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Remote Sensing of Atmospheric Conditions for Wind Energy Applications

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ISBN: 9783038979425 9783038979432 Year: Pages: 290 DOI: 10.3390/books978-3-03897-943-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

This Special Issue “Atmospheric Conditions for Wind Energy Applications” hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. Wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations is presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented..

Keywords

detached eddy simulation --- turbulence --- Lidar --- range gate length --- wind energy resources --- QuikSCAT --- WindSAT --- ASCAT --- global ocean --- wind energy --- resource assessment --- power performance testing --- wind turbine controls --- complex flow --- Doppler lidar --- coherent Doppler lidar --- wind sensing --- single-particle --- wind gusts --- Doppler lidar --- detecting and tracking --- impact prediction --- wind energy --- atmospheric boundary layer --- wind turbine wake --- wind lidar --- turbulence --- wake modeling --- field experiments --- wind energy --- atmospheric boundary layer --- wind turbine wake --- wind lidar --- virtual lidar --- turbulence --- wake modeling --- large-eddy simulations --- tropical cyclones --- Doppler Wind Lidar --- atmospheric boundary layer --- wind structure --- wind energy --- Doppler lidar --- wind turbine controls --- lidar-assisted control (LAC) --- IEA Wind Task 32 --- coastal wind measurement --- vertical Light Detection and Ranging --- NeoWins --- fetch effect --- Hazaki Oceanographical Research Station --- empirical equation --- complex terrain --- complex flow --- lidar --- VAD --- remote sensing --- wind energy --- Doppler lidar --- NWP model --- mesoscale --- Floating Lidar System (FLS), wind resource assessment --- wind atlas --- lidar --- wind --- Doppler --- aerosol --- motion estimation --- optical flow --- cross-correlation --- wind energy --- gust prediction --- variational analysis --- Doppler radar --- five-minute ahead wind power forecasting --- probabilistic forecasting --- remote sensing forecasting --- offshore wind speed forecasting --- wind energy --- remote sensing --- Doppler wind lidar --- velocity-azimuth-display algorithm --- resource assessment --- offshore --- turbulence intensity --- Doppler wind lidar --- wind energy --- aerosol --- wind turbine --- wind farm --- wake --- control --- complex terrain --- offshore

Visual Sensors

Authors: ---
ISBN: 9783039283385 9783039283392 Year: Pages: 738 DOI: 10.3390/books978-3-03928-339-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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Visual sensors are able to capture a large quantity of information from the environment around them. A wide variety of visual systems can be found, from the classical monocular systems to omnidirectional, RGB-D, and more sophisticated 3D systems. Every configuration presents some specific characteristics that make them useful for solving different problems. Their range of applications is wide and varied, including robotics, industry, agriculture, quality control, visual inspection, surveillance, autonomous driving, and navigation aid systems. In this book, several problems that employ visual sensors are presented. Among them, we highlight visual SLAM, image retrieval, manipulation, calibration, object recognition, navigation, etc.

Keywords

3D reconstruction --- RGB-D sensor --- non-rigid reconstruction --- pedestrian detection --- boosted decision tree --- scale invariance --- receptive field correspondence --- soft decision tree --- single-shot 3D shape measurement --- digital image correlation --- warp function --- inverse compositional Gauss-Newton algorithm --- UAV image --- dynamic programming --- seam-line --- optical flow --- image mosaic --- iris recognition --- presentation attack detection --- convolutional neural network --- support vector machines --- content-based image retrieval --- textile retrieval --- textile localization --- texture retrieval --- texture description --- visual sensors --- iris recognition --- iris segmentation --- semantic segmentation --- convolutional neural network (CNN) --- visible light and near-infrared light camera sensors --- laser sensor --- line scan camera --- lane marking detection --- support vector machine (SVM) --- image binarization --- lane marking reconstruction --- automated design --- vision system --- FOV --- illumination --- recognition algorithm --- action localization --- action segmentation --- 3D ConvNets --- LSTM --- visual sensors --- image retrieval --- hybrid histogram descriptor --- perceptually uniform histogram --- motif co-occurrence histogram --- omnidirectional imaging --- visual localization --- catadioptric sensor --- visual information fusion --- image processing --- underwater imaging --- embedded systems --- stereo vision --- visual odometry --- 3D reconstruction --- handshape recognition --- sign language --- finger alphabet --- skeletal data --- visual odometry --- ego-motion estimation --- stereo --- RGB-D --- mobile robots --- around view monitor (AVM) system --- automatic calibration --- lane marking --- parking assist system --- advanced driver assistance system (ADAS) --- pose estimation --- symmetry axis --- point cloud --- sweet pepper --- semantic mapping --- RGB-D SLAM --- visual mapping --- indoor visual SLAM --- adaptive model --- motion estimation --- stereo camera --- person re-identification --- end-to-end architecture --- appearance-temporal features --- Siamese network --- pivotal frames --- visual tracking --- correlation filters --- motion-aware --- adaptive update strategy --- confidence response map --- camera calibration --- Gray code --- checkerboard --- visual sensor --- image retrieval --- human visual system --- local parallel cross pattern --- pose estimation --- straight wing aircraft --- structure extraction --- consistent line clustering --- parallel line --- planes intersection --- salient region detection --- appearance based model --- regression based model --- human visual attention --- background dictionary --- quality control --- fringe projection profilometry --- depth image registration --- 3D reconstruction --- speed measurement --- stereo-vision --- large field of view --- vibration --- calibration --- CLOSIB --- statistical information of gray-levels differences --- Local Binary Patterns --- texture classification --- texture description --- Visual Sensors --- SLAM --- RGB-D --- indoor environment --- Manhattan frame estimation --- orientation relevance --- spatial transformation --- robotic welding --- seam tracking --- visual detection --- narrow butt joint --- GTAW --- LRF --- camera calibration --- extrinsic calibration --- sensors combination --- geometric moments --- camera pose --- rotation-angle --- measurement error --- robotics --- robot manipulation --- depth vision --- star image prediction --- star sensor --- Richardson-Lucy algorithm --- neural network --- tightly-coupled VIO --- SLAM --- fused point and line feature matching --- pose estimates --- simplified initialization strategy --- patrol robot --- map representation --- vision-guided robotic grasping --- object recognition --- pose estimation --- global feature descriptor --- iterative closest point --- n/a

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