Academic literature on the topic 'Cloud structure/detection'
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Journal articles on the topic "Cloud structure/detection"
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Van Tricht, K., I. V. Gorodetskaya, S. Lhermitte, D. D. Turner, J. H. Schween, and N. P. M. Van Lipzig. "An improved algorithm for cloud base detection by ceilometer over the ice sheets." Atmospheric Measurement Techniques Discussions 6, no. 6 (November 14, 2013): 9819–55. http://dx.doi.org/10.5194/amtd-6-9819-2013.
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Abstract. Optically thin ice clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation on the surface. Cloud bases can be detected by lidar-based ceilometers that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. Despite their importance, thin clouds are however not well detected by the standard cloud base detection algorithm of most ceilometers operational at Arctic and Antarctic stations. This paper presents the Polar Threshold (PT) algorithm that was developed to detect optically thin hydrometeor layers (optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10−4 km−1 sr−1 for cloud base detection was objectively derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 50 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly differentiate between clear sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid, shows a seasonal cycle at Summit with a monthly mean summer peak of 40%. The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 47%, which reduces to 14% for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87% of all detections below 500 m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat, that may be insensitive for hydrometeors near the surface. The results of this study highlight the potential of the PT algorithm to extract information in polar regions about a wide range of hydrometeor types from measurements by the robust and relatively low-cost ceilometer instrument.
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Van Tricht, K., I. V. Gorodetskaya, S. Lhermitte, D. D. Turner, J. H. Schween, and N. P. M. Van Lipzig. "An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets." Atmospheric Measurement Techniques 7, no. 5 (May 6, 2014): 1153–67. http://dx.doi.org/10.5194/amt-7-1153-2014.
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Abstract. Optically thin ice and mixed-phase clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation. Cloud-base heights can be detected by ceilometers, low-power backscatter lidars that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. The standard cloud-base detection algorithms of ceilometers are designed to detect optically thick liquid-containing clouds, while the detection of thin ice clouds requires an alternative approach. This paper presents the polar threshold (PT) algorithm that was developed to be sensitive to optically thin hydrometeor layers (minimum optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10−4 km−1 sr−1 for cloud-base detection near the surface was derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. At higher altitudes where the average noise level is higher than the backscatter threshold, the PT algorithm becomes signal-to-noise ratio driven. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 90 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly discriminate between clear-sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% (±10%) at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid water droplets, shows a seasonal cycle at Summit with a monthly mean summer peak of 40 % (±4%). The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 46% (±5%), which reduces to 12% (±2.5%) for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87% of all detections below 500 m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat that may be insensitive for hydrometeors near the surface. The decrease of sensitivity with height, which is an inherent limitation of the ceilometer, does not have a significant impact on our results. This study highlights the potential of the PT algorithm to extract information in polar regions from various hydrometeor layers using measurements by the robust and relatively low-cost ceilometer instrument.
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Li, Xiaolong, Hong Zheng, Chuanzhao Han, Wentao Zheng, Hao Chen, Ying Jing, and Kaihan Dong. "SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images." Remote Sensing 13, no. 15 (July 24, 2021): 2910. http://dx.doi.org/10.3390/rs13152910.
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Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics Discussions 10, no. 3 (March 30, 2010): 8247–96. http://dx.doi.org/10.5194/acpd-10-8247-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated as about 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover (0 or 0.3). 40% of all clouds are high clouds, and about 44% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics 10, no. 15 (August 6, 2010): 7197–214. http://dx.doi.org/10.5194/acp-10-7197-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Li, Zhi, Haitao Xu, and Yanzhu Liu. "A differential game model of intrusion detection system in cloud computing." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668799. http://dx.doi.org/10.1177/1550147716687995.
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The multi-mesh distributed and open structure of cloud computing is more weak and vulnerable to security threats. Intrusion detection system should be incorporated in cloud infrastructure to monitor cloud resources against security attacks. In this article, the interaction between rational cloud resource defender and the potential malicious user in the cloud as a differential game is investigated. The feedback Nash equilibrium of the game is reviewed and a complex decision-making process and interactions between the cloud resource defender and a malicious user of cloud are also analyzed. The system results support a theoretical foundation in detecting the malicious attack, which can help cloud intrusion detection system make the optimal dynamic strategies to improve the defensive ability.
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Liu, Lei, Xuejin Sun, Feng Chen, Shijun Zhao, and Taichang Gao. "Cloud Classification Based on Structure Features of Infrared Images." Journal of Atmospheric and Oceanic Technology 28, no. 3 (March 1, 2011): 410–17. http://dx.doi.org/10.1175/2010jtecha1385.1.
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Abstract Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium–small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%.
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Tinkham, Wade T., and Neal C. Swayze. "Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models." Forests 12, no. 2 (February 22, 2021): 250. http://dx.doi.org/10.3390/f12020250.
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Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.
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Li, Zongmin, Chunchun Yao, Yujie Liu, and Hua Li. "Vehicle Detection Based on Structure Perception in Point Cloud." Journal of Computer-Aided Design & Computer Graphics 33, no. 3 (March 1, 2021): 405–12. http://dx.doi.org/10.3724/sp.j.1089.2021.18368.
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Hatipoğlu, P. U., R. T. Albayrak, and A. A. Alatan. "OBJECT DETECTION UNDER MOVING CLOUD SHADOWS IN WAMI." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 837–44. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-837-2020.
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Abstract. For a reliable and robust moving object detection, the subtraction of a precisely modeled background is crucial in wide-area motion imagery (WAMI). Even the most successful background subtraction algorithms that are designed to model highly-dynamic environments cannot cope with rapidly changing scenery, such as moving cloud shadows, which has different characteristics from dynamic textures. This paper presents a novel method to detect moving objects and to eliminate false alarms under moving cloud shadow regions in gray-level video sequences. The proposed method uses the relation between reflectance values of the shadowed and well-illuminated sequences of the regions in the video frame. A modified adaptive region growing approach, which extends from seed points, is designed to obtain the moving parts of the cloud shadows without presuming the geometric structure of the clouds. In order to determine the moving border of the cloud shadows, where false alarms typically occur, the cloud shadow motion should be detected. As the last stage of the proposed method, real moving objects in the scene are tried to be discriminated from false alarms by exploiting the relation of intensity ratios between the object candidate and its surroundings. The accuracy and computational efficiency of the proposed approach make it a reliable and feasible approach to be used in real-time surveillance solutions.
Dissertations / Theses on the topic "Cloud structure/detection"
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Lloyd, P. E. "Tropospheric sounding from the TIROS-N series of satellites." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379918.
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Neeli, Yeshwanth Sai. "Use of Photogrammetry Aided Damage Detection for Residual Strength Estimation of Corrosion Damaged Prestressed Concrete Bridge Girders." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99445.
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Corrosion damage reduces the load-carrying capacity of bridges which poses a threat to passenger safety. The objective of this research was to reduce the resources involved in conventional bridge inspections which are an important tool in the condition assessment of bridges and to help in determining if live load testing is necessary. This research proposes a framework to link semi-automated damage detection on prestressed concrete bridge girders with the estimation of their residual flexural capacity. The framework was implemented on four full-scale corrosion damaged girders from decommissioned bridges in Virginia. 3D point clouds of the girders reconstructed from images using Structure from Motion (SfM) approach were textured with images containing cracks detected at pixel level using a U-Net (Fully Convolutional Network). Spalls were detected by identifying the locations where normals associated with the points in the 3D point cloud deviated from being perpendicular to the reference directions chosen, by an amount greater than a threshold angle. 3D textured mesh models, overlaid with the detected cracks and spalls were used as 3D damage maps to determine reduced cross-sectional areas of prestressing strands to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011). Scaling them to real-world dimensions enabled the measurement of any required dimension, eliminating the need for physical contact.
The flexural capacities of a box beam and an I-beam estimated using strain compatibility analysis were validated with the actual capacities at failure sections determined from four destructive tests conducted by Al Rufaydah (2020). Along with the reduction in the cross-sectional areas of strands, limiting the ultimate strain that heavily corroded strands can develop was explored as a possible way to improve the results of the analysis. Strain compatibility analysis was used to estimate the ultimate rupture strain, in the heavily corroded bottommost layer prestressing strands exposed before the box beam was tested. More research is required to associate each level of strand corrosion with an average ultimate strain at which the corroded strands rupture. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework.Master of Science
Corrosion damage is a major concern for bridges as it reduces their load carrying capacity. Bridge failures in the past have been attributed to corrosion damage. The risk associated with corrosion damage caused failures increases as the infrastructure ages. Many bridges across the world built forty to fifty years ago are now in a deteriorated condition and need to be repaired and retrofitted. Visual inspections to identify damage or deterioration on a bridge are very important to assess the condition of the bridge and determine the need for repairing or for posting weight restrictions for the vehicles that use the bridge. These inspections require close physical access to the hard-to-reach areas of the bridge for physically measuring the damage which involves many resources in the form of experienced engineers, skilled labor, equipment, time, and money. The safety of the personnel involved in the inspections is also a major concern. Nowadays, a lot of research is being done in using Unmanned Aerial Vehicles (UAVs) like drones for bridge inspections and in using artificial intelligence for the detection of cracks on the images of concrete and steel members. Girders or beams in a bridge are the primary longitudinal load carrying members. Concrete inherently is weak in tension. To address this problem, High Strength steel reinforcement (called prestressing steel or prestressing strands) in prestressed concrete beams is pre-loaded with a tensile force before the application of any loads so that the regions which will experience tension under the service loads would be subjected to a pre-compression to improve the performance of the beam and delay cracking. Spalls are a type of corrosion damage on concrete members where portions of concrete fall off (section loss) due to corrosion in the steel reinforcement, exposing the reinforcement to the environment which leads to accelerated corrosion causing a loss of cross-sectional area and ultimately, a rupture in the steel. If the process of detecting the damage (cracks, spalls, exposed or severed reinforcement, etc.) is automated, the next logical step that would add great value would be, to quantify the effect of the damage detected on the load carrying capacity of the bridges. Using a quantified estimate of the remaining capacity of a bridge, determined after accounting for the corrosion damage, informed decisions can be made about the measures to be taken. This research proposes a stepwise framework to forge a link between a semi-automated visual inspection and residual capacity evaluation of actual prestressed concrete bridge girders obtained from two bridges that have been removed from service in Virginia due to extensive deterioration. 3D point clouds represent an object as a set of points on its surface in three dimensional space. These point clouds can be constructed either using laser scanning or using Photogrammetry from images of the girders captured with a digital camera. In this research, 3D point clouds are reconstructed from sequences of overlapping images of the girders using an approach called Structure from Motion (SfM) which locates matched pixels present between consecutive images in the 3D space. Crack-like features were automatically detected and highlighted on the images of the girders that were used to build the 3D point clouds using artificial intelligence (Neural Network). The images with cracks highlighted were applied as texture to the surface mesh on the point cloud to transfer the detail, color, and realism present in the images to the 3D model. Spalls were detected on 3D point clouds based on the orientation of the normals associated with the points with respect to the reference directions. Point clouds and textured meshes of the girders were scaled to real-world dimensions facilitating the measurement of any required dimension on the point clouds, eliminating the need for physical contact in condition assessment. Any cracks or spalls that went unidentified in the damage detection were visible on the textured meshes of the girders improving the performance of the approach. 3D textured mesh models of the girders overlaid with the detected cracks and spalls were used as 3D damage maps in residual strength estimation. Cross-sectional slices were extracted from the dense point clouds at various sections along the length of each girder. The slices were overlaid on the cross-section drawings of the girders, and the prestressing strands affected due to the corrosion damage were identified. They were reduced in cross-sectional area to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011) and were used in the calculation of the ultimate moment capacity of the girders using an approach called strain compatibility analysis. Estimated residual capacities were compared to the actual capacities of the girders found from destructive tests conducted by Al Rufaydah (2020). Comparisons are presented for the failure sections in these tests and the results were analyzed to evaluate the effectiveness of this framework. More research is to be done to determine the factors causing rupture in prestressing strands with different degrees of corrosion. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework.
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Lama, Salomon Abraham. "Digital State Models for Infrastructure Condition Assessment and Structural Testing." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/84502.
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This research introduces and applies the concept of digital state models for civil infrastructure condition assessment and structural testing. Digital state models are defined herein as any transient or permanent 3D model of an object (e.g. textured meshes and point clouds) combined with any electromagnetic radiation (e.g., visible light, infrared, X-ray) or other two-dimensional image-like representation. In this study, digital state models are built using visible light and used to document the transient state of a wide variety of structures (ranging from concrete elements to cold-formed steel columns and hot-rolled steel shear-walls) and civil infrastructures (bridges). The accuracy of digital state models was validated in comparison to traditional sensors (e.g., digital caliper, crack microscope, wire potentiometer). Overall, features measured from the 3D point clouds data presented a maximum error of ±0.10 in. (±2.5 mm); and surface features (i.e., crack widths) measured from the texture information in textured polygon meshes had a maximum error of ±0.010 in. (±0.25 mm). Results showed that digital state models have a similar performance between all specimen surface types and between laboratory and field experiments. Also, it is shown that digital state models have great potential for structural assessment by significantly improving data collection, automation, change detection, visualization, and augmented reality, with significant opportunities for commercial development. Algorithms to analyze and extract information from digital state models such as cracks, displacement, and buckling deformation are developed and tested. Finally, the extensive data sets collected in this effort are shared for research development in computer vision-based infrastructure condition assessment, eliminating the major obstacle for advancing in this field, the absence of publicly available data sets.Ph. D.
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Hänert, Stephan. "Entwicklung und Validierung methodischer Konzepte einer kamerabasierten Durchfahrtshöhenerkennung für Nutzfahrzeuge." 2019. https://tud.qucosa.de/id/qucosa%3A71402.
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Die vorliegende Arbeit beschäftigt sich mit der Konzeptionierung und Entwicklung eines neuartigen Fahrerassistenzsystems für Nutzfahrzeuge, welches die lichte Höhe von vor dem Fahrzeug befindlichen Hindernissen berechnet und über einen Abgleich mit der einstellbaren Fahrzeughöhe die Passierbarkeit bestimmt. Dabei werden die von einer Monokamera aufgenommenen Bildsequenzen genutzt, um durch indirekte und direkte Rekonstruktionsverfahren ein 3D-Abbild der Fahrumgebung zu erschaffen. Unter Hinzunahme einer Radodometrie-basierten Eigenbewegungsschätzung wird die erstellte 3D-Repräsentation skaliert und eine Prädiktion der longitudinalen und lateralen Fahrzeugbewegung ermittelt. Basierend auf dem vertikalen Höhenplan der Straßenoberfläche, welcher über die Aneinanderreihung mehrerer Ebenen modelliert wird, erfolgt die Klassifizierung des 3D-Raums in Fahruntergrund, Struktur und potentielle Hindernisse.
Die innerhalb des Fahrschlauchs liegenden Hindernisse werden hinsichtlich ihrer Entfernung und Höhe bewertet. Ein daraus abgeleitetes Warnkonzept dient der optisch-akustischen Signalisierung des Hindernisses im Kombiinstrument des Fahrzeugs. Erfolgt keine entsprechende Reaktion durch den Fahrer, so wird bei kritischen Hindernishöhen eine Notbremsung durchgeführt.
Die geschätzte Eigenbewegung und berechneten Hindernisparameter werden mithilfe von Referenzsensorik bewertet. Dabei kommt eine dGPS-gestützte Inertialplattform sowie ein terrestrischer und mobiler Laserscanner zum Einsatz. Im Rahmen der Arbeit werden verschiedene Umgebungssituationen und Hindernistypen im urbanen und ländlichen Raum untersucht und Aussagen zur Genauigkeit und Zuverlässigkeit des Verfahrens getroffen. Ein wesentlicher Einflussfaktor auf die Dichte und Genauigkeit der 3D-Rekonstruktion ist eine gleichmäßige Umgebungsbeleuchtung innerhalb der Bildsequenzaufnahme. Es wird in diesem Zusammenhang zwingend auf den Einsatz einer Automotive-tauglichen Kamera verwiesen. Die durch die Radodometrie bestimmte Eigenbewegung eignet sich im langsamen Geschwindigkeitsbereich zur Skalierung des 3D-Punktraums. Dieser wiederum sollte durch eine Kombination aus indirektem und direktem Punktrekonstruktionsverfahren erstellt werden. Der indirekte Anteil stützt dabei die Initialisierung des Verfahrens zum Start der Funktion und ermöglicht eine robuste Kameraschätzung. Das direkte Verfahren ermöglicht die Rekonstruktion einer hohen Anzahl an 3D-Punkten auf den Hindernisumrissen, welche zumeist die Unterkante beinhalten. Die Unterkante kann in einer Entfernung bis zu 20 m detektiert und verfolgt werden. Der größte Einflussfaktor auf die Genauigkeit der Berechnung der lichten Höhe von Hindernissen ist die Modellierung des Fahruntergrunds. Zur Reduktion von Ausreißern in der Höhenberechnung eignet sich die Stabilisierung des Verfahrens durch die Nutzung von zeitlich vorher zur Verfügung stehenden Berechnungen. Als weitere Maßnahme zur Stabilisierung wird zudem empfohlen die Hindernisausgabe an den Fahrer und den automatischen Notbremsassistenten mittels einer Hysterese zu stützen.
Das hier vorgestellte System eignet sich für Park- und Rangiervorgänge und ist als kostengünstiges Fahrerassistenzsystem interessant für Pkw mit Aufbauten und leichte Nutzfahrzeuge.The present work deals with the conception and development of a novel advanced driver assistance system for commercial vehicles, which estimates the clearance height of obstacles in front of the vehicle and determines the passability by comparison with the adjustable vehicle height. The image sequences captured by a mono camera are used to create a 3D representation of the driving environment using indirect and direct reconstruction methods. The 3D representation is scaled and a prediction of the longitudinal and lateral movement of the vehicle is determined with the aid of a wheel odometry-based estimation of the vehicle's own movement. Based on the vertical elevation plan of the road surface, which is modelled by attaching several surfaces together, the 3D space is classified into driving surface, structure and potential obstacles. The obstacles within the predicted driving tube are evaluated with regard to their distance and height. A warning concept derived from this serves to visually and acoustically signal the obstacle in the vehicle's instrument cluster. If the driver does not respond accordingly, emergency braking will be applied at critical obstacle heights. The estimated vehicle movement and calculated obstacle parameters are evaluated with the aid of reference sensors. A dGPS-supported inertial measurement unit and a terrestrial as well as a mobile laser scanner are used. Within the scope of the work, different environmental situations and obstacle types in urban and rural areas are investigated and statements on the accuracy and reliability of the implemented function are made. A major factor influencing the density and accuracy of 3D reconstruction is uniform ambient lighting within the image sequence. In this context, the use of an automotive camera is mandatory. The inherent motion determined by wheel odometry is suitable for scaling the 3D point space in the slow speed range. The 3D representation however, should be created by a combination of indirect and direct point reconstruction methods. The indirect part supports the initialization phase of the function and enables a robust camera estimation. The direct method enables the reconstruction of a large number of 3D points on the obstacle outlines, which usually contain the lower edge. The lower edge can be detected and tracked up to 20 m away. The biggest factor influencing the accuracy of the calculation of the clearance height of obstacles is the modelling of the driving surface. To reduce outliers in the height calculation, the method can be stabilized by using calculations from older time steps. As a further stabilization measure, it is also recommended to support the obstacle output to the driver and the automatic emergency brake assistant by means of hysteresis. The system presented here is suitable for parking and maneuvering operations and is interesting as a cost-effective driver assistance system for cars with superstructures and light commercial vehicles.
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Schauer, Marin Rodrigues Johannes. "Detecting Changes and Finding Collisions in 3D Point Clouds : Data Structures and Algorithms for Post-Processing Large Datasets." Doctoral thesis, 2020. https://doi.org/10.25972/OPUS-21428.
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Affordable prices for 3D laser range finders and mature software solutions for registering multiple point clouds in a common coordinate system paved the way for new areas of application for 3D point clouds. Nowadays we see 3D laser scanners being used not only by digital surveying experts but also by law enforcement officials, construction workers or archaeologists. Whether the purpose is digitizing factory production lines, preserving historic sites as digital heritage or recording environments for gaming or virtual reality applications -- it is hard to imagine a scenario in which the final point cloud must also contain the points of "moving" objects like factory workers, pedestrians, cars or flocks of birds. For most post-processing tasks, moving objects are undesirable not least because moving objects will appear in scans multiple times or are distorted due to their motion relative to the scanner rotation. The main contributions of this work are two postprocessing steps for already registered 3D point clouds. The first method is a new change detection approach based on a voxel grid which allows partitioning the input points into static and dynamic points using explicit change detection and subsequently remove the latter for a "cleaned" point cloud. The second method uses this cleaned point cloud as input for detecting collisions between points of the environment point cloud and a point cloud of a model that is moved through the scene. Our approach on explicit change detection is compared to the state of the art using multiple datasets including the popular KITTI dataset. We show how our solution achieves similar or better F1-scores than an existing solution while at the same time being faster. To detect collisions we do not produce a mesh but approximate the raw point cloud data by spheres or cylindrical volumes. We show how our data structures allow efficient nearest neighbor queries that make our CPU-only approach comparable to a massively-parallel algorithm running on a GPU. The utilized algorithms and data structures are discussed in detail. All our software is freely available for download under the terms of the GNU General Public license. Most of the datasets used in this thesis are freely available as well. We provide shell scripts that allow one to directly reproduce the quantitative results shown in this thesis for easy verification of our findingsKostengünstige Laserscanner und ausgereifte Softwarelösungen um mehrere Punktwolken in einem gemeinsamen Koordinatensystem zu registrieren, ermöglichen neue Einsatzzwecke für 3D Punktwolken. Heutzutage werden 3D Laserscanner nicht nur von Expert*innen auf dem Gebiet der Vermessung genutzt sondern auch von Polizist*innen, Bauarbeiter*innen oder Archäolog*innen. Unabhängig davon ob der Einsatzzweck die Digitalisierung von Fabrikanlagen, der Erhalt von historischen Stätten als digitaler Nachlass oder die Erfassung einer Umgebung für Virtual Reality Anwendungen ist - es ist schwer ein Szenario zu finden in welchem die finale Punktwolke auch Punkte von sich bewegenden Objekten enthalten soll, wie zum Beispiel Fabrikarbeiter*innen, Passant*innen, Autos oder einen Schwarm Vögel. In den meisten Bearbeitungsschritten sind bewegte Objekte unerwünscht und das nicht nur weil sie in mehrmals im gleichen Scan vorkommen oder auf Grund ihrer Bewegung relativ zur Scanner Rotation verzerrt gemessen werden. Der Hauptbeitrag dieser Arbeit sind zwei Nachverarbeitungsschritte für registrierte 3D Punktwolken. Die erste Methode ist ein neuer Ansatz zur Änderungserkennung basierend auf einem Voxelgitter, welche es erlaubt die Eingabepunktwolke in statische und dynamische Punkte zu segmentieren. Die zweite Methode nutzt die gesäuberte Punktwolke als Eingabe um Kollisionen zwischen Punkten der Umgebung mit der Punktwolke eines Modells welches durch die Szene bewegt wird zu erkennen. Unser Vorgehen für explizite Änderungserkennung wird mit dem aktuellen Stand der Technik unter Verwendung verschiedener Datensätze verglichen, inklusive dem populären KITTI Datensatz. Wir zeigen, dass unsere Lösung ähnliche oder bessere F1-Werte als existierende Lösungen erreicht und gleichzeitig schneller ist. Um Kollisionen zu finden erstellen wir kein Polygonnetz sondern approximieren die Punkte mit Kugeln oder zylindrischen Volumen. Wir zeigen wie unsere Datenstrukturen effiziente Nächste-Nachbarn-Suche erlaubt, die unsere CPU Lösung mit einer massiv-parallelen Lösung für die GPU vergleichbar macht. Die benutzten Algorithmen und Datenstrukturen werden im Detail diskutiert. Die komplette Software ist frei verfügbar unter den Bedingungen der GNU General Public license. Die meisten unserer Datensätze die in dieser Arbeit verwendet wurden stehen ebenfalls zum freien Download zur Verfügung. Wir publizieren ebenfalls all unsere Shell-Skripte mit denen die quantitativen Ergebnisse die in dieser Arbeit gezeigt werden reproduziert und verifiziert werden können
Book chapters on the topic "Cloud structure/detection"
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Gołosz, Mateusz, and Dariusz Mrozek. "Detection of Dangers in Human Health with IoT Devices in the Cloud and on the Edge." In Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis, 40–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19093-4_4.
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Zraenko, Sergey M. "Influence of Reflections from the Clouds and Artificial Structures on Fire Detection from Space." In Innovation and Discovery in Russian Science and Engineering, 21–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37514-0_2.
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Khaloo, Ali, and David Lattanzi. "Automatic Detection of Structural Deficiencies Using 4D Hue-Assisted Analysis of Color Point Clouds." In Conference Proceedings of the Society for Experimental Mechanics Series, 197–205. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74421-6_26.
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de Souza, Rogério Pinheiro, César A. Sierra-Franco, Paulo Ivson Netto Santos, Marina Polonia Rios, Daniel Luiz de Mattos Nascimento, and Alberto Barbosa Raposo. "Automatic Deformation Detection and Analysis Visualization of 3D Steel Structures in As-Built Point Clouds." In Human-Computer Interaction. Design and User Experience, 635–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49059-1_47.
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, Mamta, and Brij B. Gupta. "An Attribute-Based Searchable Encryption Scheme for Non-Monotonic Access Structure." In Handbook of Research on Intrusion Detection Systems, 263–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch013.
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Attribute based encryption (ABE) is a widely used technique with tremendous application in cloud computing because it provides fine-grained access control capability. Owing to this property, it is emerging as a popular technique in the area of searchable encryption where the fine-grained access control is used to determine the search capabilities of a user. But, in the searchable encryption schemes developed using ABE it is assumed that the access structure is monotonic which contains AND, OR and threshold gates. Many ABE schemes have been developed for non-monotonic access structure which supports NOT gate, but this is the first attempt to develop a searchable encryption scheme for the same. The proposed scheme results in fast search and generates secret key and search token of constant size and also the ciphertext components are quite fewer than the number of attributes involved. The proposed scheme is proven secure against chosen keyword attack (CKA) in selective security model under Decisional Bilinear Diffie-Hellman (DBDH) assumption.
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Han, Jun, Guodong Chen, Tao Liu, and Qian Yang. "Research on the Automatic Detection Method of Tunnel Clearance Based on Point Cloud Data." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200236.
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Due to the deformation of the tunnel and the abnormal outburst of internal facilities, the existing railway tunnel line needs to be inspected regularly. However, the existing detection methods have some shortcomings, such as large measurement interference, low efficiency, discontinuity of section, and independence with the track structure. Therefore, an automatic detection method of tunnel space clearance based on point cloud data is proposed. By fitting the central axis of the tunnel, the extraction can be realized at any position of the tunnel. The coordinate system of tunnel gauge detection based on rail top surface is established, and different types of tunnel gauge frames are introduced. The improved ray algorithm method is used to realize automatic detection and analysis of various tunnel types. Field experiments on existing railway tunnels show that the method can accurately obtain the limit point and size of the tunnel. The cross-section of transgression is obtained. It can meet the requirements of tunnel detection accuracy and has great practicability in tunnel disease detection.
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Liou, K. N., and Y. Gu. "Radiative Transfer in Cirrus Clouds: Light Scatting and Spectral Information." In Cirrus. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195130720.003.0017.
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The importance of cirrus clouds in climate has been recognized in the light of a number of intensive composite field observations: the First ISCCP Regional Experiment (FIRE) I in October-November 1986; FIRE II in November-December 1991; the European experiment on cirrus (ICE/EUCREX) in 1989; Subsonic Aircraft: Contrail and Cloud Effect Special Study (SUCCESS) in April 1996. Based on observations from the ground-based lidar and radar, airborne instrumentation, and satellites, cirrus clouds are typically located in the upper troposphere and lower stratosphere (Liou 1986). The formation, maintenance, and dissipation of cirrus clouds are directly associated with synoptic and mesoscale disturbances as well as related to deep cumulus outflows. Increases of high cloud cover have been reported at a number of urban airports in the United States based on surface observations spanning 40 years (Liou et al. 1990; Frankel et al. 1997). These increases have been attributed to the contrails and water vapor produced by jet airplane traffic. Satellite observations from NOAA polar-orbiting High-Resolution Infrared Radiation Sounder (HIRS) using the CO2 slicing method (Wylie et al. 1994) also show that cirrus cloud cover substantially increased between 60° S and 60° N during a 4-year period from June 1989 to September 1993. Understanding the role of cirrus clouds in climate must begin with reliable modeling of their radiative properties for incorporation in climate models as well as determination of the global variability of their composition, structure, and optical properties. Development of the remote sensing methodologies for the detection and retrieval of the ubiquitous visible and subvisual cirrus clouds requires the basic scattering, absorption, and polarization data for ice crystals in conjunction with appropriate radiative transfer models. We present the fundamentals involving radiative transfer in cirrus clouds and review pertinent research. In section 13.1, an overview of the subject of light scattering by ice crystals is presented in which we discuss a unification of the geometric optics approach for large ice particles and the finite-difference time domain numerical solution for small ice particles, referred to as the unified theory. Section 13.2 presents radiative transfer in cirrus clouds involving two unique properties: orientation of nonspherical ice crystals and cloud inhomogeneity.
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Itani, Wassim, Ayman Kayssi, and Ali Chehab. "Efficient Healthcare Integrity Assurance in the Cloud with Incremental Cryptography and Trusted Computing." In Cloud Technology, 845–57. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch039.
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In this chapter, the authors propose the design and implementation of an integrity-enforcement protocol for detecting malicious modification on Electronic Healthcare Records (EHRs) stored and processed in the cloud. The proposed protocol leverages incremental cryptography premises and trusted computing building blocks to support secure integrity data structures that protect the medical records while: (1) complying with the specifications of regulatory policies and recommendations, (2) highly reducing the mobile client energy consumption, (3) considerably enhancing the performance of the applied cryptographic mechanisms on the mobile client as well as on the cloud servers, and (4) efficiently supporting dynamic data operations on the EHRs.
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Li, Nanya, Guido Link, Junhui Ma, and John Jelonnek. "LiDAR Based Multi-Robot Cooperation for the 3D Printing of Continuous Carbon Fiber Reinforced Composite Structures." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210024.
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3D printing of lightweight continuous carbon fiber reinforced plastics (CCFRP) in three dimensions changes the traditional composite manufacturing processes. The continuous carbon fibers reinforced plastic filament can be printed along the load transmission path and significantly improve the strength of composite structures. Compared to the three-axis computer numerical controlled (CNC) machine based printing process, industrial robots provide the possibility to manufacture complex, spatial and large-scale composite structures. Here, the concept to use multi-robot to print complex spatial CCFRP components simultaneously has been presented. More than one 6 degrees of freedom industrial robots can cooperate with each other and solve the contradiction between structural complexity and printing reachability. During the printing process, the deformation of composite structures may happen, especially for the self-supporting components. Thus, in this paper, a Light Detection and Ranging (LiDAR) method is introduced to detect the deformation of printed composite structure and the movements of two UR robots. To obtain the point clouds of the printed structure, a LiDAR camera D435i has been installed on one robot. A new approach by combining coordinate transformation and iterative-closest-point (ICP) algorithm has been developed to merge the point clouds collected from different shooting angles of the camera.
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Chowdhury, Akash, Swastik Mukherjee, and Sourav Banerjee. "An Approach towards Survey and Analysis of Cloud Robotics." In Detecting and Mitigating Robotic Cyber Security Risks, 208–31. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2154-9.ch015.
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This chapter highlights the total structure and capabilities of robotic systems. This chapter then discusses the invocation of cloud technology in robotics technology empowering the whole system with higher processing power and bigger storage unit which was not possible earlier in the conventional robotic system being restricted in on-board manipulation. The flexibility of handling big data, ability to perform cloud computing, crowed sourcing and collaborative robot learning using the cloud robotics technology has been discussed briefly. This chapter describes concepts of Cloud Enabled Standalone Robotic System (CeSRS), Cloud Enabled Networked Robotic System (CeNRS), Cloud Robotic Networking System (CRNS), Standalone Robotic System (SRS), Common Networked Robotic (CNRS), Infrastructure As A Service (IAAS), Multi Robot System, R/R and R/C Network, ROS, Tele Operated Robotic System, Quality of Service (QoS), Virtual Machine (VM) and Cloud Datacenter. The existing applications of the cloud robotics technology are also described. However, the chapter focuses on the problems either inherited from the parent technology or appeared in the child technology. This chapter further recommends some solutions, new future directions and research aspects of the cloud robotics technology depending on the applications.
Conference papers on the topic "Cloud structure/detection"
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Uppal, Anmol, Vipul Sachdeva, and Seema Sharma. "Fake news detection using discourse segment structure analysis." In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2020. http://dx.doi.org/10.1109/confluence47617.2020.9058106.
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He, Chenhang, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang. "Structure Aware Single-Stage 3D Object Detection From Point Cloud." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01189.
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Yuan, Feng, Yee Hui Lee, Yu Song Meng, and Jin Teong Ong. "Detection of cloud vertical structure using water vapor pressure in tropical region." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325912.
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Wang, Renmin, and Qingsheng Zhu. "LSOF: Novel Outlier Detection Approach Based on Local Structure." In 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00124.
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Zhan, Qingming, Qiancong Pang, and Wenzhong Shi. "Automatic structure detection in a point-cloud of buildings obtained by terrestrial laser scanning." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.774718.
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Wang, Le, Shengquan Xie, Wenjun Xu, Bitao Yao, Jia Cui, Quan Liu, and Zude Zhou. "Human Point Cloud Inpainting for Industrial Human-Robot Collaboration Using Deep Generative Model." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8353.
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Abstract In complex industrial human-robot collaboration (HRC) environment, obstacles in the shared working space will occlude the operator, and the industrial robot will threaten the safety of the operator if it is unable to get the complete human spatial point cloud. This paper proposes a real-time human point cloud inpainting method based on the deep generative model. The method can recover the human point cloud occluded by obstacles in the shared working space to ensure the safety of the operator. The method proposed in this paper can be mainly divided into three parts: (i) real-time obstacles detection. This process can detect obstacle locations in real time and generate the image of obstacles. (ii) the application of the deep generative model algorithm. It is a complete convolutional neural network (CNN) structure and introduces advanced generative adversarial loss. The model can generate the missing depth data of operators at arbitrary position in the human depth image. (iii) spatial mapping of the depth image. The depth image will be mapped to point cloud by coordinate system conversion. The effectiveness of the method is verified by filling hole of the human point cloud occluded by obstacles in industrial HRC environment. The experiment results show that the proposed method can accurately generate the occluded human point cloud in real time and ensure the safety of the operator.
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Hu, Yazhe, and Tomonari Furukawa. "A Self-Supervised Learning Technique for Road Defects Detection Based on Monocular Three-Dimensional Reconstruction." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98135.
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Abstract This paper presents a self-supervised learning technique for road surface defects detection using a monocular camera. The uniqueness of the proposed technique relies on its self-supervised learning structure which is achieved by combining physics-driven three-dimensional (3D) reconstruction with data-driven Convolutional Neural Network (CNN). Only images from one camera are needed as the inputs to the model without human labeling. The 3D point cloud are reconstructed from input images based on a near-planar road 3D reconstruction process to self-supervise the learning process. During testing, the network receives images and predicts the images as defect or non-defect. A refined class prediction is produced by combining the 3D road surface data with the network output when the belief of original network prediction is not strong enough to conclude the classification. Experiments are conducted on real road surface images to find the optimal parameters for this model. The testing results demonstrate the robustness and effectiveness of the proposed self-supervised road surface defects detection technique.
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Khasawneh, Firas A., and Elizabeth Munch. "Exploring Equilibria in Stochastic Delay Differential Equations Using Persistent Homology." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35655.
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This paper explores the possibility of using techniques from topological data analysis for studying datasets generated from dynamical systems described by stochastic delay equations. The dataset is generated using Euler-Maryuama simulation for two first order systems with stochastic parameters drawn from a normal distribution. The first system contains additive noise whereas the second one contains parametric or multiplicative noise. Using Taken’s embedding, the dataset is converted into a point cloud in a high-dimensional space. Persistent homology is then employed to analyze the structure of the point cloud in order to study equilibria and periodic solutions of the underlying system. Our results show that the persistent homology successfully differentiates between different types of equilibria. Therefore, we believe this approach will prove useful for automatic data analysis of vibration measurements. For example, our approach can be used in machining processes for chatter detection and prevention.
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Kuehl, Joseph, and David Chelidze. "Invariant Manifold Detection From Phase Space Trajectories." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67473.
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Invariant manifolds provide important information about the structure of flows. When basins of attraction are present, the stable invariant manifold serves as the boundary between these basins. Thus, in experimental applications such as vibrations problems, knowledge of these manifolds is essential to understanding the evolution of phase space trajectories. Most existing methods for identifying invariant manifolds of a flow rely on knowledge of the flow field. However, in experimental applications only knowledge of phase space trajectories is available. We provide modifications to several existing invariant manifold detection methods which enables them to deal with trajectory only data, as well as introduce a new method based on the concept of phase space warping. The method of Stochastic Interrogation applied to the damped, driven Duffing equation is used to generate our data set. The result is a set of trajectory data which randomly populates a phase space. Manifolds are detected from this data set using several different methods. First is a variation on manifold “growing,” and is based on distance of closest approach to a hyperbolic trajectory with “saddle like behavior.” Second, three stretching based schemes are considered. One considers the divergence of trajectory pairs, another quantifies the deformation of a nearest neighbor cloud, and the last uses flow fields calculated from the trajectory data. Finally, the new phase space warping method is introduced. This method takes advantage of the shifting (warping) experienced by a phase space as the parameters of the system are slightly varied. This results in a shift of the invariant manifolds. The region spanned by this shift, provides a means to identify the invariant manifolds. Results show that this method gives superior detection and is robust with respect to the amount of data.
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Feraco, Stefano, Angelo Bonfitto, Nicola Amati, and Andrea Tonoli. "A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22339.
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Abstract This paper presents a clustering technique for the detection of the obstacles and lane boundaries on a road. The algorithm consists of two nested clustering stages. The first stage is based on hierarchical clustering, and the second on k-means clustering. The method exploits a preliminary ground-plane filtering algorithm to process the raw LIDAR point cloud, that is based on the semantic segmentation of point clouds. The clustering algorithm estimates the position of the obstacles that define the race track. Once the race track is sensed, the lane boundaries are detected. The method is validated experimentally on a four-wheel drive electric vehicle participating in the Formula SAE events. The validation environment is structured with traffic cones to define the race track.