Auswahl der wissenschaftlichen Literatur zum Thema „Geometrical constraints“
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Zeitschriftenartikel zum Thema "Geometrical constraints":
TROMBETTONI, GILLES, und MARTA WILCZKOWIAK. „GPDOF — A FAST ALGORITHM TO DECOMPOSE UNDER-CONSTRAINED GEOMETRIC CONSTRAINT SYSTEMS: APPLICATION TO 3D MODELING“. International Journal of Computational Geometry & Applications 16, Nr. 05n06 (Dezember 2006): 479–511. http://dx.doi.org/10.1142/s0218195906002154.
Suzuki, H., T. Ito, H. Ando, K. Kikkawa und F. Kimura. „Solving regional constraints in components layout design based on geometric gadgets“. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, Nr. 4 (September 1997): 343–53. http://dx.doi.org/10.1017/s0890060400003267.
Hördt, Andreas, Katharina Bairlein, Matthias Bücker und Hermann Stebner. „Geometrical constraints for membrane polarization“. Near Surface Geophysics 15, Nr. 6 (01.10.2017): 579–92. http://dx.doi.org/10.3997/1873-0604.2017053.
Pauly, Daniel. „Geometrical constraints on body size“. Trends in Ecology & Evolution 12, Nr. 11 (November 1997): 442. http://dx.doi.org/10.1016/s0169-5347(97)85745-x.
Evans, A. K. D., I. K. Wehus, Ø. Grøn und Ø. Elgarøy. „Geometrical constraints on dark energy“. Astronomy & Astrophysics 430, Nr. 2 (20.01.2005): 399–410. http://dx.doi.org/10.1051/0004-6361:20041590.
de Luis-García, Rodrigo, Carl-Fredrik Westin und Carlos Alberola-López. „Geometrical constraints for robust tractography selection“. NeuroImage 81 (November 2013): 26–48. http://dx.doi.org/10.1016/j.neuroimage.2013.04.096.
Maia, M. D., und G. S. Silva. „Geometrical constraints on the cosmological constant“. Physical Review D 50, Nr. 12 (15.12.1994): 7233–38. http://dx.doi.org/10.1103/physrevd.50.7233.
Peng, Heping, und Zhuoqun Peng. „Concurrent design and process tolerances determination in consideration of geometrical tolerances“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, Nr. 19-20 (01.08.2019): 6727–40. http://dx.doi.org/10.1177/0954406219866866.
SATO, Yuki, Takayuki YAMADA, Kazuhiro IZUI und Shinji NISHIWAKI. „Topology optimization with geometrical constraints based on fictitious physical models (The geometrical constraint for molding and milling)“. Transactions of the JSME (in Japanese) 83, Nr. 851 (2017): 17–00081. http://dx.doi.org/10.1299/transjsme.17-00081.
Dong, Yan, und Mei Li. „The Geometrical Feature Recognition Method of Part Drawing“. Advanced Materials Research 415-417 (Dezember 2011): 523–26. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.523.
Dissertationen zum Thema "Geometrical constraints":
Nestoras, Konstantinos Nav E. Massachusetts Institute of Technology. „A tool to create hydrodynamically optimized hull-forms with geometrical constraints from internal arrangements“. Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81587.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 145-146).
Internal arrangements and bulky equipment like machinery have been treated for many years as a secondary aspect of the ship design. Traditionally, in the design process, the centerpiece of the effort is the hull and its hydrodynamic performance. Once the hull of a ship has been selected, all the other systems, like propulsion and electric plants, are selected and fitted in the ship. Due to the fact that the hull is considered as the most important system of the ship, any compromises and systems trade-offs that need to be done in the design process are focused mainly on all the systems apart from the hull-form. This inherent prioritization in the traditional design process, might lead to the selection of suboptimal solutions for the other systems like the propulsion and electric plants, which in turns might lead to a global suboptimal solution for the whole ship design. Unfortunately, these decisions bound the designed ship for lifetime and, down the road, might lead to excess operational costs. The tool developed in this thesis treats the internal arrangements and the hull-form of the ship as two systems that need to be optimized together and not on a decoupled manner. Thus, the selection of the propulsion and electric plants or even large weapon systems like VLCs becomes as important as the hull during the design process. Propulsion and electric systems can be preselected in the early stage design, based on their efficiency and then a hull can be wrapped around them. The optimization of the hull can be done either with the use of the Holtrop method or a potential flow panel method, which provides higher fidelity. The designer has the ability to utilize this tool in order to easily conduct trade-off studies between the internal arrangements and the hull-form or save time from their integration and allocate it in other important problems of the design. This could aid the decision-making process in the early stage of the design, where information is scarce, decisions are crucial and uncertainty is high.
by Konstantinos Nestoras.
S.M.
Nav.E.
Nesselroth, Susan Marian. „I Substituent effects on carbanion photophysics An application of the energy gap law : II Solvent and geometrical constraints on excited state proton transfer“. Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/30331.
Wang, Bihao. „Geometrical and contextual scene analysis for object detection and tracking in intelligent vehicles“. Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2197/document.
For autonomous or semi-autonomous intelligent vehicles, perception constitutes the first fundamental task to be performed before decision and action/control. Through the analysis of video, Lidar and radar data, it provides a specific representation of the environment and of its state, by extracting key properties from sensor data with time integration of sensor information. Compared to other perception modalities such as GPS, inertial or range sensors (Lidar, radar, ultrasonic), the cameras offer the greatest amount of information. Thanks to their versatility, cameras allow intelligent systems to achieve both high-level contextual and low-level geometrical information about the observed scene, and this is at high speed and low cost. Furthermore, the passive sensing technology of cameras enables low energy consumption and facilitates small size system integration. The use of cameras is however, not trivial and poses a number of theoretical issues related to how this sensor perceives its environmen. In this thesis, we propose a vision-only system for moving object detection. Indeed,within natural and constrained environments observed by an intelligent vehicle, moving objects represent high risk collision obstacles, and have to be handled robustly. We approach the problem of detecting moving objects by first extracting the local contextusing a color-based road segmentation. After transforming the color image into illuminant invariant image, shadows as well as their negative influence on the detection process can be removed. Hence, according to the feature automatically selected onthe road, a region of interest (ROI), where the moving objects can appear with a high collision risk, is extracted. Within this area, the moving pixels are then identified usin ga plane+parallax approach. To this end, the potential moving and parallax pixels a redetected using a background subtraction method; then three different geometrical constraints : the epipolar constraint, the structural consistency constraint and the trifocaltensor are applied to such potential pixels to filter out parallax ones. Likelihood equations are also introduced to combine the constraints in a complementary and effectiveway. When stereo vision is available, the road segmentation and on-road obstacles detection can be refined by means of the disparity map with geometrical cues. Moreover, in this case, a robust tracking algorithm combining image and depth information has been proposed. If one of the two cameras fails, the system can therefore come back to a monocular operation mode, which is an important feature for perception system reliability and integrity. The different proposed algorithms have been tested on public images data set with anevaluation against state-of-the-art approaches and ground-truth data. The obtained results are promising and show that the proposed methods are effective and robust on the different traffic scenarios and can achieve reliable detections in ambiguous situations
Rohmer, Damien. „Géométrie active pour l'animation et la modélisation“. Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00635079.
Seth, Abhishek. „Combining physical constraints with geometric constraint-based modeling for virtual assembly“. [Ames, Iowa : Iowa State University], 2007.
Lie, Chin Cheong Patrick. „Geometrically constrained matching schemes“. Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=39316.
Baltsavias, Emmanuel P. Baltsavias Emmanuel P. Baltsavias Emmanuel P. „Multiphoto geometrically constrained matching /“. Zürich, 1991. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=9561.
Coulter, Stewart. „Representation of geometric constraints in parametric synthesis“. Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/17982.
Phipps, Richard L. „Some Geometric Constraints on Ring-Width Trend“. Tree-Ring Society, 2005. http://hdl.handle.net/10150/262639.
Ma'ani-Hessari, Nason J. „Design of quadruplex DNA through geometric constraints“. Thesis, University of Ulster, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551558.
Bücher zum Thema "Geometrical constraints":
International Workshop on Shock Wave Focusing Phenomena in Combustible Mixtures: Ignition and Transition to Detonation of Reactive Media under Geometrical Constraints (1998 Aachen, Germany). Proceedings of the International Workshop on Shock Wave Focusing Phenomena in Combustible Mixtures: Ignition and Transition to Detonation of Reactive Media under Geometrical Constraints, December 15-16, 1998. Aachen: Shaker, 2000.
International Workshop on Shock Wave Focusing Phenomena in Combustible Mixtures (1998 Aachen, Germany). Proceedings of the International Workshop on Shock Wave Focusing Phenomena in Combustible Mixtures: Ignition and transition to detonation of reactive media under geometrical constraints : December 15 to 16, 1998. Aachen, Germany: Shock Wave Laboratory, 2000.
Govaerts, Jan. Hamiltonian quantisation and constrained dynamics. Leuven (Belgium): Leuven University Press, 1991.
Mann, Peter. Constrained Hamiltonian Dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0021.
Succi, Sauro. Out of Legoland: Geoflexible Lattice Boltzmann Equations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0023.
Andersson, Nils. Gravitational-Wave Astronomy. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198568032.001.0001.
Buchteile zum Thema "Geometrical constraints":
Helwani, Karim. „Geometrical Constraints“. In T-Labs Series in Telecommunication Services, 67–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08954-6_6.
Rozvany, G. I. N., und M. Zhou. „COC Methods for Additional Geometrical Constraints“. In Shape and Layout Optimization of Structural Systems and Optimality Criteria Methods, 41–56. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-2788-9_4.
Nigam, Aditya, und Phalguni Gupta. „Palmprint Recognition Using Geometrical and Statistical Constraints“. In Advances in Intelligent Systems and Computing, 1303–15. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1602-5_136.
Giorgi, G., P. J. G. Teunissen, S. Verhagen und P. J. Buist. „Integer Ambiguity Resolution with Nonlinear Geometrical Constraints“. In International Association of Geodesy Symposia, 39–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22078-4_6.
Cooper, David H., Christopher J. Taylor, Jim Graham und Tim F. Cootes. „Locating Overlapping Flexible Shapes Using Geometrical Constraints“. In BMVC91, 185–92. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-1921-0_24.
Liégeois, Alain. „Structure of robots: geometrical and mechanical constraints“. In Performance and Computer-Aided Design, 81–135. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4684-6852-6_4.
Schöllhorn, R. „Geometrical and Electronic Constraints in Redox Intercalation Systems“. In Chemical Reactions in Organic and Inorganic Constrained Systems, 323–40. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4582-1_25.
Bank, Bernd, Teresa Krick, Reinhard Mandel und Pablo Solernó. „A geometrical bound for integer programming with polynomial constraints“. In Fundamentals of Computation Theory, 121–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54458-5_56.
Higashi, Masatake, Hiroki Senga, Atsuhide Nakamura und Mamoru Hosaka. „Parametric Design Method Based on Topological and Geometrical Constraints“. In From Geometric Modeling to Shape Modeling, 165–80. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35495-8_13.
Damme, H., P. Levitz und L. Gatineau. „Energetical and Geometrical Constraints on Adsorption and Reaction Kinetics on Clay Surfaces“. In Chemical Reactions in Organic and Inorganic Constrained Systems, 283–304. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4582-1_22.
Konferenzberichte zum Thema "Geometrical constraints":
„GEOMETRICAL CONSTRAINTS FOR LIGAND POSITIONING“. In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003166002040209.
Lazkoz, Ruth, Mauricio Carbajal, Luis Manuel Montaño, Oscar Rosas-Ortiz, Sergio A. Tomas Velazquez und Omar Miranda. „Geometrical Constraints on Dark Energy Models“. In Advanced Summer School in Physics 2007. AIP, 2007. http://dx.doi.org/10.1063/1.2825127.
Cooper, David H., Christopher J. Taylor, Jim Graham und Tim F. Cootes. „Locating Overlapping Flexible Shapes Using Geometrical Constraints“. In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.24.
Morgera, S. D. „On noisy pattern matching under geometrical constraints“. In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226226.
Zhang, Hanchao, und Jinhua Xu. „Supervised sparse coding with local geometrical constraints“. In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178362.
Gruen, Armin W., und Emmanuel P. Baltsavias. „Adaptive Least Squares Correlation With Geometrical Constraints“. In 1985 International Technical Symposium/Europe, herausgegeben von Olivier D. Faugeras und Robert B. Kelley. SPIE, 1986. http://dx.doi.org/10.1117/12.952246.
Plateaux, Régis, Olivia Penas, Faïda Mhenni, Jean-Yves Choley und Alain Riviere. „Introduction of the 3D Geometrical Constraints in Modelica“. In The 7 International Modelica Conference, Como, Italy. Linköping University Electronic Press, 2009. http://dx.doi.org/10.3384/ecp09430038.
Zhang, Wei, Xiaochun Cao, Zhiyong Feng, Jiawan Zhang und Ping Wang. „Detecting photographic composites using two-view geometrical constraints“. In 2009 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2009. http://dx.doi.org/10.1109/icme.2009.5202685.
Hagita, Katsumi, und Hiroshi Takano. „Dynamics of a polymer chain under geometrical constraints“. In The 8th tohwa university international symposium on slow dynamics in complex systems. AIP, 1999. http://dx.doi.org/10.1063/1.58567.
Le, Van-Hung, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thi-Thanh-Hai Tran, Michiel Vlaminck, Wilfried Philips und Peter Veelaert. „3D Object Finding Using Geometrical Constraints on Depth Images“. In 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2015. http://dx.doi.org/10.1109/kse.2015.17.
Berichte der Organisationen zum Thema "Geometrical constraints":
Toroker, Z., V. M. Malkin, G. M. Fraiman, A. A. Balakin und N. J. Fisch. Geometrical Constraints on Plasma Couplers for Raman Compression. Office of Scientific and Technical Information (OSTI), Juli 2012. http://dx.doi.org/10.2172/1056829.
Ogawa, Naohisa. Diffusion Under Geometrical Constraint. Jgsp, 2014. http://dx.doi.org/10.7546/jgsp-34-2014-35-49.
Ogawa, Naohisa. Diffusion Under Geometrical Constraint. GIQ, 2014. http://dx.doi.org/10.7546/giq-15-2014-204-217.
Theiler, J., und B. G. Henderson. A geometrical constraint on shadowing in rough surfaces. Office of Scientific and Technical Information (OSTI), Oktober 1997. http://dx.doi.org/10.2172/532451.
Parikh, Jo A., und Anne Werkheiser. Incorporating Geometric Constraints into Rule-Based Systems Using Nonlinear Optimization. Fort Belvoir, VA: Defense Technical Information Center, Januar 1994. http://dx.doi.org/10.21236/ada275093.
GENERAL ELECTRIC CO SCHENECTADY NY. Representation and Recognition with Invariants and Geometric Constraint Models. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada263235.
Mundy, Joseph L. Representation and Recognition with Algebraic Invariants and Geometric Constraint Models. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1993. http://dx.doi.org/10.21236/ada282926.
Mundy, Joseph L. Representation and Recognition with Algebraic Invariants and Geometric Constraint Models. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada271395.
Yan, Yujie, und Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, Mai 2021. http://dx.doi.org/10.17760/d20410114.