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Letteratura scientifica selezionata sul tema "Computational analysis"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Computational analysis"

1

Liu, G. R. "Computational methods for certified solutions, adaptive analysis, real-time computation, and inverse analysis of mechanics problem." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _—1_—_—5_. http://dx.doi.org/10.1299/jsmecmd.2011.24._-1_.

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ILIE, Marcel, Augustin Semenescu, Gabriela Liliana STROE, and Sorin BERBENTE. "NUMERICAL COMPUTATIONS OF THE CAVITY FLOWS USING THE POTENTIAL FLOW THEORY." ANNALS OF THE ACADEMY OF ROMANIAN SCIENTISTS Series on ENGINEERING SCIENCES 13, no. 2 (2021): 78–86. http://dx.doi.org/10.56082/annalsarscieng.2021.2.78.

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Computational fluid dynamics of turbulent flows requires large computational resources or are not suitable for the computations of transient flows. Therefore methods such as Reynolds-averaged Navier-Stokes equations are not suitable for the computation of transient flows. The direct numerical simulation provides the most accurate solution, but it is not suitable for high-Reynolds number flows. Large-eddy simulation (LES) approach is computationally less demanding than the DNS but still computationally expensive. Therefore, alternative computational methods must be sought. This research concerns the modelling of inviscid incompressible cavity flow using the potential flow. The numerical methods employed the finite differences approach. The time and space discretization is achieved using second-order schemes. The studies reveal that the finite differences approach is a computationally efficient approach and large computations can be performed on a single computer. The analysis of the flow physics reveals the presence of the recirculation region inside the cavity as well at the corners of the cavity

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Juneja, Shall, Deepayan Mukherjee, and Sachi Garg. "Computational Analysis of RNA Nucleotide Sequences." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 369–72. http://dx.doi.org/10.31142/ijtsrd21342.

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Fajdiga, Gorazd. "Computational fatigue analysis of contacting mechanical elements." Tehnicki vjesnik - Technical Gazette 22, no. 1 (2015): 169–75. http://dx.doi.org/10.17559/tv-20140429122305.

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Protsko, I. O., and D. V. Ostrovka. "ANALYSIS OF THE ERROR OF COMPUTATION FAST TRANSFORMS OF FOURIER CLASS BASED ON CYCLIC CONVOLUTIONS." Ukrainian Journal of Information Technology 2, no. 1 (2020): 52–56. http://dx.doi.org/10.23939/ujit2020.02.052.

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The features of the computational model of discrete transforms of Fourier class based on cyclic convolutions to determine the algorithmic calculation error are analyzed. Based on the approach of efficient computation of discrete transforms of Fourier class of arbitrary size N, using of a hashing array to transform a discrete basis matrix into a set of block-cyclic submatrices, the components of computational costs are considered. These components of computational costs depend on the type of transform, the size and the block-cycle structure of the transformation core. Examples of computational model and block-cyclic structure of matrices of simplified arguments of basis functions for mutually inverse discrete cosine transforms of types II, III are given. The computational model characterizes the accumulation of rounding errors at the stages of adding input data, computing cyclic convolutions, combining the results of convolutions. Discrete cyclic convolutions can be implemented using fast algorithms or a type of system that corresponds to digital filters with finite pulse characteristics. The possibility of parallel computation of the reduced number of cyclic convolutions makes the analysis of errors insensitive to rearrangement of their computations. The multiplication operations performed when computing the cyclic convolution uses a smaller number of basis coefficients equal to N/4 or N/2 depending on the size of transform. The formats of representation of real numbers in computer systems are considered, which also determine the magnitude of the computational error of transforms. The results of direct and fast computation of discrete cosine transform of type II based on cyclic convolutions with size N=58 in the format wit floating point of double precision and computation error between them are presented. The apriori process of studying the transform errors of the corresponding type and size by the method of mathematical modeling and computational experiment is approximate, which allows to predict the statistical averages of the accuracy of computing the discrete Fourier transform of arbitrary size based on cyclic convolutions.

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LESSNER, Daniel. "ANALYSIS OF TERM MEANING "COMPUTATIONAL THINKING"." Journal of Technology and Information 6, no. 1 (April 1, 2014): 71–88. http://dx.doi.org/10.5507/jtie.2014.006.

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Planitz, Max, and R. E. Moore. "Computational Functional Analysis." Mathematical Gazette 70, no. 451 (March 1986): 69. http://dx.doi.org/10.2307/3615858.

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Fink, James P., and R. E. Moore. "Computational Functional Analysis." Mathematics of Computation 47, no. 175 (July 1986): 372. http://dx.doi.org/10.2307/2008105.

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Giraud, Mathieu, Richard Groult, Emmanuel Leguy, and Florence Levé. "Computational Fugue Analysis." Computer Music Journal 39, no. 2 (June 2015): 77–96. http://dx.doi.org/10.1162/comj_a_00300.

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One of the pinnacles of form in classical Western music, the fugue is often used in the teaching of music analysis and composition. Fugues alternate between instances of a subject and other patterns and modulatory sections, called episodes. Musicological analyses are generally built on these patterns and sections. We have developed several algorithms to perform an automated analysis of a fugue, starting from a score in which all the voices are separated. By focusing on the diatonic similarities between pitch intervals, we detect subjects and countersubjects, as well as partial harmonic sequences inside the episodes. We also implemented tools to detect subject scale degrees, cadences, and pedals, as well as a method for segmenting the fugue into exposition and episodic parts. Our algorithms were tested on a corpus of 36 fugues by J. S. Bach and Dmitri Shostakovich. We provide formalized ground-truth data on this corpus as well as a dynamic visualization of the ground truth and of our computed results. The complete system showed acceptable or good results for about one half of the fugues tested, enabling us to depict their design.

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Bhardwaj, Shalini, and Yashwant Buke. "Computational Fluid Dynamics Analysis of A Turbocharger System." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 161–64. http://dx.doi.org/10.15373/22778179/may2014/49.

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Tesi sul tema "Computational analysis"

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Pocock, Matthew Richard. "Computational analysis of genomes." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615724.

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Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.

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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.

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Shenoy, A. "Computational analysis of facial expressions." Thesis, University of Hertfordshire, 2010. http://hdl.handle.net/2299/4359.

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This PhD work constitutes a series of inter-disciplinary studies that use biologically plausible computational techniques and experiments with human subjects in analyzing facial expressions. The performance of the computational models and human subjects in terms of accuracy and response time are analyzed. The computational models process images in three stages. This includes: Preprocessing, dimensionality reduction and Classification. The pre-processing of face expression images includes feature extraction and dimensionality reduction. Gabor filters are used for feature extraction as they are closest biologically plausible computational method. Various dimensionality reduction methods: Principal Component Analysis (PCA), Curvilinear Component Analysis (CCA) and Fisher Linear Discriminant (FLD) are used followed by the classification by Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). Six basic prototypical facial expressions that are universally accepted are used for the analysis. They are: angry, happy, fear, sad, surprise and disgust. The performance of the computational models in classifying each expression category is compared with that of the human subjects. The Effect size and Encoding face enable the discrimination of the areas of the face specific for a particular expression. The Effect size in particular emphasizes the areas of the face that are involved during the production of an expression. This concept of using Effect size on faces has not been reported previously in the literature and has shown very interesting results. The detailed PCA analysis showed the significant PCA components specific for each of the six basic prototypical expressions. An important observation from this analysis was that with Gabor filtering followed by non linear CCA for dimensionality reduction, the dataset vector size may be reduced to a very small number, in most cases it was just 5 components. The hypothesis that the average response time (RT) for the human subjects in classifying the different expressions is analogous to the distance measure of the data points from the classification hyper-plane was verified. This means the harder a facial expression is to classify by human subjects, the closer to the classifying hyper-plane of the classifier it is. A bi-variate correlation analysis of the distance measure and the average RT suggested a significant anti-correlation. The signal detection theory (SDT) or the d-prime determined how well the model or the human subjects were in making the classification of an expressive face from a neutral one. On comparison, human subjects are better in classifying surprise, disgust, fear, and sad expressions. The RAW computational model is better able to distinguish angry and happy expressions. To summarize, there seems to some similarities between the computational models and human subjects in the classification process.

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Etherington, Graham John. "Computational analysis of foodborne viruses." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423473.

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Wen, Wen. "Computational texture analysis and segmentation." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.

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Buchala, Samarasena. "Computational analysis of face images." Thesis, University of Hertfordshire, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431938.

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Hussain, R. "Computational geometry using fourier analysis." Thesis, De Montfort University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391483.

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Ikoma, Hayato. "Computational microscopy for sample analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91427.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.<br>46<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 41-44).<br>Computational microscopy is an emerging technology which extends the capabilities of optical microscopy with the help of computation. One of the notable example is super resolution fluorescence microscopy which achieves sub-wavelength resolution. This thesis explores the novel application of computational imaging methods to fluorescence microscopy and oblique illumination microscopy. In fluorescence spectroscopy, we have developed a novel nonlinear matrix unmixing algorithm to separate fluorescence spectra distorted by absorption effect. By extending the method to tensor form, we have also demonstrated the performance of a nonlinear fluorescence tensor unmixing algorithm on spectral fluorescence imaging. In the future, this algorithm may be applied to fluorescence unmixing in deep tissue imaging. The performance of the two algorithms were examined on simulation and experiments. In another project, we applied switchable multiple oblique illuminations to reflected-light microscopy. While the proposed system is easily implemented compared to existing methods, we demonstrate that the microscope detects the direction of surface roughness whose height is as small as illumination wavelength.<br>by Hayato Ikoma.<br>S.M.

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Xu, Yangjian. "Computational analysis of fretting fatigue." Düsseldorf VDI-Verl, 2009. http://d-nb.info/996624554/04.

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Li, Xiang. "Computational analysis of ultraviolet reactors /." Online version of thesis, 2009. http://hdl.handle.net/1850/11175.

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Libri sul tema "Computational analysis"

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Anastassiou, George A., and Oktay Duman, eds. Computational Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28443-9.

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Laub, Alan J. Computational matrix analysis. Philadelphia: Society for Industrial and Applied Mathematics, 2012.

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Neuman, Yair. Computational Personality Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42460-6.

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Im, Chang-Hwan, ed. Computational EEG Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0908-3.

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Meredith, David, ed. Computational Music Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25931-4.

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Laube, Patrick. Computational Movement Analysis. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10268-9.

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Abraham, Ajith, Aboul-Ella Hassanien, and Vaclav Sná¿el, eds. Computational Social Network Analysis. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-229-0.

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Anastassiou, George A. Intelligent Mathematics: Computational Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17098-0.

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Zhang, David, Wangmeng Zuo, and Peng Wang. Computational Pulse Signal Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4044-3.

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F, Rosenthal David, and Okuno Hiroshi G, eds. Computational auditory scene analysis. Mahwah, N.J: Lawrence Erlbaum Associates, 1998.

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Capitoli di libri sul tema "Computational analysis"

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Betounes, David. "Computational Analysis." In Partial Differential Equations for Computational Science, 87–108. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-2198-2_5.

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Weihrauch, Klaus. "7. Computational Complexity." In Computable Analysis, 195–235. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-56999-9_7.

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Hart, George W. "Multidimensional Computational Methods." In Multidimensional Analysis, 171–208. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4208-6_7.

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Dascalu, Mihai. "Computational Discourse Analysis." In Analyzing Discourse and Text Complexity for Learning and Collaborating, 53–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03419-5_4.

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Wang, DeLiang. "Computational Scene Analysis." In Challenges for Computational Intelligence, 163–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71984-7_8.

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Hausser, Roland. "Computational language analysis." In Foundations of Computational Linguistics, 13–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04337-0_2.

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Gudmundsson, Joachim, Patrick Laube, and Thomas Wolle. "Computational Movement Analysis." In Springer Handbook of Geographic Information, 423–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72680-7_22.

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Hausser, Roland. "Computational language analysis." In Foundations of Computational Linguistics, 13–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03920-5_2.

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Aigner, Wolfgang, Silvia Miksch, Heidrun Schumann, and Christian Tominski. "Computational Analysis Support." In Human–Computer Interaction Series, 169–92. London: Springer London, 2023. http://dx.doi.org/10.1007/978-1-4471-7527-8_6.

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AbstractThis chapter is concerned with computational methods to support the analysis of time-oriented data. A general overview of temporal data analysis is provided and specific application examples will be used for demonstration.

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Elwert, Frederik. "Computational Text Analysis." In The Routledge Handbook of Research Methods in the Study of Religion, 164–79. 2nd ed. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003222491-12.

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Atti di convegni sul tema "Computational analysis"

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Li, Shaoheng, and Peter Kner. "Self-interference digital holography with computational adaptive optics." In Adaptive Optics: Methods, Analysis and Applications, OTh4F.3. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/aopt.2024.oth4f.3.

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Self-interference Digital Holography (SIDH) based single molecule localization microscopy has the potential to perform large volume 3D super-resolution imaging without mechanical refocusing of the sample. This work presents a fast, guide-star-free computational Adaptive Optics method for SIDH.

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Yang, Changhuei. "Computational Microscopy for Pathology Analysis." In Frontiers in Optics, FM1B.1. Washington, D.C.: Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.fm1b.1.

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In this talk, I will discuss some of our recent computational microscopy and deep learning work, that showcase some of these shifts in the context of pathology. I will talk about APIC – an improved computational microscopy method to collect and process image data, which brings significant workflow advantages to pathology. I will also talking about the use of Deep Learning in image analysis, and point out some of the surprising and impactful ways Deep Learning can improve pathology. Full-text article not available; see video presentation

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Yeo, Hyeonsoo, and Mark Potsdam. "Rotor Structural Loads Analysis Using Coupled Computational FluidDynamics/Computational Structural Dynamics." In Vertical Flight Society 70th Annual Forum & Technology Display, 1–26. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9548.

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Coupled CFD/CSD (RCAS/Helios and CAMRAD II/Helios) analyses are performed and the calculated rotor structural loads are compared with the flight test data obtained from the NASA/Army UH-60A Airloads Program. Three challenging level flight conditions are investigated: 1) high speed with advancing blade negative lift, 2) low speed with blade-wake interaction, and 3) high thrust with dynamic stall. The predicted flap bending and torsion moments, pitch link and lag damper loads, in general, show reasonably good correlation with the test data. A nonlinear lag damper model is essential for the accurate prediction of root chord bending moment and lag damper load. Both analyses, however, significantly underpredict the chord bending moments, especially the 4/rev harmonic amplitude. Parametric study shows that blade stiffness variations have only a small influence on the loads calculations. However, modal damping in the first flap mode has a significant influence on the flap bending moments. Inclusion of a simple one degree-of-freedom drivetrain model shows the potential importance of high frequency drivetrain modes for the accurate prediction of the 4/rev chord bending moments and a need to develop a realistic drivetrain model.

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Nayar, Shree. "Advances in Computational Imaging." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt1a.2.

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Majak, Jüri, and M. Di Sciuva. "Preface: Computational Mechanics." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026521.

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Bechhoefer, Eric. "Low Computational, Nonlinear Component Trend Analysis." In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14606.

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This paper is concerned with a nonlinear, computationally efficient method for trending component health. While conceptually simple, the goal of component trending is to reduce spurious noise in the measured component health and to estimate the remaining useful life (RUL). The need for lower computational effort allows this to be done on an embedded system. This would be important for display on a cockpit multi-function display. Additionally, we describe a new method for state smoothing. This is a forward-backward technique with no computational overhead associated with updating the plant noise (associated with a Kalman Smoother), significantly reducing the number of operations needed. Finally, a comparison is made between the precision of the nonlinear state estimation vs. a linear state estimation in the computation of the RUL. The precision was quantified using mean relative of the prognostic.

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Chamis, C. C., and R. H. Johns. "Computational Engine Structural Analysis." In ASME 1986 International Gas Turbine Conference and Exhibit. American Society of Mechanical Engineers, 1986. http://dx.doi.org/10.1115/86-gt-70.

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A significant research activity at the NASA Lewis Research Center is the computational simulation of complex multidisciplinary engine structural problems. This simulation is performed using computational engine structural analysis (CESA) which consists of integrated multidisciplinary computer codes in conjunction with computer post-processing for “problem-specific” application. A variety of the computational simulations of specific cases are described in some detail in this paper. These case studies include (1) aeroelastic behavior of bladed rotors, (2) high velocity impact of fan blades, (3) blade-loss transient response, (4) rotor/stator/squeeze-film/bearing interaction, (5) blade-fragment/rotor-burst containment, and (6) structural behavior of advanced swept turboprops. These representative case studies were selected to demonstrate the breadth of the problems analyzed and the role of the computer including post-processing and graphical display of voluminous output data.

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Gupta, M. Satyanarayana, Nirmith Kumar Mishra, Mosin, Aishwarya Jaiswal, and Ankadala Jyoshnavi. "Computational analysis over wings." In PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0113273.

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Degtyarev, Alexander, Vasily Khramushin, and Julia Shichkina. "Tensor methodology and computational geometry in direct computational experiments in fluid mechanics." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992291.

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Venkatesh, Suresh, Naren Viswanathan, and David Schurig. "W-Band Sparse Synthetic Aperture for Computational Imaging." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt5a.17.

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Rapporti di organizzazioni sul tema "Computational analysis"

1

George, D. L. Computational techniques in gamma-ray skyshine analysis. Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/6077591.

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Gentile, Ann C., Youssef M. Marzouk, James M. Brandt, and Philippe Pierre Pebay. Meaningful statistical analysis of large computational clusters. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/958384.

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Lax, P., and M. Berger. Applied analysis/computational mathematics. Final report 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10113926.

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Babuska, Ivo, Y. Li, and K. L. Jerina. Reliability of Computational Analysis of Plasticity Problems. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada239646.

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5

Davis, George B., and Kathleen M. Carley. Computational Analysis of Merchant Marine GPS Data. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada471469.

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6

Plotts, Dylan. Computational Notebooks: Designing for Exploratory Data Analysis. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-409.

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Barhen, J., C. W. Glover, and V. A. Protopopescu. Advanced computational tools for 3-D seismic analysis. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/450786.

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8

Grandhi, Ramana V. Computational Mechanics Approach for Multidisciplinary Nonlinear Sensitivity Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada416568.

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9

Rzhetsky, Andrey, and Dimitris Anastassiou. COMPUTATIONAL ANALYSIS AND SIMULATION OF BACTERIAL MOLECULAR NETWORKS. Office of Scientific and Technical Information (OSTI), December 2009. http://dx.doi.org/10.2172/968434.

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Konstantin Mischaikow, Michael Schatz, William Kalies, and Thomas Wanner. Multiscale analysis of nonlinear systems using computational homology. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/979569.

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