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Academic literature on the topic 'Inversion identification'

Author: Grafiati

Published: 21 December 2024

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Journal articles on the topic "Inversion identification"

1

Corbett-Detig, Russell B., Iskander Said, Maria Calzetta, Max Genetti, Jakob McBroome, Nicholas W. Maurer, Vincenzo Petrarca, Alessandra della Torre, and Nora J. Besansky. "Fine-Mapping Complex Inversion Breakpoints and Investigating Somatic Pairing in the Anopheles gambiae Species Complex Using Proximity-Ligation Sequencing." Genetics 213, no. 4 (October 30, 2019): 1495–511. http://dx.doi.org/10.1534/genetics.119.302385.

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Chromosomal inversions are fundamental drivers of genome evolution. In the main Afrotropical malaria vector species, belonging to the Anopheles gambiae species complex, inversions play an important role in local adaptation and have a rich history of cytological study. Despite the importance and ubiquity of some chromosomal inversions across the species complex, inversion breakpoints are often challenging to map molecularly due to the presence of large repetitive regions. Here, we develop an approach that uses Hi-C sequencing data to molecularly fine-map the breakpoints of inversions. We demonstrate that this approach is robust and likely to be widely applicable for both identification and fine-mapping inversion breakpoints in species whose inversions have heretofore been challenging to characterize. We apply our method to interrogate the previously unknown inversion breakpoints of 2Rbc and 2Rd in An. coluzzii. We found that inversion breakpoints occur in large repetitive regions, and, strikingly, among three inversions analyzed, two breakpoints appear to be reused in two separate inversions. These breakpoint-adjacent regions are strongly enriched for the presence of a 30 bp satellite repeat sequence. Because low frequency inversion breakpoints are not correlated with genomic regions containing this satellite, we suggest that interrupting this particular repeat may result in arrangements with higher relative fitness. Additionally, we use heterozygous individuals to quantitatively investigate the impacts of somatic pairing in the regions immediately surrounding inversion breakpoints. Finally, we discuss important considerations for possible applications of this approach for inversion breakpoint identification in a range of organisms.

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TAROUDAKIS, MICHAEL I. "IDENTIFYING MODAL ARRIVALS IN SHALLOW WATER FOR BOTTOM GEOACOUSTIC INVERSIONS." Journal of Computational Acoustics 08, no. 02 (June 2000): 307–24. http://dx.doi.org/10.1142/s0218396x00000224.

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A mode identification process that can be applied to broadband acoustic transmissions in the ocean is presented. The process is associated with a modal travel time inversion scheme for geoacoustic or tomographic inversions. The process is based on the assumption that a reference (background) environment is known and the identification process is based on information on the group (modal) velocities of the reference environment only. Using two characteristic examples corresponding to shallow water environments it is shown that the identification process works well at least for the lower order modes.

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Shukla, Sanjay K., Jennifer Kislow, Adam Briska, John Henkhaus, and Colin Dykes. "Optical Mapping Reveals a Large Genetic Inversion between Two Methicillin-Resistant Staphylococcus aureus Strains." Journal of Bacteriology 191, no. 18 (June 19, 2009): 5717–23. http://dx.doi.org/10.1128/jb.00325-09.

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ABSTRACT Staphylococcus aureus is a highly versatile and evolving bacterium of great clinical importance. S. aureus can evolve by acquiring single nucleotide polymorphisms and mobile genetic elements and by recombination events. Identification and location of novel genomic elements in a bacterial genome are not straightforward, unless the whole genome is sequenced. Optical mapping is a new tool that creates a high-resolution, in situ ordered restriction map of a bacterial genome. These maps can be used to determine genomic organization and perform comparative genomics to identify genomic rearrangements, such as insertions, deletions, duplications, and inversions, compared to an in silico (virtual) restriction map of a known genome sequence. Using this technology, we report here the identification, approximate location, and characterization of a genetic inversion of ∼500 kb of a DNA element between the NRS387 (USA800) and FPR3757 (USA300) strains. The presence of the inversion and location of its junction sites were confirmed by site-specific PCR and sequencing. At both the left and right junction sites in NRS387, an IS1181 element and a 73-bp sequence were identified as inverted repeats, which could explain the possible mechanism of the inversion event.

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Day, Tanya K., Guoxin Zeng, Antony M. Hooker, Madhava Bhat, David R. Turner, and Pamela J. Sykes. "Extremely Low Doses of X-Radiation can Induce Adaptive Responses in Mouse Prostate." Dose-Response 5, no. 4 (October 1, 2007): dose—response.0. http://dx.doi.org/10.2203/dose-response.07-019.day.

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The pKZ1 mouse chromosomal inversion assay is the only assay that has detected modulation of a mutagenic endpoint after single whole body X-irradiation with doses lower than 1 mGy. A non-linear dose response for chromosomal inversion has been observed in spleen and prostate between 0.001 mGy and 10 mGy, with doses between 0.005–0.01 mGy causing an increase in inversions and doses between 1–10 mGy causing a reduction below spontaneous inversion frequency. An adaptive response is a decreased biological effect induced by a low radiation dose. Adaptive responses contradict the linear-no-threshold model of risk estimation. We demonstrated that very low (0.001 mGy, 0.01 mGy, 1 mGy and 10 mGy) doses of X-radiation induced a chromosomal inversion adaptive response as measured by a reduction in the frequency of subsequent high dose (1000 mGy) induced inversions in prostate. These are the lowest X-radiation doses reported to induce an adaptive response for any endpoint. Adaptive response experiments were also performed where the high dose was administered four hours prior to a low dose of 0.01 mGy or 10 mGy. In both cases an adaptive response was observed. Identification of the modifying factors involved in the adaptive response may provide candidates for radioprotection.

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Yin, Peng-Yeng, Ray-I. Chang, Rong-Fuh Day, Yen-Cheng Lin, and Ching-Yuan Hu. "Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion." Applied Sciences 12, no. 1 (December 22, 2021): 71. http://dx.doi.org/10.3390/app12010071.

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The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor.

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Huang, Li-Feng, Cheng-Guo Liu, Zhi-Peng Wu, Li-Jun Zhang, Hong-Guang Wang, Qing-Lin Zhu, Jie Han, and Ming-Chen Sun. "Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals." Remote Sensing 15, no. 14 (July 17, 2023): 3577. http://dx.doi.org/10.3390/rs15143577.

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Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters.

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Schulz, Jonas, Philipp Aziz, and Hans‐Jörg Bart. "Identification of Phase Inversion on Sieve Trays." Chemie Ingenieur Technik 93, no. 7 (March 30, 2021): 1080–87. http://dx.doi.org/10.1002/cite.202000140.

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Shenvi, Neil, J. M. Geremia, and Herschel Rabitz. "Nonlinear Kinetic Parameter Identification through Map Inversion." Journal of Physical Chemistry A 106, no. 51 (December 2002): 12315–23. http://dx.doi.org/10.1021/jp021762e.

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Li, Xiao Long, Jun Jing Zhang, Fu Ming Wang, and Bei Zhang. "Identification of Surrounding Rock Parameters Based on MSVR." Applied Mechanics and Materials 580-583 (July 2014): 1227–31. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1227.

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An inversion method based on multi-output support vector regression (MSVR) is proposed for identifying the mechanical parameters of surrounding rock. This method considers the surrounding rock as a multi-output system during excavation, and the surveyed rock deformations of each monitoring section as its output. First, perform numerical experiments based on the principle of orthogonal test to obtain the calculated deformation values corresponding to different rock parameter combinations, and use them as the samples for training the model of MSVR as reflecting the nonlinear mapping relationship between rock and its deformations. Second, use the PSO to seek the optimal rock parameters based on measured deformations of rock mass. An example is employed to test the presented inversion method. The results showed that compared with the inversion method based on single-output support vector regression (SSVR), the proposed one is more inclined to reach the global optimization goals and achieve more reliable inversion results due to its consideration of the inherent correlativity among the measured deformations of each monitoring section.

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Zhou, Dapeng, Zeyu Jin, and Guoqiang Wu. "Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain." Applied Sciences 13, no. 12 (June 8, 2023): 6951. http://dx.doi.org/10.3390/app13126951.

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The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

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Dissertations / Theses on the topic "Inversion identification"

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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.

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Stakvik, 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.

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Atomic force microscopes offer the possibility for imaging a sample topography with accuracy in the nanometer scale. This kind of performance is impossible without actuators enabling a certain accuracy. A frequently used actuator in such nanopositioning applications is the piezoelectric actuator which, although precise, exhibit the unwanted phenomenon of hysteresis. Hysteresis is recognized as the main nonlinearity in the piezoelectric actuator, and introduces severe control issues for accurate positioning.To compensate for hysteresis an accurate model of the nonlinearity has to be identified. A discrete and a piecewise-continuous model, based on the Preisach operator, is identified with a constraint least squares method. An algorithm for computing the output of the continuous model is proposed and outlined together with a solution for the discrete model. The general approach for control of hysteresis is to create a right inverse of the identified model. Since the Preisach operator does not have an analytical inverse, this is done numerically by a closest match algorithm. These algorithms are described based on previous literature.In order to achieve a well designed model for the Preisach operator, the input function has to be thoughtfully designed. It is pointed out that to achieve a persistently excited input signal, the number of reversals has to be sufficient compared to the discretization level of the implemented Preisach operator. Additionally, effects from systematic errors in the signal loop, such as negative phase due to low pass filtering, time delay, creep and actuator dynamics, has to be minimized. It was shown that negative phase in the signal loop can invalidate the identified models.Both the discrete and continuous model was shown to describe the hysteresis nonlinearity. To reduce the step-size of each discrete value, a higher discretization level can be applied to the discrete model. The inverse models were simulated to verify the inversion algorithms, followed by results from testing the inverse signal. The inverted signal based on the discrete method showed need for interpolation between the discrete levels, while the continuous inverse exhibit higher frequency components that causing vibrations due to resonance dynamics. An input signal consisting of the same amount of reversals as the discretization level was shown to give a valid identified model. Furthermore, increasing the number of reversals was shown to continue improving the accuracy. The effect of negative phase, from low pass filtering and time delay, was tested, and changes in the distribution of the Preisach weights were shown. Such effects can cause significant errors in identification of hysteresis models, especially if the reference frequency is subject to changes.

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Cognet, 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.

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Vu, Tuan-Anh. "One-shot inversion methods and domain decomposition." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAE009.

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Notre objectif principal est d’analyser la convergence d’une méthode d’optimisation basée sur le gradient, pour résoudre des problèmes inverses d’identification de paramètres, dans laquelle les problèmes directs et adjoints correspondants sont résolus par un solveur itératif. Le couplage des itérations pour les trois inconnues (le paramètre du problème inverse, la solution du problème direct et la solution du problème adjoint) donne ce que l’on appelle les méthodes d’inversion de type one-shot. De nombreux tests numériques ont montré que l’utilisation de très peu d’itérations internes pour les problèmes directs et adjoints peut néanmoins conduire à une bonne convergence pour le problème inverse. Cela nous motive à développer une théorie de convergence rigoureuse pour les méthodes de type one-shot en utilisant un petit nombre fixe d’itérations internes, avec un schéma semi-implicite pour la mise à jour du paramètre et une fonction de coût régularisée. Notre théorie couvre une classe générale de problèmes inverses linéaires dans le cadre discret de dimension finie, pour lesquels les problèmes directs et adjoints sont résolus par des méthodes génériques d’itération de point fixe. En étudiant le rayon spectral de la matrice par blocs des itérations couplées, nous prouvons que pour des pas de descente suffisamment petits, les méthodes de type one-shot (semi-implicites) convergent. En particulier, dans le cas scalaire, où les inconnues appartiennent à des espaces à une dimension, nous établissons des conditions de convergence suffisantes et même nécessaires sur le pas de descente. Ensuite, nous appliquons des méthodes de type one-shot aux problèmes inverses de conductivité (linéarisés et puis non linéaires), et résolvons les problèmes directs et adjoints par des méthodes de décomposition de domaines, plus spécifiquement des méthodes de Schwarz optimisées sans recouvrement. Nous analysons un algorithme de décomposition de domaine qui calcule simultanément les solutions directe et adjointe pour une conductivité donnée. En combinant cet algorithme avec la mise à jour du paramètre par descente de gradient, nous obtenons une méthode one-shot de décomposition de domaine qui résout le problème inverse. Nous proposons deux versions discrétisées de l’algorithme couplé, dont la seconde (dans le cas du problème inverse de conductivité linéarisé) s’inscrit dans le cadre abstrait de notre théorie de convergence. Enfin, plusieurs expériences numériques sont fournies pour illustrer les performances des méthodes de type one-shot, en comparaison avec la méthode de descente de gradient classique dans laquelle les problèmes directs et adjoints sont résolus par des solveurs directs. En particulier, nous observons que, même dans le cas de données bruitées, très peu d’itérations internes peuvent toujours garantir une bonne convergence des méthodes de type one-shot
Our 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

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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.

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Arnst, Maarten. "Inversion of probabilistic models of structures using measured transfer functions." Châtenay-Malabry, Ecole centrale de Paris, 2007. http://www.theses.fr/2007ECAP1037.

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L'objectif de la thèse est de développer une méthodologie d’identification expérimentale de modèles probabilistes qui prédisent le comportement dynamique de structures. Nous focalisons en particulier sur l’inversion de modèles probabilistes à paramétrage minimal, introduits par Soize, à partir de fonctions de transfert expérimentales. Nous montrons d’abord que les méthodes classiques d’estimation de la théorie des statistiques mathématiques, telle que la méthode du maximum de vraisemblance, ne sont pas bien adaptées pour aborder ce problème. En particulier, nous montrons que des difficultés numériques, ainsi que des problèmes conceptuels dus au risque d’une mauvaise spécification des modèles, peuvent entraver l’application des méthodes classiques. Ces difficultés nous motivent à formuler l’inversion de modèles probabilistes alternativement comme la minimisation, par rapport aux paramètres recherchés, d’une fonction objectif, mesurant une distance entre les données expérimentales et le modèle probabiliste. Nous proposons deux principes de construction pour la définition de telles distances, basé soit sur la fonction de logvraisemblance, soit l’entropie relative. Nous montrons comment la limitation de ces distances aux lois marginales d’ordre bas permet de surmonter les difficultés mentionnées plus haut. La méthodologie est appliquée à des exemples avec des données simulées et à un problème en ingénierie civile et environnementale avec des mesures réelles
The 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

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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.

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Les variants structuraux (SVs), des variations génomiques de plus de 50 pb, contribuent de manière significative à la diversité génétique et à l'évolution des espèces. La détection et le génotypage précis des SVs est crucial pour comprendre leur rôle dans la variation phénotypique et l'adaptation. Les graphes de variation (VGs) et graphes de pangénomes (PGs), qui représentent les variations génomiques comme des chemins alternatifs dans un graphe, offrent une approche prometteuse pour l'analyse des SVs. Cette thèse explore l'utilisation des VGs et PGs pour la détection et le génotypage des SVs, en se concentrant sur un complexe de quatre espèces de papillons Coenonympha alpins. Deux outils bio-informatiques ont été développés au cours de cette thèse : (1) SVJedi-graph, le premier génotypeur de SVs à partir de lectures longues utilisant un VG pour représenter les SVs, fournissant une précision de génotypage supérieure aux outils de l’état de l’art, en particulier pour les SVs proches et chevauchants, et (2) INVPG-annot, un outil d’identification des inversions dans les PGs, qui a permi de démontrer que les inversions sont représentées par différentes topologies dans les PGs selon l’outil de construction utilisé. L'analyse comparative des génomes des papillons Coenonympha a permis d'identifier douze grandes inversions (≥ 100 kbp) entre les quatre espèces, dont certaines pourraient jouer un rôle dans l'isolement reproductif et l'adaptation locale de deux de ces espèces. Bien que l'approche basée sur les PGs présente des avantages pour la comparaison de génomes, des défis restent à relever pour l'analyse des grands variants comme les inversions
Structural 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

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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.

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L'objectif de la thèse est de développer une méthodologie d'identification expérimentale de modèles probabilistes qui prédisent le comportement dynamique de structures. Nous focalisons en particulier sur l'inversion de modèles probabilistes à paramétrage minimal, introduits par Soize, à partir de fonctions de transfert expérimentales. Nous montrons d'abord que les méthodes classiques d'estimation de la théorie des statistiques mathématiques, telle que la méthode du maximum de vraisemblance, ne sont pas bien adaptées pour aborder ce problème. En particulier, nous montrons que des difficultés numériques, ainsi que des problèmes conceptuels dus au risque d'une mauvaise spécification des modèles, peuvent entraver l'application des méthodes classiques. Ces difficultés nous motivent à formuler l'inversion de modèles probabilistes alternativement comme la minimisation, par rapport aux paramètres recherchés, d'une fonction objectif, mesurant une distance entre les données expérimentales et le modèle probabiliste. Nous proposons deux principes de construction pour la définition de telles distances, basé soit sur la fonction de logvraisemblance, soit l'entropie relative. Nous montrons comment la limitation de ces distances aux lois marginales d'ordre bas permet de surmonter les difficultés mentionnées plus haut. La méthodologie est appliquée à des exemples avec des données simulées et à un problème en ingénierie civile et environnementale avec des mesures réelles.

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Foddis, 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.

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[. . . ]Certaines caractéristiques concernant la qualité et l’hydrogéologie des eaux souterraines, dans de nombreux cas, ne sont pas directement mesurables et doivent être évaluées en fonction d'autres paramètres directement mesurables. Le problème de la détermination des paramètres inconnus du modèle est généralement dénommé "problème inverse". La résolution du problème inverse pour la modélisation de l'écoulement et le transport des contaminants dans les eaux souterraines est l'objectif principal de ce travail de recherche. Quant à la résolution du problème inverse, dans le présent document, nous avons pour objectif la définition d’une méthodologie qui permette l’identification des caractéristiques dans l’espace et le temps des sources inconnues de contaminations. Dans ce travail de recherche, le problème inverse est résolu sur la base de mesures de concentrations du contaminant dans les puits de surveillance situés dans un domaine d'intérêt. Une fois connu l’effet d’un certain phénomène, nous cherchons à reconstruire la cause qui l’a généré. Ainsi, la recherche a-t-elle été élaborée selon les points suivants : - Modélisation de la contamination des eaux souterraines par l'utilisation d'un logiciel non-commercial pour la modélisation des flux et le transport des contaminants dans les milieux poreux. -Modélisation des relations cause-effet de la contamination des eaux souterraines par les Réseaux de Neurones Artificiels (RNA). - Application des RNA pour la résolution du problème inverse dans deux cas de contamination des eaux souterraines étudiés. [. . . ]
[. . . ]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. […]

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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.

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Books on the topic "Inversion identification"

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Identity and Idolatry: The Image of God and Its Inversion. InterVarsity Press, 2015.

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Carson, D. A., and Richard Lints. Identity and Idolatry: The Image of God and Its Inversion. InterVarsity Press, 2015.

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Identity and idolatry: The image of God and its inversion. Downers Grove, IL: InterVarsity Press, 2015.

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McKinlay Gardner, R. J., and David J. Amor. Complex Chromosomal Rearrangements. Edited by R. J. McKinlay Gardner and David J. Amor. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199329007.003.0010.

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Complex chromosome rearrangements (CCRs) include most of the rearrangements that would not be accounted for as “straightforward” classical categories. They may be translocations with three or more segments involved; or they may comprise a mix of translocation and, for example, inversion. Some can be extraordinarily complex. CCRs are classified as types I–IV, most falling into the “least complex” type I category, while types II–IV are grouped as “exceptional CCRs.” Many unbalanced CCRs have arisen de novo and imply no increased reproductive risk. The identification of the clinically normal balanced CCR carrier is less frequent, and for these people, the reproductive risks can be very high.

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Book chapters on the topic "Inversion identification"

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Mirzaei, M., J. W. Bredewout, and 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.

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Fang, Zhang, Zhou Hong, and 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.

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Sindi, Suzanne S., and 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.

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Aykan, Murat, and 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.

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Lombaerts, Thomas, Ping Chu, and 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.

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Lombaerts, Thomas, Ping Chu, Hafid Smaili, Olaf Stroosma, and 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.

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Huang, Li-juan, Bo Jiang, Rong Yang, Kun Kang, Rong-biao Tan, Jin-yu Zhou, Rui-ning Li, Shi-yu Zhou, and 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.

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Zhu, Weizhu, Xi Chu, and 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.

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AbstractStructural health monitoring is carried out for a limited number of measuring points of the bridge. The root of the problem of bridge damage identification is that the mechanical equation inversion result is not unique due to the incomplete measured data. The full-field description ability of digital images to the structural shapes can effectively alleviate the problem of damage identification caused by incomplete measured data. This project aims to research the full-field shape monitoring method of the bridge. Firstly, the mathematical representation of the corresponding image points on the bridge surface is formed by using the image feature extraction method. Analyze the feature points position change mathematical model before and after deformation, and propose the structural full-field displacement vector calculation theory. Verify the full-field displacement calculation theory by a test beam. The results show that the maximum absolute error of the vector length is 0.24 mm and the relative error is less than 5%. The research realizes the structural full-field displacement monitoring under the natural texture condition for the first time. The results can promote the application and development of digital image processing technology in the field of structural health monitoring, improve the level of bridge safety evaluation, and realize the automation, intelligence and visualization of structural deformation monitoring.

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Cacciola, Matteo, Maurizio Campolo, Fabio La Foresta, Francesco Carlo Morabito, and 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.

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Singh, Ankit, Rohtash Kumar, Amritansh Rai, Shatrughan Singh, Raghav Singh, Satya Prakash, and 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.

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Conference papers on the topic "Inversion identification"

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Elkhamry, A., Eduard Bikchandaev, M. Fouda, and 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.

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Abstract Modifying the well position to maximize production requires both detailed reservoir characterization and mapping of sub-seismic features to identify and remain within the optimal zone. Sand channel characterization, including mapping their orientation, thickness and geometry is important to enable optimization of the well path in this complex environment to maximize the net to gross. Combining high Resolution Imaging tools used traditionally for sand channel characterization and fault identification with ultra-deep 3D electromagnetic inversion improves understanding and provides clear visualization of sand channel behavior. Advanced azimuthal ultra-deep resistivity tools with improved signal to noise ratio and processing techniques have enabled better reservoir visualization. Resistivity and geo-signal curves, images, raw components and associated inversions are provided which can be widely used for landing, geo-steering, geo-mapping and lithology identification (Anisotropy) or even petrophysical analysis (Far Field Saturation) at distance. Recent advances provide 3D Inversions, which can be used for sand channel characterization (mapping out the orientation, thickness, and geometry) and fault identification (dip, strike and amplitude) with confidence. In the candidate wells, 3D EM Inversion revealed sand channels distribution in the area for the target reservoir. An innovative workflow for Sand channel characterization and fault identification with ultra-deep resistivity 3D inversion in complex clastics reservoirs has been developed based on the acquired 3D Inversion data and combining multiple sources of data from near wellbore as well as offset logs. In the first field case two sand channels were identified, in the second field case a break with displacement (fault) dislocating the reservoir has been identified and characterized with dip, strike and amplitude values from the 3D Inversion. The 3D Inversion and proposed workflow could be used for optimizing current and future well placement by adjusting well azimuth while drilling, planning sidetracks and field studies incorporating the 3D data into seismic models. This paper demonstrates field cases utilizing ultra-deep resistivity 3D inversion for mapping out the orientation, thickness and geometry of the sand channels and fault identification in complex clastic reservoir to provide increased reservoir understanding.

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Buland, Arild, Martin Landroø, Roger Sollie, Mona Andersen, and 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.

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Weglein, Arthur, Paul B. Viloette, and 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.

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Elkhamry, Ayman, Ahmed Taher, Eduard Bikchandaev, and 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.

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Ultra-deep ElectroMagnetic (EM) inversion is used to resolve multiple layers far from the wellbore. Since it primarily responds to resistivity variations, shale and water bearing sand layers of similar resistivity values can’t be resolved using a traditional resistivity inversion alone. It can be critical to identify the presence of water at distance, so that a standoff from the oil/water contact can be maintained to optimize production. Analysis of the resistivity alone means that a well path may be optimized to maintain a standoff from any low resistivity zone, to avoid drilling close to a potential water contact. Identifying the lithology at distance would remove the uncertainty, allowing a well to drill closer to a shale zone or maintaining distance from a water zone to optimize both well’s position and future production. Inversion for resistivity and anisotropy in three dimensions provides a solution that can improve lithological and fluid analysis in real time, allowing for more sophisticated well placement decisions to be made, by identifying any critical water bearing zones at distance. Real-time transmission of nine ultra-deep resistivity measurement components allows 3D inversion for both resistivity and anisotropy far away from the wellbore. The interpretation of these inversions is examined in clastic reservoirs where it is critical to differentiate between shale and water bearing sand layers at a distance from the well trajectory. Although shale zones could have very low resistivity values similar to that seen in water bearing sand, anisotropy values are generally much higher, due to the laminated nature of the shales. Therefore, resolving both resistivity and anisotropy at a distance allows the shale zone to be distinguished from water filled sand in real time, enabling successful geo-steering and keeping a desired offset above the water level. An example is presented where ultra-deep 3D inversion successfully mapped both resistivity and anisotropy. The lithology has a significant impact on the anisotropy; hence, it was possible to differentiate low resistive shale from the low resistive water bearing sand at a distance from the well trajectory. This information was used to optimize geosteering, geomapping and geostopping processes, and has the potential to be applied to future wells, where applicable. Interpretation results also provide valuable inputs for optimizing completions designs and reservoir management strategies. This paper presents the global first real-time well placement application of 3D resistivity and anisotropy inversions to avoid drilling into a water zone. This novel approach using ultra-deep azimuthal resistivity technology addressed the existing challenge of identifying shale and water filled sand zones of similar resistivities.

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Seidl, Robert, and 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.

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Liu*, Yong, Shangxu Wang, Sanyi Yuan, Nan Tian, and 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.

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Liu, Guoyan, Kun Gao, Xuefeng Liu, and Guoqiang Ni. "RMB identification based on polarization parameters inversion imaging." In Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), edited by Xiangang Luo, Tianchun Ye, Tingwen Xin, Song Hu, Minghui Hong, and Min Gu. SPIE, 2016. http://dx.doi.org/10.1117/12.2241651.

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Barclay, F., B. Bailey, and 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.

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Hongqian Sun, Yujie J. Ding, and 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.

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He, Weikun, Renbiao Wu, and 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.

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Reports on the topic "Inversion identification"

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INVERSION METHOD OF UNCERTAIN PARAMETERS FOR TRUSS STRUCTURES BASED ON GRAPH NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, December 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.5.

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Uncertainty exists widely in practical engineering. It is an important challenge in engineering structural analysis. In truss structures, the uncertainties of axial stiffness of bolted joints will significantly affect the mechanical behavior of the structure as the axial load is dominated by the member internal forces. Structural response analysis based on determined structural parameters is a common forward problem that can be solved by modeling analysis methods. However, the uncertainties parameter of axial stiffness of bolted joint cannot be determined during the design and analysis of truss structure in the direct nonlinear analysis method. Structural parameter identification based on structural response is a typical inverse problem in engineering, which is difficult to solve using traditional analysis tools. In this paper, an inverse model based on Graph Neural Network (GNN) is proposed. The feature encoding method for transforming truss structures into graph representations of GNN is defined. A parameterized acquisition method for large-scale datasets is presented, and an innovative inversion model based on GNN for the inversion of uncertain parameters of truss structures is proposed. The proposed method is shown to perform well with an inversion accuracy, and accurate results can be obtained with limited data sets. The inversion method has strong data mining capability and model interpretability, making it a promising direction for exploring engineering structural analysis.

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