative uses and it is often necessary to detect and classify them. In this paper we employ radar micro-Doppler for detection and classification of small drones. Micro-Doppler are Doppler shifts generated by the movements of internal parts of the target. We have used radar measurements of small drones and birds, extracted physical features from TVDs (Time Velocity Diagrams), and used a boosting classifier to distinguish between drones and birds (target detection) and types of drones (target classification) with good results. We have also compared with a SVM (Support Vector Machine) classifier. Our conclusion is that Micro-Doppler radar has the potential for reliable small drone target detection and is also promising for classifying the type of drone. The boosting classifier has some advantages over SVM.
关键词： detection,boosting,drone,UAV,radar,micro-Doppler,classification
更新于2025-09-23 15:21:21
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Quantification and analysis of geomorphic processes on a recultivated iron ore mine on the Italian island of Elba using long-term ground-based lidar and photogrammetric SfM data by a UAV
摘要： This study focuses on the quanti?cation and analysis of geomorphic processes on the barely vegetated slopes of a recultivated iron ore mine on the Italian island of Elba using photographs from terrestrial laser scanning (TLS) and digital photogrammetry by an unmanned aerial vehicle (UAV) over a period of 5 1/2 years. Beside this, the study tried to work out the potential and the limitations of both methods to detect surface changes by geomorphic process dynamics within a natural environment. Both UAV and TLS show the pattern of the erosion and accumulation processes on the investigated slope quite well, but the calculated amounts differ clearly between the methods. The reasons for these differences could be found in the different accuracies (variable level of detections) of the methods and the different viewing geometries. Both effects have an impact on the detectable process dynamics over different timescales on the slope and their calculated amounts, which in both cases can lead to an underestimation of erosion and accumulation by ?uvial processes.
关键词： terrestrial laser scanning,erosion,geomorphic processes,UAV,recultivated iron ore mine,accumulation,digital photogrammetry
更新于2025-09-23 15:21:21
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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations
摘要： Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN and AGB (CV-R2 = 0.95, performed well rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m?2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m?2) to 5% (8 pts·m?2).
关键词： Ginkgo,UAV,LiDAR,Forest structural attributes,Point cloud density,Planted forest
更新于2025-09-23 15:21:21