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Auswahl der wissenschaftlichen Literatur zum Thema „Inversion identification“
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Zeitschriftenartikel zum Thema "Inversion identification"
Corbett-Detig, Russell B., Iskander Said, Maria Calzetta, Max Genetti, Jakob McBroome, Nicholas W. Maurer, Vincenzo Petrarca, Alessandra della Torre und Nora J. Besansky. „Fine-Mapping Complex Inversion Breakpoints and Investigating Somatic Pairing in the Anopheles gambiae Species Complex Using Proximity-Ligation Sequencing“. Genetics 213, Nr. 4 (30.10.2019): 1495–511. http://dx.doi.org/10.1534/genetics.119.302385.
Der volle Inhalt der QuelleTAROUDAKIS, MICHAEL I. „IDENTIFYING MODAL ARRIVALS IN SHALLOW WATER FOR BOTTOM GEOACOUSTIC INVERSIONS“. Journal of Computational Acoustics 08, Nr. 02 (Juni 2000): 307–24. http://dx.doi.org/10.1142/s0218396x00000224.
Der volle Inhalt der QuelleShukla, Sanjay K., Jennifer Kislow, Adam Briska, John Henkhaus und Colin Dykes. „Optical Mapping Reveals a Large Genetic Inversion between Two Methicillin-Resistant Staphylococcus aureus Strains“. Journal of Bacteriology 191, Nr. 18 (19.06.2009): 5717–23. http://dx.doi.org/10.1128/jb.00325-09.
Der volle Inhalt der QuelleDay, Tanya K., Guoxin Zeng, Antony M. Hooker, Madhava Bhat, David R. Turner und Pamela J. Sykes. „Extremely Low Doses of X-Radiation can Induce Adaptive Responses in Mouse Prostate“. Dose-Response 5, Nr. 4 (01.10.2007): dose—response.0. http://dx.doi.org/10.2203/dose-response.07-019.day.
Der volle Inhalt der QuelleYin, Peng-Yeng, Ray-I. Chang, Rong-Fuh Day, Yen-Cheng Lin und Ching-Yuan Hu. „Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion“. Applied Sciences 12, Nr. 1 (22.12.2021): 71. http://dx.doi.org/10.3390/app12010071.
Der volle Inhalt der QuelleHuang, Li-Feng, Cheng-Guo Liu, Zhi-Peng Wu, Li-Jun Zhang, Hong-Guang Wang, Qing-Lin Zhu, Jie Han und Ming-Chen Sun. „Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals“. Remote Sensing 15, Nr. 14 (17.07.2023): 3577. http://dx.doi.org/10.3390/rs15143577.
Der volle Inhalt der QuelleSchulz, Jonas, Philipp Aziz und Hans‐Jörg Bart. „Identification of Phase Inversion on Sieve Trays“. Chemie Ingenieur Technik 93, Nr. 7 (30.03.2021): 1080–87. http://dx.doi.org/10.1002/cite.202000140.
Der volle Inhalt der QuelleShenvi, Neil, J. M. Geremia und Herschel Rabitz. „Nonlinear Kinetic Parameter Identification through Map Inversion“. Journal of Physical Chemistry A 106, Nr. 51 (Dezember 2002): 12315–23. http://dx.doi.org/10.1021/jp021762e.
Der volle Inhalt der QuelleLi, Xiao Long, Jun Jing Zhang, Fu Ming Wang und Bei Zhang. „Identification of Surrounding Rock Parameters Based on MSVR“. Applied Mechanics and Materials 580-583 (Juli 2014): 1227–31. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1227.
Der volle Inhalt der QuelleZhou, Dapeng, Zeyu Jin und Guoqiang Wu. „Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain“. Applied Sciences 13, Nr. 12 (08.06.2023): 6951. http://dx.doi.org/10.3390/app13126951.
Der volle Inhalt der QuelleDissertationen zum Thema "Inversion identification"
Markusson, Ola. „Model and System Inversion with Applications in Nonlinear System Identification and Control“. Doctoral thesis, KTH, Signals, Sensors and Systems, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3287.
Der volle Inhalt der QuelleStakvik, Jon Åge. „Identification, Inversion and Implementaion of the Preisach Hysteresis Model in Nanopositioning“. Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25744.
Der volle Inhalt der QuelleCognet, Jean-Marc. „Inversion sismique : identification du signal source et modélisation des réflexions multiples“. Paris 9, 2001. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2001PA090016.
Der volle Inhalt der QuelleVu, Tuan-Anh. „One-shot inversion methods and domain decomposition“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAE009.
Der volle Inhalt der QuelleOur main goal is to analyze the convergence of a gradient-based optimization method, to solve inverse problems for parameter identification, in which the corresponding forward and adjoint problems are solved by an iterative solver. Coupling the iterations for the three unknowns (the inverse problem parameter, the forward problem solution and the adjoint problem solution) yields the so-called one-shot inversion methods. Many numerical experiments showed that using very few inner iterations for the forward and adjoint problems may still lead to a good convergence for the inverse problem. This motivates us to develop a rigorous convergence theory for one-shot methods using a fixed small number of inner iterations, with a semi-implicit scheme for the parameter update and a regularized cost functional. Our theory covers a general class of linear inverse problems in the finite-dimensional discrete setting, for which the forward and adjoint problems are solved by generic fixed point iteration methods. By studying the spectral radius of the block iteration matrix of the coupled iterations, we prove that for sufficiently small descent steps the (semi-implicit) one-shot methods converge. In particular, in the scalar case, where the unknowns belong to one-dimensional spaces, we establish not only sufficient but even necessary convergence conditions on the descent step. Next, we apply one-shot methods to (linearized and then non-linear) inverse conductivity problems, and solve the forward and adjoint problems by domain decomposition methods, more specifically nonoverlapping optimized Schwarz methods. We analyze a domain decomposition algorithm that simultaneously calculates the forward and adjoint solutions for a given conductivity. By combining this algorithm with the gradient descent parameter update, we obtain a domain decomposition one-shot method that solves the inverse problem. We propose two discretized versions of the coupled algorithm, the second of which (in the case of the linearized inverse conductivity problem) falls into the abstract framework of our convergence theory. Finally, several numerical experiments are provided to illustrate the performance of the one-shot methods, in comparison with the classical gradient descent in which the forward and adjoint problems are solved using direct solvers. In particular, we observe that, even in the case of noisy data, very few inner iterations may still guarantee good convergence of the one-shot methods
Komandur, Deepak K. „Load Identification using Matrix Inversion Method (MIM) for Transfer Path Analysis (TPA)“. University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563872419648032.
Der volle Inhalt der QuelleArnst, Maarten. „Inversion of probabilistic models of structures using measured transfer functions“. Châtenay-Malabry, Ecole centrale de Paris, 2007. http://www.theses.fr/2007ECAP1037.
Der volle Inhalt der QuelleThe aim of this thesis is to develop a methodology for the experimental identification of probabilistic models for the dynamical behaviour of structures. The inversion of probabilistic structural models with minimal parameterization, introduced by Soize, from measured transfer functions is in particular considered. It is first shown that the classical methods of estimation from the theory of mathematical statistics, such as the method of maximum likelihood, are not well-adapted to formulate and solve this inverse problem. In particular, numerical difficulties and conceptual problems due to model misspecification are shown to prohibit the application of the classical methods. The inversion of probabilistic structural models is then formulated alternatively as the minimization, with respect to the parameters to be identified, of an objective function measuring a distance between the experimental data and the probabilistic model. Two principles of construction for the definition of this distance are proposed, based on either the loglikelihood function, or the relative entropy. The limitation of the distance to low-order marginal laws is demonstrated to allow to circumvent the aforementioned difficulties. The methodology is applied to examples featuring simulated data and to a civil and environmental engineering case history featuring real experimental data
Romain, Sandra. „Identification, génotypage et représentation des variants de structure dans les pangénomes“. Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/71b8c90f-bac9-4948-9bb1-a4b6d953f322.
Der volle Inhalt der QuelleStructural variants (SVs), genomic variations of more than 50 bp, contribute significantly to genetic diversity and species evolution. Accurate detection and genotyping SVs is crucial to understanding their role in phenotypic variation and adaptation. Variation graphs (VGs) and pangenome graphs (PGs), which represent genomic variations as alternative paths in a graph, offer a promising approach for the analysis of SVs. This thesis explores the use of VGs and PGs for the detection and genotyping of SVs, focusing on a complex of four species of alpine Coenonympha butterflies. Two bioinformatics tools were developed during this thesis: (1) SVJedi-graph, the first long-read SV genotyper using a VG to represent SVs, providing a genotyping accuracy superior to state-of-the-art tools, particularly for close and overlapping SVs, and (2) INVPG-annot, a tool for identifying inversions in PGs, which demonstrated that inversions are represented by different topologies in PGs depending on the construction tool used. Comparative analysis of the Coenonympha butterfly genomes identified twelve large inversions (≥ 100 kbp) between the four species, some of which could play a role in the reproductive isolation and local adaptation of two of these species. While the PG-based approach offers advantages for genome comparison, challenges remain for the analysis of large variants such as inversions
Arnst, Maarten. „Inversion de modèles probabilistes de structures à partir de fonctionsde transfert expérimentales“. Phd thesis, Ecole Centrale Paris, 2007. http://tel.archives-ouvertes.fr/tel-00238573.
Der volle Inhalt der QuelleFoddis, Maria Laura. „Application of artificial neural networks in hydrogeology : Identification of unknown pollution sources in contaminated aquifers“. Strasbourg, 2011. https://publication-theses.unistra.fr/public/theses_doctorat/2011/FODDIS_Maria_Laura_2011.pdf.
Der volle Inhalt der Quelle[. . . ]In many cases, some hydrogeological and groundwater quality characteristics, are not directly measurable and must be physically assessed in function of directly measurable parameters. The problem of determining the unknown model parameters is usually identified as "inverse problem". Solving the inverse problem is the main goal of modeling groundwater flow and contaminant transport. The validity of an aquifer forecasting model is closely related to the reliability and accuracy of the parameters assessment. With respect to the resolution of the inverse problem, this work aims at defining a methodology that allows to identify the features in space and time of unknown contamination sources. In our case, the inverse problem is solved on the basis of measurements of contaminant concentrations in monitoring wells located in the studied areas. In the framework of this thesis, the research is developed under the following themes: - groundwater contamination modeling using a non-commercial software for the flux and transport model in porous media. - modeling of the cause and effect relationships in groundwater contamination with Artificial Neural Networks (ANN) technology. - application of ANN to solve the inverse problem in two cases of groundwater contamination. Over the past decades, Artificial Neural Networks (ANN) have become increasingly popular as a problem solving tool and have been extensively used as a forecasting tool in many disciplines. […]
Leleu, Claire. „Sismique très haute résolution tri-dimensionnelle : identification de la position du dispositif d'acquisition par une reformulation en temps“. Paris 9, 2001. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2001PA090021.
Der volle Inhalt der QuelleBücher zum Thema "Inversion identification"
Identity and Idolatry: The Image of God and Its Inversion. InterVarsity Press, 2015.
Den vollen Inhalt der Quelle findenCarson, D. A., und Richard Lints. Identity and Idolatry: The Image of God and Its Inversion. InterVarsity Press, 2015.
Den vollen Inhalt der Quelle findenIdentity and idolatry: The image of God and its inversion. Downers Grove, IL: InterVarsity Press, 2015.
Den vollen Inhalt der Quelle findenMcKinlay Gardner, R. J., und David J. Amor. Complex Chromosomal Rearrangements. Herausgegeben von R. J. McKinlay Gardner und David J. Amor. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199329007.003.0010.
Der volle Inhalt der QuelleBuchteile zum Thema "Inversion identification"
Mirzaei, M., J. W. Bredewout und R. K. Snieder. „Gravity Data Inversion Using the Subspace Method“. In Parameter Identification and Inverse Problems in Hydrology, Geology and Ecology, 187–98. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1704-0_11.
Der volle Inhalt der QuelleFang, Zhang, Zhou Hong und Wang Erbing. „Matrix Inversion Method for Load Identification in Transfer Paths Analysis“. In Lecture Notes in Electrical Engineering, 517–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27311-7_69.
Der volle Inhalt der QuelleSindi, Suzanne S., und Benjamin J. Raphael. „Identification and Frequency Estimation of Inversion Polymorphisms from Haplotype Data“. In Lecture Notes in Computer Science, 418–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02008-7_30.
Der volle Inhalt der QuelleAykan, Murat, und H. Nevzat Özgüven. „Parametric Identification of Nonlinearity from Incomplete FRF Data Using Describing Function Inversion“. In Topics in Nonlinear Dynamics, Volume 3, 311–22. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2416-1_25.
Der volle Inhalt der QuelleLombaerts, Thomas, Ping Chu und Jan Albert (Bob) Mulder. „Flight Control Reconfiguration Based on Online Physical Model Identification and Nonlinear Dynamic Inversion“. In Lecture Notes in Control and Information Sciences, 363–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11690-2_13.
Der volle Inhalt der QuelleLombaerts, Thomas, Ping Chu, Hafid Smaili, Olaf Stroosma und Jan Albert (Bob) Mulder. „Piloted Evaluation Results of a Nonlinear Dynamic Inversion Based Controller Using Online Physical Model Identification“. In Lecture Notes in Control and Information Sciences, 477–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11690-2_17.
Der volle Inhalt der QuelleHuang, Li-juan, Bo Jiang, Rong Yang, Kun Kang, Rong-biao Tan, Jin-yu Zhou, Rui-ning Li, Shi-yu Zhou und Deng-bi Ding. „Application of Seismic Waveform Difference Inversion and Characteristic Parameter Simulation in Shale Gas Dessert Identification“. In Springer Series in Geomechanics and Geoengineering, 223–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2485-1_23.
Der volle Inhalt der QuelleZhu, Weizhu, Xi Chu und Xin Duan. „Computer-Vision-Based Structure Shape Monitoring of Bridges Using Natural Texture Feature Tracking“. In Lecture Notes in Civil Engineering, 445–54. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2532-2_38.
Der volle Inhalt der QuelleCacciola, Matteo, Maurizio Campolo, Fabio La Foresta, Francesco Carlo Morabito und Mario Versaci. „A Kernel Based Learning by Sample Technique for Defect Identification Through the Inversion of a Typical Electric Problem“. In Lecture Notes in Computer Science, 243–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74829-8_30.
Der volle Inhalt der QuelleSingh, Ankit, Rohtash Kumar, Amritansh Rai, Shatrughan Singh, Raghav Singh, Satya Prakash und Pnkhudi Thakur. „Determination and Identification of Focal Mechanism Solutions for the 2016 Kumamoto Earthquake from Waveform Inversion Using ISOLA Software“. In Recent Developments in Earthquake Seismology, 165–78. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-47538-2_12.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Inversion identification"
Elkhamry, A., Eduard Bikchandaev, M. Fouda und A. Taher. „Sand Channel Characterization and Fault Identification Utilizing Ultra-Deep Resistivity 3D Inversion in Complex Clastic Reservoirs“. In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24299-ea.
Der volle Inhalt der QuelleBuland, Arild, Martin Landroø, Roger Sollie, Mona Andersen und Terje Dahl. „Lithology identification by AVO inversion“. In SEG Technical Program Expanded Abstracts 1995. Society of Exploration Geophysicists, 1995. http://dx.doi.org/10.1190/1.1887621.
Der volle Inhalt der QuelleWeglein, Arthur, Paul B. Viloette und Timothy H. Keho. „Artifact identification in multiparameter born inversion“. In 1985 SEG Technical Program Expanded Abstracts. SEG, 1985. http://dx.doi.org/10.1190/1.1892633.
Der volle Inhalt der QuelleElkhamry, Ayman, Ahmed Taher, Eduard Bikchandaev und Mohamed Fouda. „Real-Time 3D Anisotropy Analysis Enables Lithology Identification at Distance“. In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0052.
Der volle Inhalt der QuelleSeidl, Robert, und Ernst Rank. „Full waveform inversion for ultrasonic flaw identification“. In 43RD ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLUME 36. Author(s), 2017. http://dx.doi.org/10.1063/1.4974657.
Der volle Inhalt der QuelleLiu*, Yong, Shangxu Wang, Sanyi Yuan, Nan Tian und Junzhou Liu. „Inversion Spectral Decomposition with Channel Complex Identification“. In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5843824.1.
Der volle Inhalt der QuelleLiu, Guoyan, Kun Gao, Xuefeng Liu und Guoqiang Ni. „RMB identification based on polarization parameters inversion imaging“. In Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), herausgegeben von Xiangang Luo, Tianchun Ye, Tingwen Xin, Song Hu, Minghui Hong und Min Gu. SPIE, 2016. http://dx.doi.org/10.1117/12.2241651.
Der volle Inhalt der QuelleBarclay, F., B. Bailey und A. Paxton. „Prospect Identification Using AVO Inversion and Lithology Prediction“. In 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010. European Association of Geoscientists & Engineers, 2010. http://dx.doi.org/10.3997/2214-4609.201400888.
Der volle Inhalt der QuelleHongqian Sun, Yujie J. Ding und Ioulia B. Zotova. „Identification of weak inversion and inversion-rotational transitions within excited vibrational state of ammonia“. In 2008 Conference on Lasers and Electro-Optics (CLEO). IEEE, 2008. http://dx.doi.org/10.1109/cleo.2008.4552250.
Der volle Inhalt der QuelleHe, Weikun, Renbiao Wu und Jiaxue Liu. „Identification method of EM property inversion for multilayer media“. In 2011 IEEE Radar Conference (RadarCon). IEEE, 2011. http://dx.doi.org/10.1109/radar.2011.5960648.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Inversion identification"
INVERSION METHOD OF UNCERTAIN PARAMETERS FOR TRUSS STRUCTURES BASED ON GRAPH NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, Dezember 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.5.
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