Добірка наукової літератури з теми "Cylinder extraction"
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Статті в журналах з теми "Cylinder extraction"
Moradi, Saed, Denis Laurendeau, and Clement Gosselin. "Multiple Cylinder Extraction from Organized Point Clouds." Sensors 21, no. 22 (November 17, 2021): 7630. http://dx.doi.org/10.3390/s21227630.
Повний текст джерелаJunwu, Wu, and Yin Zhongjun. "Numerical Investigation on Vortex-Induced Vibration Energy Extraction Efficiency of Double Circular Cylinders In Tandem Arrangement at Low Reynolds Number." MATEC Web of Conferences 153 (2018): 05001. http://dx.doi.org/10.1051/matecconf/201815305001.
Повний текст джерелаKarim, F., B. Farouk, and I. Namer. "Natural Convection Heat Transfer From a Horizontal Cylinder Between Vertical Confining Adiabatic Walls." Journal of Heat Transfer 108, no. 2 (May 1, 1986): 291–98. http://dx.doi.org/10.1115/1.3246918.
Повний текст джерелаKasten, Jens, Christoph Petz, Ingrid Hotz, Hans-Christian Hege, Bernd R. Noack, and Gilead Tadmor. "Lagrangian feature extraction of the cylinder wake." Physics of Fluids 22, no. 9 (September 2010): 091108. http://dx.doi.org/10.1063/1.3483220.
Повний текст джерелаTilton, Nils, and Denis Martinand. "Taylor–Couette–Poiseuille flow with a weakly permeable inner cylinder: absolute instabilities and selection of global modes." Journal of Fluid Mechanics 849 (June 26, 2018): 741–76. http://dx.doi.org/10.1017/jfm.2018.437.
Повний текст джерелаYuxian, Zhang, Yang mengke, Wang Hong, and Liu Binbin. "Design and Finite Element Analysis of Water Jet Energy Accumulator Barrel." MATEC Web of Conferences 153 (2018): 06011. http://dx.doi.org/10.1051/matecconf/201815306011.
Повний текст джерелаLiu, Yuanpeng. "GEOMETRIC PARAMETERS EXTRACTION OF SPHERE, CYLINDER AND CONE." Chinese Journal of Mechanical Engineering 41, no. 11 (2005): 144. http://dx.doi.org/10.3901/jme.2005.11.144.
Повний текст джерелаShibata, Shinpei, and Shota Kisaka. "On the angular momentum extraction from the rotation powered pulsars." Monthly Notices of the Royal Astronomical Society 507, no. 1 (August 2, 2021): 1055–63. http://dx.doi.org/10.1093/mnras/stab2206.
Повний текст джерелаNishi, Yoshiki, Yuta Ueno, Masachika Nishio, Luis Antonio Rodrigues Quadrante, and Kentaroh Kokubun. "Power extraction using flow-induced vibration of a circular cylinder placed near another fixed cylinder." Journal of Sound and Vibration 333, no. 10 (May 2014): 2863–80. http://dx.doi.org/10.1016/j.jsv.2014.01.007.
Повний текст джерелаSun, Yun Ling, Sheng Jie Wang, and Hong Xiang Tian. "The Research on Weak Fault Time Domain Feature Extraction of Diesel Engine Instantaneous Speed." Applied Mechanics and Materials 687-691 (November 2014): 1026–29. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1026.
Повний текст джерелаДисертації з теми "Cylinder extraction"
Georgiev, Kristiyan. "REALTIME MAPPING AND SCENE RECONSTRUCTION BASED ON MID-LEVEL GEOMETRIC FEATURES." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/296059.
Повний текст джерелаPh.D.
Robot mapping is a major field of research in robotics. Its basic task is to combine (register) spatial data, usually gained from range devices, to a single data set. This data set is called global map and represents the environment, observed from different locations, usually without knowledge of their positions. Various approaches can be classified into groups based on the type of sensor, e.g. Lasers, Microsoft Kinect, Stereo Image Pair. A major disadvantage of current methods is the fact, that they are derived from hardly scalable 2D approaches that use a small amount of data. However, 3D sensing yields a large amount of data in each 3D scan. Autonomous mobile robots have limited computational power, which makes it harder to run 3D robot mapping algorithms in real-time. To remedy this limitation, the proposed research uses mid-level geometric features (lines and ellipses) to construct 3D geometric primitives (planar patches, cylinders, spheres and cones) from 3D point data. Such 3D primitives can serve as distinct features for faster registration, allowing real-time performance on a mobile robot. This approach works in real-time, e.g. using a Microsoft Kinect to detect planes with 30 frames per second. While previous approaches show insufficient performance, the proposed method operates in real-time. In its core, the algorithm performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model using a three stage approach. The first step inspects 1.5D sub spaces, to find lines and ellipses. The next stage uses these lines and ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidates are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. The real-time performance suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping. In this thesis, I will show how these features are derived and used for scene registration. Optimal registration is determined by finding plane-feature correspondence based on mutual similarity and geometric constraints. Our approach determines the plane correspondence in three steps. First step computes the distance between all pairs of planes from the first scan to all pair of planes from the second scan. The distance function captures angular, distance and co-planarity differences. The resulting distances are accumulated in a distance matrix. The next step uses the distance matrix to compute the correlation matrix between planes from the first and second scan. Finally plane correspondence is found by finding the global optimal assignment from the correlation matrix. After finding the plane correspondence, an optimal pose registration is computed. In addition to that, I will provide a comparison to existing state-of-the-art algorithms. This work is part of an industry collaboration effort sponsored by the National Institute of Standards and Technology (NIST), aiming at performance evaluation and modeling of autonomous navigation in unstructured and dynamic environments. Additional field work, in the form of evaluation of real robotic systems in a robot test arena was performed.
Temple University--Theses
Georgiev, Kristiyan. "GALATEA_RESETAct2.mp4." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/296060.
Повний текст джерелаPh.D.
Robot mapping is a major field of research in robotics. Its basic task is to combine (register) spatial data, usually gained from range devices, to a single data set. This data set is called global map and represents the environment, observed from different locations, usually without knowledge of their positions. Various approaches can be classified into groups based on the type of sensor, e.g. Lasers, Microsoft Kinect, Stereo Image Pair. A major disadvantage of current methods is the fact, that they are derived from hardly scalable 2D approaches that use a small amount of data. However, 3D sensing yields a large amount of data in each 3D scan. Autonomous mobile robots have limited computational power, which makes it harder to run 3D robot mapping algorithms in real-time. To remedy this limitation, the proposed research uses mid-level geometric features (lines and ellipses) to construct 3D geometric primitives (planar patches, cylinders, spheres and cones) from 3D point data. Such 3D primitives can serve as distinct features for faster registration, allowing real-time performance on a mobile robot. This approach works in real-time, e.g. using a Microsoft Kinect to detect planes with 30 frames per second. While previous approaches show insufficient performance, the proposed method operates in real-time. In its core, the algorithm performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model using a three stage approach. The first step inspects 1.5D sub spaces, to find lines and ellipses. The next stage uses these lines and ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidates are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. The real-time performance suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping. In this thesis, I will show how these features are derived and used for scene registration. Optimal registration is determined by finding plane-feature correspondence based on mutual similarity and geometric constraints. Our approach determines the plane correspondence in three steps. First step computes the distance between all pairs of planes from the first scan to all pair of planes from the second scan. The distance function captures angular, distance and co-planarity differences. The resulting distances are accumulated in a distance matrix. The next step uses the distance matrix to compute the correlation matrix between planes from the first and second scan. Finally plane correspondence is found by finding the global optimal assignment from the correlation matrix. After finding the plane correspondence, an optimal pose registration is computed. In addition to that, I will provide a comparison to existing state-of-the-art algorithms. This work is part of an industry collaboration effort sponsored by the National Institute of Standards and Technology (NIST), aiming at performance evaluation and modeling of autonomous navigation in unstructured and dynamic environments. Additional field work, in the form of evaluation of real robotic systems in a robot test arena was performed.
Temple University--Theses
Georgiev, Kristiyan. "IROS2013_video_submission.mp4." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/296061.
Повний текст джерелаPh.D.
Robot mapping is a major field of research in robotics. Its basic task is to combine (register) spatial data, usually gained from range devices, to a single data set. This data set is called global map and represents the environment, observed from different locations, usually without knowledge of their positions. Various approaches can be classified into groups based on the type of sensor, e.g. Lasers, Microsoft Kinect, Stereo Image Pair. A major disadvantage of current methods is the fact, that they are derived from hardly scalable 2D approaches that use a small amount of data. However, 3D sensing yields a large amount of data in each 3D scan. Autonomous mobile robots have limited computational power, which makes it harder to run 3D robot mapping algorithms in real-time. To remedy this limitation, the proposed research uses mid-level geometric features (lines and ellipses) to construct 3D geometric primitives (planar patches, cylinders, spheres and cones) from 3D point data. Such 3D primitives can serve as distinct features for faster registration, allowing real-time performance on a mobile robot. This approach works in real-time, e.g. using a Microsoft Kinect to detect planes with 30 frames per second. While previous approaches show insufficient performance, the proposed method operates in real-time. In its core, the algorithm performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model using a three stage approach. The first step inspects 1.5D sub spaces, to find lines and ellipses. The next stage uses these lines and ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidates are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. The real-time performance suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping. In this thesis, I will show how these features are derived and used for scene registration. Optimal registration is determined by finding plane-feature correspondence based on mutual similarity and geometric constraints. Our approach determines the plane correspondence in three steps. First step computes the distance between all pairs of planes from the first scan to all pair of planes from the second scan. The distance function captures angular, distance and co-planarity differences. The resulting distances are accumulated in a distance matrix. The next step uses the distance matrix to compute the correlation matrix between planes from the first and second scan. Finally plane correspondence is found by finding the global optimal assignment from the correlation matrix. After finding the plane correspondence, an optimal pose registration is computed. In addition to that, I will provide a comparison to existing state-of-the-art algorithms. This work is part of an industry collaboration effort sponsored by the National Institute of Standards and Technology (NIST), aiming at performance evaluation and modeling of autonomous navigation in unstructured and dynamic environments. Additional field work, in the form of evaluation of real robotic systems in a robot test arena was performed.
Temple University--Theses
Georgiev, Kristiyan. "RTPAlabama-PromoVideo.mp4." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/296062.
Повний текст джерелаPh.D.
Robot mapping is a major field of research in robotics. Its basic task is to combine (register) spatial data, usually gained from range devices, to a single data set. This data set is called global map and represents the environment, observed from different locations, usually without knowledge of their positions. Various approaches can be classified into groups based on the type of sensor, e.g. Lasers, Microsoft Kinect, Stereo Image Pair. A major disadvantage of current methods is the fact, that they are derived from hardly scalable 2D approaches that use a small amount of data. However, 3D sensing yields a large amount of data in each 3D scan. Autonomous mobile robots have limited computational power, which makes it harder to run 3D robot mapping algorithms in real-time. To remedy this limitation, the proposed research uses mid-level geometric features (lines and ellipses) to construct 3D geometric primitives (planar patches, cylinders, spheres and cones) from 3D point data. Such 3D primitives can serve as distinct features for faster registration, allowing real-time performance on a mobile robot. This approach works in real-time, e.g. using a Microsoft Kinect to detect planes with 30 frames per second. While previous approaches show insufficient performance, the proposed method operates in real-time. In its core, the algorithm performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model using a three stage approach. The first step inspects 1.5D sub spaces, to find lines and ellipses. The next stage uses these lines and ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidates are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. The real-time performance suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping. In this thesis, I will show how these features are derived and used for scene registration. Optimal registration is determined by finding plane-feature correspondence based on mutual similarity and geometric constraints. Our approach determines the plane correspondence in three steps. First step computes the distance between all pairs of planes from the first scan to all pair of planes from the second scan. The distance function captures angular, distance and co-planarity differences. The resulting distances are accumulated in a distance matrix. The next step uses the distance matrix to compute the correlation matrix between planes from the first and second scan. Finally plane correspondence is found by finding the global optimal assignment from the correlation matrix. After finding the plane correspondence, an optimal pose registration is computed. In addition to that, I will provide a comparison to existing state-of-the-art algorithms. This work is part of an industry collaboration effort sponsored by the National Institute of Standards and Technology (NIST), aiming at performance evaluation and modeling of autonomous navigation in unstructured and dynamic environments. Additional field work, in the form of evaluation of real robotic systems in a robot test arena was performed.
Temple University--Theses
Menshov, Anton. "Novel single-source surface integral equations for scattering on 2-D penetrable cylinders and current flow modeling in 2-D and 3-D conductors." IEEE, 2012. http://hdl.handle.net/1993/23439.
Повний текст джерелаWang, Hao. "Wave Energy Extraction from an Oscillating Water Column in a Truncated Circular Cylinder." Thesis, 2013. http://hdl.handle.net/1969.1/151188.
Повний текст джерелаGao, Di. "Identification and location derivation of grapevine features through point clouds." Thesis, 2014. http://hdl.handle.net/2440/99572.
Повний текст джерелаThesis (M.Eng.Sc.) -- University of Adelaide, School of Mechanical Engineering, 2014.
WU, ZHU-SONG, and 吳竹松. "Data consistency checking for mechanical CAD files of prismatic parts with cylindrical faces:constructing mechanical parts by cylinder extraction." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/55255202339452448691.
Повний текст джерелаЧастини книг з теми "Cylinder extraction"
Zhao, Xiuxu, Zhemin Hu, Rui Li, Chuanli Zhou, and Jihai Jiang. "Internal Leakage Fault Feature Extraction of Hydraulic Cylinder Using Wavelet Packet Energy." In Communications in Computer and Information Science, 363–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53962-6_32.
Повний текст джерелаWang, Fengli, Shulin Duan, and Hongliang Yu. "Fault Feature Extraction of Cylinder-Piston Wear in Diesel Engine with EMD." In Advances in Intelligent and Soft Computing, 419–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30223-7_65.
Повний текст джерелаBAO, XIAO-LING. "RESONANCE EXTRACTION, PHASE MATCHING METHOD AND THE SURFACE PATHS FOR FINITE ELASTIC CYLINDERS." In Series on Stability, Vibration and Control of Systems, Series B, 329–47. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811950_0011.
Повний текст джерелаТези доповідей конференцій з теми "Cylinder extraction"
Cochet, Christophe, and Ronald W. Yeung. "Dynamic Analysis and Configuration Design of a Two-Component Wave-Energy Absorber." In ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/omae2012-83613.
Повний текст джерелаInui, Masatomo, and Nobuyuki Umezo. "Extraction of Vertical Cylinder Contacting Area for Motorcycle Safety Verification." In CAD'17. CAD Solutions LLC, 2017. http://dx.doi.org/10.14733/cadconfp.2017.149-153.
Повний текст джерелаTakahashi, Hideharu, Hiroshige Kikura, Kenji Takeshita, and Masanori Aritomi. "Visualization of Dispersed Phase Flow in Centrifugal Extractor Using Taylor-Couette Vortex Flow." In ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44403.
Повний текст джерелаJbira, Ibtissem, Aicha Ben Makhlouf, Borhen Louhich, Antoine Tahan, Mohamed Ali Mahjoub, and Dominique Deneux. "A Comparative Study of Extraction Cylinder Features in Industrial Point Clouds." In 2019 23rd International Conference Information Visualisation (IV). IEEE, 2019. http://dx.doi.org/10.1109/iv.2019.00079.
Повний текст джерелаProenca, Pedro F., and Yang Gao. "Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593516.
Повний текст джерелаHenningsson, Maria, Bo Bernhardsson, Per Tunestal, and Rolf Johansson. "A Machine Learning Approach to Information Extraction from Cylinder Pressure Sensors." In SAE 2012 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2012. http://dx.doi.org/10.4271/2012-01-0440.
Повний текст джерелаMalefaki, Iro, and Efstathios Konstantinidis. "Optimal Damping for Energy Extraction From Drag-Aided Vortex-Induced Motions." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78394.
Повний текст джерелаWang, Lu, Daewoong Son, and Ronald W. Yeung. "On the Performance of a Dual-Cylinder Wave-Energy Converter: Single Versus Two Degrees of Freedom." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54422.
Повний текст джерелаZanganeh, Hossein, and Narakorn Srinil. "Two-Dimensional Coupled Vortex-Induced Vibration of Circular Cylinder: Prediction and Extraction of Hydrodynamics Properties." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10240.
Повний текст джерелаAllison, James T., Allen Kaitharath, and Daniel R. Herber. "Wave Energy Extraction Maximization Using Direct Transcription." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86619.
Повний текст джерелаЗвіти організацій з теми "Cylinder extraction"
Fisher, S., and N. McFerran. Nuclear Safeguards: Feature Extraction for Machine Learning Enrichment Analysis of UF6 Cylinders. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812580.
Повний текст джерела