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Dissertations / Theses on the topic 'Neural structures'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Kirikera, Goutham Raghavendra. "A Structural Neural System for Health Monitoring of Structures." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155149869.

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2

Cohen, Michael A. "High-level neural structures constrain visual behavior." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11447.

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Visual cognition is notoriously limited: only a finite amount of information can be fully processed at a given instant. What is the source of these limitations? Here, we suggest that the organization of higher-level visual cortex into content-specific channels constrains information processing across the visual system. Each channel is primarily involved in representing one particular type of visual content (e.g. faces, cars, certain types of shapes, etc.). Furthermore, each channel has a finite processing capacity/bandwidth and is limited in the amount of information it can process. When mult

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3

Coelho, Regina Célia. "Síntese,modelagem e simulação de estruturas neurais morfologicamente realísticas." Universidade de São Paulo, 1998. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-15052009-091429/.

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Os aspectos morfológicos dos neurônios e estruturas neurais, embora potencialmente importantes, têm recebido relativamente pouca atenção na literatura em neurociência. Este trabalho consiste numa substancial parte de um projeto em desenvolvimento no Grupo de Pesquisa em Visão Cibernética voltado para o estudo da relação formal/função neural. Mais especificamente, o presente trabalho dedica particular atenção para a síntese, modelagem e simulação de estruturas neurais morfologicamente realísticas. A tese se inicia com revisões bibliográficas sobre visão biológica e neurociência, direcionadas ao

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4

Neville, Richard. "Augmentation of sigma-pi structures and learning regimes." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359920.

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5

Elhewy, Ahmed. "Probabilistic analysis of composite structures using artificial neural network." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413045.

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6

Oyallon, Edouard. "Analyzing and introducing structures in deep convolutional neural networks." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE060.

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Cette thèse étudie des propriétés empiriques des réseaux de neurones convolutifs profonds, et en particulier de la transformée en Scattering. En effet, l’analyse théorique de ces derniers est difficile et représente jusqu’à ce jour un défi : les couches successives de neurones ont la capacité de réaliser des opérations complexes, dont la nature est encore inconnue, via des algorithmes d’apprentissages dont les garanties de convergences ne sont pas bien comprises. Pourtant, ces réseaux de neurones sont de formidables outils pour s’attaquer à une grande variété de tâches difficiles telles la cla

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7

Search, David John. "Inspection of periodic structures using coherent optics." Thesis, Liverpool John Moores University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242378.

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8

Cheng, Yi-Hsun Ethan. "Memory-based learning structure : learning covergence [sic], network structures and training techniques /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974613.

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9

Gil, Ferrer Alejandro. "A neural network performance analysis with three different model structures." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302144.

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This report analyzes three neural network structures: dense, convolutional and recurrent. One data set example and problem has been chosen for each type of structure: a multi-class classification problem, an image classifier and a time sequence prediction, respectively. This report also aims at understanding which structure performs better for different problem statements, and how the different parameters they depend on affect their performance. The most common parameters that have been analyzed are the following: the number of intermediate layers, the number of neurons, the number of epochs,

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10

Large, Imogen. "Investigating neural structures and behavioural biases in perceptual decision-making." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:1a107371-424b-4dfd-96e4-d851d801bec8.

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Why do we behave as we do? The general mechanisms underlying visual perception and decision-making within a social context have been under scientific scrutiny for over half a century (Sherif, 1945; Asch, 1951; Berns et al, 2005). In spite of this, neither a definitive mechanism for how perceptual biases arise, nor a robust neural basis have emerged. In the first part of my thesis, I use a combination of visual behavioural testing and computational modelling to investigate the development of perceptual biases under social advice in children, and explore their potential mechanisms. In the second

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11

Li, Zhe. "Optimizing neural network structures: faster speed, smaller size, less tuning." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6460.

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Deep neural networks have achieved tremendous success in many domains (e.g., computer vision~\cite{Alexnet12,vggnet15,fastrcnn15}, speech recognition~\cite{hinton2012deep,dahl2012context}, natural language processing~\cite{dahl2012context,collobert2011natural}, games~\cite{silver2017mastering,silver2016mastering}), however, there are still many challenges in deep learning comunity such as how to speed up training large deep neural networks, how to compress large nerual networks for mobile/embed device without performance loss, how to automatically design the optimal network structures for a ce

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12

Billingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.

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Deep machine learning has enjoyed recent success in vision and speech-to-text tasks, using deep multi-layered neural networks. They have obtained remarkable results particularly where the internal representation of the task is unclear. In parsing, where the structure of syntax is well studied and understood from linguistics, neural networks have so far not performed so well. State-of-the-art parsers use a tree-based graphical model that requires a large number of equivalent classes to represent each parse node and its phrase label. A recursive neural network (RNN) parser has been developed th

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13

Sunny, Mohammed Rabius. "Towards Structural Health Monitoring of Gossamer Structures Using Conductive Polymer Nanocomposite Sensors." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28797.

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The aim of this research is to calibrate conductive polymer nanocomposite materials for large strain sensing and develop a structural health monitoring algorithm for gossamer structures by using nanocomposites as strain sensors. Any health monitoring system works on the principle of sensing the response (strain, acceleration etc.) of the structure to an external excitation and analyzing the response to find out the location and the extent of the damage in the structure. A sensor network, a mathematical model of the structure, and a damage detection algorithm are necessary components of a struc

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14

Smithies, Nicola Jane. "Advancing the analysis of architectural fabric structures, neural networks and uncertainty." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/3056.

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In current practice a plane stress framework comprising elastic moduli and Poisson’s ratios is most commonly used to represent the mechanical properties of architectural fabrics. This is often done to enable structural analysis utilising commercially available, non-specialist, finite element packages. Plane stress material models endeavour to fit a flat plane to the highly non-linear stress strain response surface of architectural fabric. Neural networks have been identified as a possible alternative to plane stress material models. Through a process of training they are capable of capturing t

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15

Gruhl, Alexander [Verfasser]. "Phylogenetic significance of mesodermal and neural structures in Bryozoa / Alexander Gruhl." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1023095785/34.

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16

Park, Hyo Seon. "Neural dynamics models for integrated optimum design of large steel structures." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1239621706.

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17

Li, Chien-Kuo. "New neural network structures for problems with high-dimensional input space /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841317.

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18

Mitchell, Ryan. "A WANFIS Model for Use in System Identification and Structural Control of Civil Engineering Structures." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1165.

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With the increased deterioration of infrastructure in this country, it has become important to find ways to maintain the strength and integrity of a structure over its design life. Being able to control the amount a structure displaces or vibrates during a seismic event, as well as being able to model this nonlinear behavior, provides a new challenge for structural engineers. This research proposes a wavelet-based adaptive neuro- fuzzy inference system for use in system identification and structural control of civil engineering structures. This algorithm combines aspects of fuzzy logic theory,

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19

Zubaydi, Achmad. "Use of neural networks for the identification of damage in ship structures." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ62462.pdf.

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20

Hassanein, N. M. "The application of neural networks to service life prediction of concrete structures." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263091.

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21

Glasmacher, B., AL Halabi F, O. Gryshkov, A. Avakian, and О. Г. Аврунін. "Cross-linked alginate structures for engineering of scaffolds for neural tissue engineering." Thesis, Prague, Czech Republic, 2018. http://openarchive.nure.ua/handle/document/8334.

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22

Fieldsend, Jonathan E. "Novel algorithms for multi-objective search and their application in multi-objective evolutionary neural network training." Thesis, University of Exeter, 2003. http://hdl.handle.net/10871/11706.

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23

Hessel, Günther, Frank-Peter Weiß, and Wilfried Schmitt. "Acoustic Leak Detection at Complicated Geometrical Structures Using Fuzzy Logic and Neural Networks." Forschungszentrum Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-32699.

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Methods of acoustic leak monitoring are of great practical interest for the safety of pressure vessels and pipe lines not only at the primary circuit of nuclear power plants. In this report some aspects of acoustic leak localization at complicated three-dimensional topologies for the case of leakage monitoring at the reactor vessel head of a VVER-440 are discussed. An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak.

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24

Ramadi, Khalil B. (Khalil Basil). "A chronically implantable neural device for on-demand microdosing of deep brain structures." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107067.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 54-59).<br>Chronic neuropsychiatric diseases are increasingly consuming a larger portion of healthcare costs, in part due to a lack of effective treatment techniques. Through research into the pathology of these diseases we now know that most of these disorders are due to a loss in synchrony in a specific neural network. Effective treatments must seek to attenuate these network dynamics to establish normal neural

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25

Hessel, Günther, Frank-Peter Weiß, and Wilfried Schmitt. "Acoustic Leak Detection at Complicated Geometrical Structures Using Fuzzy Logic and Neural Networks." Forschungszentrum Rossendorf, 1993. https://hzdr.qucosa.de/id/qucosa%3A22096.

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Methods of acoustic leak monitoring are of great practical interest for the safety of pressure vessels and pipe lines not only at the primary circuit of nuclear power plants. In this report some aspects of acoustic leak localization at complicated three-dimensional topologies for the case of leakage monitoring at the reactor vessel head of a VVER-440 are discussed. An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak.

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26

Gerard, Sarah E. "Multi-scale convolutional neural networks for segmentation of pulmonary structures in computed tomography." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6578.

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Computed tomography (CT) is routinely used for diagnosing lung disease and developing treatment plans using images of intricate lung structure with submillimeter resolution. Automated segmentation of anatomical structures in such images is important to enable efficient processing in clinical and research settings. Convolution neural networks (ConvNets) are largely successful at performing image segmentation with the ability to learn discriminative abstract features that yield generalizable predictions. However, constraints in hardware memory do not allow deep networks to be trained with high-r

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27

Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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28

Luo, Huageng. "Damage detection and health monitoring of structures using dynamic response and neural network techniques." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/12929.

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29

Aldwaik, Mais M. "Cost Optimization of Reinforced Concrete Highrise Building Structures." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574738135695095.

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30

Paré, Martin. "The neural control of fixation and saccadic gaze displacements by midbrain and brainstem structures." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28880.

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Exploration of the visual world requires almost incessant movements of the visual axis (gaze), which are separated by periods of fixation. This thesis describes the neural control of fixation and saccadic gaze displacements. The superior colliculus (SC) is a mid-brain structure involved in gaze redirection executed either by the eyes alone or by combined eye and head movements. Microstimulation of the cat SC revealed a map of evoked gaze shifts, which was coextensive with the motor map encoded by the neuronal activity. The spatio-temporal characteristics of these evoked gaze shifts depended, t

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31

SÌ§ahin, Melin. "Damage identification in beam-like structures by vibration-based analysis and artificial neural networks." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405529.

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32

Perez, Garnes Manuel. "Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/48799.

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Se pretende obtener un material semibiodegradable basado en ácido hialurónico químicamente enlazado a cadenas de polímeros acrílicos. Los hidrogeles de ácido hialurónico presentan en general buenas características para su utilización en regeneración del sistema nervioso central: es biodegradable, es un componente importante del tejido neural, sus propiedades mecánicas son semejantes a las del tejido cerebral, promueve la formación de nuevos capilares (angiogénesis), y limita la inflamación. Con este nuevo material se pretende mejorar el excesivo grado de hinchado en medio fisiológico, su rápid

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33

Schwartz, Samuel David. "Machine Learning Techniques as Applied to Discrete and Combinatorial Structures." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7542.

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Machine Learning Techniques have been used on a wide array of input types: images, sound waves, text, and so forth. In articulating these input types to the almighty machine, there have been all sorts of amazing problems that have been solved for many practical purposes. Nevertheless, there are some input types which don’t lend themselves nicely to the standard set of machine learning tools we have. Moreover, there are some provably difficult problems which are abysmally hard to solve within a reasonable time frame. This thesis addresses several of these difficult problems. It frames these pro

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34

Long, Luohui. "Design-Oriented Translators for Automotive Joints." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/29316.

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A hierarchical approach is typically followed in design of consumer products. First, a manufacturer sets performance targets for the whole system according to customer surveys and benchmarking of competitors' products. Then, designers cascade these targets to the subsystems or the components using a very simplified model of the overall system. Then, they try to design the components so that they meet these targets. It is important to have efficient tools that check if a set of performance targets for a component corresponds to a feasible design and determine the dimensions and mass of this

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35

Sirca, Gene F. Jr. "ANALYSIS OF FULL-SCALE IN-SERVICE CIVILENGINEERING STRUCTURES." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1556988737709233.

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36

Siganos, Ioannis. "Pulse mode light sensing using four-layer semiconductor structures and their application in neural networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Dec/08Dec%5FSiganos.pdf.

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Thesis (M.S. in Applied Physics)--Naval Postgraduate School, December 2008.<br>Thesis Advisor(s): Karunasiri, Gamani ; Tummala, Murali. "December 2008." Description based on title screen as viewed on January 29, 2009. Includes bibliographical references (p. 69-71). Also available in print.

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37

Cha, Dea Ho. "Prediction of Wave-Induced Seabed Maximum Liquefaction Depth Using Artificial Neural Network Model." Thesis, Griffith University, 2010. http://hdl.handle.net/10072/367451.

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In the last few decades, considerable effort has been devoted to the phenomenon of wave-induced liquefaction. In deed, it is one of the most important factors used in analysing the seabed stability and in designing marine structures. As waves propagate and fluctuate over the ocean surface, energy is carried within the medium of the water particles. When this energy is transmitted into the seabed, the results are a rather complex mechanism of soil behaviours that significantly affect the stability of the seabed. The prediction of wave-induced seabed liquefaction has been recognised by coastal g

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38

Todd, Charles Peter Debenham. "Non-destructive evaluation of adhesively bonded structures using ultrasonic Lamb waves and artificial neural networks." Thesis, Keele University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361828.

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39

Bandara, Arachchillage Rupika Priyadarshani. "Damage identification and condition assessment of building structures using frequency response functions and neural networks." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61196/4/Rupika_Bandara_Thesis.pdf.

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This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

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40

Nam, Jinseok [Verfasser], Johannes [Akademischer Betreuer] Fürnkranz, and Krzysztof [Akademischer Betreuer] Dembczyński. "Learning Label Structures with Neural Networks for Multi-label Classification / Jinseok Nam ; Johannes Fürnkranz, Krzysztof Dembczyński." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/118841030X/34.

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41

Sablayrolles, Alexandre. "Mémorisation et apprentissage de structures d'indexation avec les réseaux de neurones." Thesis, Université Grenoble Alpes, 2020. https://thares.univ-grenoble-alpes.fr/2020GRALM044.pdf.

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Les systèmes d’apprentissage machine, et en particulier les systèmes dits d’apprentissage profond, nécessitent de grandes quantités de données pour l’entraînement. En vision par ordinateur, les réseaux de neurones convolutionnels utilisés pour la classification d’images, la reconnaissance de scènes et la détection d’objets notamment sont entrainés sur des jeux de données dont la taille se situe entre quelques dizaines de milliers et quelques milliards d’exemples. Les modèles paramétriques ont une très large capacité, souvent du même ordre de grandeur que le nombre d’exemples. Dans cette thèse,

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42

Kvale, David Thomas. "Artificial Neural Network-Based Approaches for Modeling the Radiated Emissions from Printed Circuit Board Structures and Shields." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1280698960.

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43

Park, Gyuhae. "Assessing Structural Integrity using Mechatronic Impedance Transducers with Applications in Extreme Environments." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27719.

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This research reviews and extends the impedance-based structural health monitoring technique in order to detect and identify structural damage on various complex structures. The basic principle behind this technique is to apply high frequency structural excitations (typically higher than 30 kHz) through the surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. Several case studies, includi

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44

Mohammadzadeh, Soroush. "System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/868.

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"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy in

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Ding, Simon. "Advancing cosmological field-level inference with physics-informed Bayesian neural networks." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS050.

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La cosmologie repose essentiellement sur des observations passives, obtenues avec des télescopes sophistiqués, pour comprendre l'origine, la dynamique et le destin ultime de l'Univers. Les nouveaux relevés de galaxies propulsent la cosmologie dans une ère dominée par les données, nécessitant des adaptations significatives de nos méthodes d'analyse. L'un des principaux défis de la cosmologie moderne est d'extraire des informations physiques significatives des prochains relevés cosmologiques. Parallèlement à cette révolution, l'apprentissage automatique s'impose comme une technique puissante pou

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46

Kallassy, Assad. "Intégration des réseaux de neurones dans la conception et l'optimisation des structures mécaniques." Université Joseph Fourier (Grenoble), 1998. http://www.theses.fr/1998GRE10166.

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L'optimisation des structures et systemes mecaniques utilise essentiellement des methodes d'optimisation se basant sur un calcul de sensibilite. Ces methodes requierent une mise en uvre delicate et risquent de tomber sur des optima locaux. D'autres methodes a strategie de recherche aleatoire, comme les algorithmes genetiques, sont simples a manipuler mais demandent un nombre prohibitif de calculs de la fonction objectif. Ces inconvenients s'accentuent lorsqu'on applique ces methodes conjointement a une analyse mecanique par elements finis, tres couteuse en temps de calcul. Notre strategie de r

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47

Uthoff, Johanna Mariah. "Cancer risk assessment using quantitative imaging features from solid tumors and surrounding structures." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6869.

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Medical imaging is a powerful tool for clinical practice allowing in-vivo insight into a patient’s disease state. Many modalities exist, allowing for the collection of diverse information about the underlying tissue structure and/or function. Traditionally, medical professionals use visual assessment of scans to search for disease, assess relevant disease predictors and propose clinical intervention steps. However, the imaging data contain potentially useful information beyond visual assessment by trained professional. To better use the full depth of information contained in the image sets, qu

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48

Щолок, Т., Н. Бурлачко, А. Скороплет та А. Винниченко. "Сосудисто-нервные структуры органов верхнего этажа брюшной полости". Thesis, Сумский государственный университет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/45102.

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Данные о нервных структурах, которые находятся в паравазальной соединительнотканной клетчатке в адвентициальном слое стенки кровеносных сосудов, весьма важны для разработки новых оперативных методик, а также в практике рефлексотерапии.

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Krasilenko, V. G., A. A. Lazarev, D. V. Nikitovich, В. Г. Красиленко, О. О. Лазарєв, and Д. В. Нікітович. "Design and simulation of cells, that realize arbitrary functions of activations of neurons in self-learning equivalent-convolutional neural structures." Thesis, Інститут проблем штучного інтелекту, 2018. http://ir.lib.vntu.edu.ua//handle/123456789/23589.

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We consider the urgent need to create hardware accelerators for CNN. We show a overview of the equivalent models (EMs), EM-paradigms for recognition images and learning with CL- operations as: "equivalence". We consider approaches to the design of arrays of neuron-equivalentors (NEs) with different activation functions. Approach is based on the use of mixed methods, building NEs (with number of synapsis 128) and their cells based current mirrors. Simulations show that the efficiency of NEs relative to the energy is estimated at a value of not less 10^12 an. op. / sec on W.

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Dorn, Márcio. "MOIRAE : a computational strategy to predict 3-D structures of polypeptides." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/142870.

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Abstract:

Currently, one of the main research problems in Structural Bioinformatics is associated to the study and prediction of the 3-D structure of proteins. The 1990’s GENOME projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures have not followed the same growth trend. The number of protein sequences is much higher than the number of known 3-D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challeng

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