Готові списки джерел за темами / Adaptive modelling

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Добірка наукової літератури з теми "Adaptive modelling"

Автор: Grafiati
Опубліковано: 29 березня 2025

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Статті в журналах з теми "Adaptive modelling"

1

Qumer Gill, Asif, and Muhammad Atif Qureshi. "Adaptive Enterprise Architecture Modelling." Journal of Software 10, no. 5 (May 2015): 628–38. http://dx.doi.org/10.17706/jsw.10.5.628-638.

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2

Wang, Y., Q. Pan, Y. Liang, and L. Zhang. "Adaptive spatiotemporal background modelling." IET Computer Vision 6, no. 5 (September 1, 2012): 451–58. http://dx.doi.org/10.1049/iet-cvi.2010.0229.

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3

Peterson, Donald M., and Kevin J. Riggs. "Adaptive Modelling and Mindreading." Mind and Language 14, no. 1 (March 1999): 80–112. http://dx.doi.org/10.1111/1468-0017.00104.

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4

Oden, J. Tinsley. "Adaptive multiscale predictive modelling." Acta Numerica 27 (May 1, 2018): 353–450. http://dx.doi.org/10.1017/s096249291800003x.

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The use of computational models and simulations to predict events that take place in our physical universe, or to predict the behaviour of engineered systems, has significantly advanced the pace of scientific discovery and the creation of new technologies for the benefit of humankind over recent decades, at least up to a point. That ‘point’ in recent history occurred around the time that the scientific community began to realize that true predictive science must deal with many formidable obstacles, including the determination of the reliability of the models in the presence of many uncertainties. To develop meaningful predictions one needs relevant data, itself possessing uncertainty due to experimental noise; in addition, one must determine model parameters, and concomitantly, there is the overriding need to select and validate models given the data and the goals of the simulation.This article provides a broad overview of predictive computational science within the framework of what is often called the science of uncertainty quantification. The exposition is divided into three major parts. In Part 1, philosophical and statistical foundations of predictive science are developed within a Bayesian framework. There the case is made that the Bayesian framework provides, perhaps, a unique setting for handling all of the uncertainties encountered in scientific prediction. In Part 2, general frameworks and procedures for the calculation and validation of mathematical models of physical realities are given, all in a Bayesian setting. But beyond Bayes, an introduction to information theory, the maximum entropy principle, model sensitivity analysis and sampling methods such as MCMC are presented. In Part 3, the central problem of predictive computational science is addressed: the selection, adaptive control and validation of mathematical and computational models of complex systems. The Occam Plausibility Algorithm, OPAL, is introduced as a framework for model selection, calibration and validation. Applications to complex models of tumour growth are discussed.
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5

Green, D. G., R. E. Reichelt, and R. G. Buck. "Self-adaptive modelling algorithms." Mathematics and Computers in Simulation 30, no. 1-2 (February 1988): 33–38. http://dx.doi.org/10.1016/0378-4754(88)90101-2.

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6

Popinet, Stéphane. "Quadtree-adaptive tsunami modelling." Ocean Dynamics 61, no. 9 (May 31, 2011): 1261–85. http://dx.doi.org/10.1007/s10236-011-0438-z.

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Guo, Mengmeng, and Wolfgang Karl Härdle. "Adaptive Interest Rate Modelling." Journal of Forecasting 36, no. 3 (July 20, 2016): 241–56. http://dx.doi.org/10.1002/for.2431.

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Hill, J., E. E. Popova, D. A. Ham, M. D. Piggott, and M. Srokosz. "Adapting to life: ocean biogeochemical modelling and adaptive remeshing." Ocean Science 10, no. 3 (May 9, 2014): 323–43. http://dx.doi.org/10.5194/os-10-323-2014.

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Abstract. An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in vertical nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a simple vertical column (quasi-1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2–3. Unlike previous work the adaptivity metric used is flexible and we show that capturing the physical behaviour of the model is paramount to achieving a reasonable solution. Adding biological quantities to the adaptivity metric further refines the solution. We then show the potential of this method in two case studies where we change the adaptivity metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate that adaptive meshes may provide a suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high vertical resolution whilst minimising the number of elements in the mesh. More work is required to move this to fully 3-D simulations.
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Hill, J., E. E. Popova, D. A. Ham, M. D. Piggott, and M. Srokosz. "Adapting to life: ocean biogeochemical modelling and adaptive remeshing." Ocean Science Discussions 10, no. 6 (November 5, 2013): 1997–2051. http://dx.doi.org/10.5194/osd-10-1997-2013.

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Abstract. An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. As an example, state-of-the-art models give values of primary production approximately two orders of magnitude lower than those observed in the ocean's oligotrophic gyres, which cover a third of the Earth's surface. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a~simple vertical column (quasi 1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The simulations capture both the seasonal and inter-annual variations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2–3, so reducing computational overhead. We then show the potential of this method in two case studies where we change the metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate adaptive meshes may provide a~suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high spatial resolution whilst minimising computational cost.
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10

Xiao, C. M., and P. C. Austin. "Yacht Modelling and Adaptive Control." IFAC Proceedings Volumes 33, no. 21 (August 2000): 405–10. http://dx.doi.org/10.1016/s1474-6670(17)37108-2.

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Дисертації з теми "Adaptive modelling"

1

Tukova, Sarka. "Adaptive mesh storm surge modelling." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542983.

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2

Brown, Martin. "Neurofuzzy adaptive modelling and control." Thesis, University of Southampton, 1993. https://eprints.soton.ac.uk/250157/.

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The drive for autonomy in manufacturing is making increasing demands on control systems, both for improved performance and for extra flexibility. This is reflected in the research and development of autonomously guided vehicles which must operate safely in ill-defined, complex and time-varying environments. Traditional control systems generally make infeasible assumptions which limit their application within this domain, and therefore current research has concentrated on Intelligent Control techniques in order to make the control systems flexible and robust. An integral part of intelligence is the ability to learn from a systems interaction with its environment, and this thesis provides a unified description of several adaptive neural and fuzzy networks. The recent resurgence of interest in these two anthropomorphic techniques has seen these algorithms widely applied within learning control systems, although a firm theoretical framework which can compare different networks and establish convergence and stability conditions has not evolved. Such results are essential if these adaptive algorithms are to be used in real-world applications where safety and correctness are prime concerns. The work described in this thesis addresses these questions by introducing a class of systems called associative memory networks, which is used to describe the similarities and differences which exist between certain fuzzy and neural algorithms. All of the networks can be implemented within a 3-layer structure, where the output is linearly dependent on a set of adjustable parameters. This allows parameter convergence to be established when a gradient descent training rule is used, and the rate of convergence can be directly related to the condition of the network's basis functions. The size, shape and position of these basis functions gives each network its own specific modelling attributes, since the learning rules are identical. Therefore it is important to study the network's internal representation as this provides information about how each network generalises (both interpolation and extrapolation), the rate of parameter convergence and the type of nonlinear functions which can be successfully modelled. Three networks are described in detail: the Albus CMAC, the is given of the Albus CMAC which illustrates its desirable features for on-line, nonlinear adaptive modelling and control: local learning and a computational cost which depends linearly on the input space dimension. The modelling capabilities of the algorithm are rigorously analysed and it is shown that they are strongly dependent on the generalisation parameter, and a set of consistency equations is derived which specify how the network generalises. The adaptive B-spline network, which embodies a piecewise polynomial representation, is also described and used for nonlinear modelling and constructing a static rule base which guides and autonomous vehicle into a parking slot. B-splines are also used for on-line, constrained trajectory generation where they approximate a set of velocity or positional subgoals. Fuzzy systems are typically ill-defined, although the approach taken in this thesis is to use algebraic rather than truncation operators and smooth fuzzy sets which means that the modelling capabilities of the fuzzy network can be determined exactly, and convergence and stability results can be derived for these algorithms. These results focus research on the learning, modelling and representational abilities of the networks by providing a common framework for their analysis. The desirable features of the networks (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasised, and the algorithms are all evaluated on a common time series prediciton problem.
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Kemppainen, A. (Anssi). "Adaptive methods for autonomous environmental modelling." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526218519.

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Abstract In this thesis, we consider autonomous environmental modelling, where robotic sensing platforms are utilized in environmental surveying. In order to allow a wide range of different environments, our models must be flexible to the data with some a prior assumptions. Respectively, in order to guide action planning, we need to have a unified sensing quality metric that depends on the prediction quality of our models. Finally, in order to be able to adapt to the observed information, at each iteration of the action planning algorithm, we must be able to provide solutions that aim at minimum travelling time needed to reach a certain level of sensing quality. These are the main topics in this thesis. At the center of our approaches are stationary and non-stationary Gaussian processes based on the assumption that the observed phenomenon is due to the diffusion of white noise, where diffusion kernel anisotropy and scale may vary between locations. For these models, we propose adaptation of diffusion kernels based on a structure tensor approach. Proposed methods are demonstrated with experiments that show, assuming sensor noise is not dominating, our iterative approach is able to return diffusion kernel values close to correct ones. In order to quantify how precise our models are, we propose a mutual information based sensing quality criterion, and prove that the optimal design using our sensing quality provides the best prediction quality for the model. To incorporate localization uncertainty in modelling, we also propose an approach where a posterior model is marginalized over sensing path distribution. The benefit is that this approach implicitly favors actions that result in previously visited or otherwise well-defined areas, meanwhile, maximizing the information gain. Experiments support our claims that our proposed approaches are best when considering predictive distribution quality. In action planning, our approach is to use graph-based approximation algorithms to obtain a certain level of model quality in an efficient way. In order account for spatial dependency and active localization, we propose adaptation methods that map sensing quality to vertex prices in a graph. Experiments demonstrate the benefit of our adaptation methods compared to the action planning algorithms that do not consider these specific features
Tiivistelmä Tässä väitöskirjassa tarkastellaan autonomista ympäristön mallinnusta, missä ympäristön kartoitukseen hyödynnetään robottimittausalustoja. Erilaisia ympäristöjä varten, käytettävien mallien tulee olla joustavia datalle tietyillä a priori oletuksilla. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan. Mukautuakseen uuteen informaatioon, ohjausalgoritmin tulee lisäksi pyrkiä joka iteraatiolla minimoimaan tietyn kartoituksen laadun saavuttava kulkuaika. Nämä ovat tämän väitöskirjan pääaiheet. Tämän väitöskirjan keskiössä ovat sellaiset stationaariset ja ei-stationaariset Gaussin prosessit, jotka perustuvat oletukseen että havaittu ilmiö johtuu valkoisen kohinan diffuusiosta. Diffuusiokernelin anisotrooppisuudelle ja skaalalle sallitaan paikkariippuvaisuus. Tässä väitöskirjassa esitetään näiden mallien mukauttamiseen rakennetensoripohjaisia menetelmiä. Suoritetut kokeet osoittavat, että esitetyt iteratiiviset mukauttamismenetelmät tuottavat lähes oikeita diffuusiokernelien arvoja, olettaen, että sensorikohina ei dominoi mittauksia. Mallien ennustustarkkuuden määrittämiseen esitetään keskinäisinformaatioon perustuva kartoituksen laatumetriikka. Väitöskirjassa todistetaan, että optimaalinen ennustuslaatu saavutetaan käyttämällä esitettyä laatumetriikkaa. Väitöskirjassa esitetään lisäksi laatumetriikka, jossa posteriori malli on marginalisoitu kartoituspolkujen jakauman yli. Tämän avulla voidaan huomioida paikannusepävarmuuden vaikutukset mallinnuksessa. Tällöin etuna on se, että kyseinen laatumetriikka suosii implisiittisesti sellaisia mittausalustojen ohjauksia, jotka johtavat aeimmin kartoitetuille tai helposti ennustettaville alueille samalla maksimoiden informaatiohyödyn. Suoritetut kokeet tukevat väittämiä, että väitöskirjassa esitetyt menetelmät tuottavat parhaan ennustusjakauman laadun. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan. Väitöskirjassa esitetään mukautusmenetelmiä kartoituksen laadun kuvaukseksi graafin solmujen kustannuksiksi. Tämän avulla sallitaan sekä spatiaalinen riippuvuus että aktiivinen paikannus. Mittausalustojen ohjaus vaatii vastaavasti yhtenäisen, mallien ennustuslaadusta riippuvan, kartoituksen laatumetriikan
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4

Albakour, M.-Dyaa. "Adaptive domain modelling for information retrieval." Thesis, University of Essex, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573703.

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5

Moore, Gareth Lewis. "Adaptive statistical class-based language modelling." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620315.

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6

Adam, Alexandros. "Finite element, adaptive spectral wave modelling." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45307.

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The ability to predict the wave climate has a great impact on a wide range of sectors, including coastal and offshore engineering, marine renewable energy and shipping. The state of the art in wave prediction is called spectral wave modelling and is based on a phase-averaged, spectral description of the sea-surface elevation. The governing equation, called the action balance equation, is five-dimensional and describes the generation, propagation and evolution of action density in geographic space, spectral space and time. Due to the multidimensional nature of the equation the feasible resolutions are restricted by the computational costs. The aim of this work is to propose schemes which can increase the range of possible resolutions in spectral wave modelling, with the use of adaptivity in space and angle. Thus, this work focuses on the development of an unstructured, adaptive finite element spectral wave model (Fluidity-SW). A sub-grid scale model for the spatial discretisation is used, which retains the stability of discontinuous systems, with continuous degrees of freedom. Then, a new framework for angular adaptivity is developed, with results in dynamic angular and spatial anisotropy of the angular mesh. Finally a spatially h−adaptive scheme is implemented, which can dynamically treat the spatial gradients of the solution fields. The resulting framework is thoroughly verified and validated in a wide range of test cases and realistic scenarios, against analytical solutions, wave measurements and the results obtained with the widely used SWAN model. Thus, the overall ability of the code to simulate surface gravity wind-waves in fixed and adaptive spatial and angular meshes is demonstrated.
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Sani, Yusuf. "Advanced modelling of adaptive bitrate selection." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/88536/.

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Nowadays, a typical video content provider serves a variety of platforms e.g. smartphones, web browsers, and smart TVs. Each of these platforms has specific requirements with respect to transmission and video quality. Moreover, since these devices are increasingly being used on-the-go, the environment within which most of these video streaming clients operate is both unreliable and time-varying. To cater for these heterogeneous requirements, content providers are increasingly adopting adaptive streaming services. Through such services, the quality of the video content received by a user is adapted to fit its specific requirements and capabilities. To adapt the video quality, system capabilities such as network capacity and memory have to be continuously monitored and measured, chunk requests have to be scheduled, and then the optimal video rate has to be decided. Each of these tasks is usually managed by a sub-module of the adaptive bitrate selection function. However, these sub-components interact in a non-trivial manner. For example, while on-off chunk scheduling helps to prevent buffer overflow, it negatively affects the TCP throughput. Hence, these complex interactions between these different sub-components of the adaptive streaming algorithm result in unnecessary rebufferings, undesirable variability, and sub-optimal video quality. To help simplify these interactions, this thesis develops several frameworks and models that define the relationships between the various components of the adaptive bitrate selection system. This includes deriving the valid system state space, which defines the state that an algorithm can be in at any given time, determining the allowable interactions between the various components, and identifying the video quality evolution rules that optimise QoE. Using this information, some state-of-the-art algorithms are improved and novel ones developed to demonstrate the effectiveness of the proposed approach. The result of extensive evaluations conducted both within a real-world Internet environment and with network trace shows the proposed schemes help in reducing the convergence time, startup delay, and rebuffering events, while at the same time increasing both the average and the stability of the video quality. All this is obtained without any adverse impact on the fairness among the competing players.
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Rojas, Durán Gonzalo Eduardo. "Modelling adaptive web applications in OOWS." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/2000.

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Las Aplicaciones Web Adaptativas son sistemas Web que plantean una solución a esta problemática, mediante la adaptación automática del acceso a ítems de información, servicios e hiperlinks, en base a las características de los usuarios. El desarrollo de estos sistemas exige adoptar una aproximación ingenieril que facilite la especificación de las funcionalidades adaptativas a proveer, junto con las características de los usuarios en las cuales se basan dichas funcionalidades. La presente tesis introduce una aproximación al desarrollo de Aplicaciones Web Adaptativas desde una perspectiva dirigida por modelos. Esta aproximación integra prácticas tradicionales de desarrollo de Aplicaciones Web con conceptos de-nidos y probados por la comunidad de Hipermedia Adaptativa. Tomando como base el proceso de desarrollo de aplicaciones Web OOWS (Object Oriented Web Solutions), se defíne un conjunto de primitivas conceptuales que permiten expresar técnicas adaptativas a un alto nivel de abstracción. La definición de estas primitivas es respaldada por una propuesta de Modelado de Usuarios. Además, un conjunto de estrategias de modelado permite incorporar Métodos Adaptativos a los esquemas navegacionales de OOWS, en base a dichas primitivas.
Rojas Durán, GE. (2008). Modelling adaptive web applications in OOWS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2000
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Wilson, Troy Daniel. "Adaptive Sampling For Efficient Online Modelling." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17257.

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This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general.
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Deshmukh, Pushkaraj M. "Modelling error estimation and adaptive modelling of functionally graded materials." Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1096036755.

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Книги з теми "Adaptive modelling"

1

Byrnes, Christopher Ian, and Alexander B. Kurzhanski, eds. Modelling and Adaptive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0043171.

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2

Xiao, Chengmo. Yacht modelling and adaptive control. Hauppauge NY: Nova Science Publishers, 2009.

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3

J, Harris C., ed. Neurofuzzy adaptive modelling and control. New York: Prentice Hall, 1994.

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4

Harris, Chris, Xia Hong, and Qiang Gan. Adaptive Modelling, Estimation and Fusion from Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-18242-6.

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5

Ulrich, Gabbert, Society of Automotive Engineers, and Euromech Colloquium (373rd : 1998 : Magdeburg, Germany), eds. Modelling and control of adaptive mechanical structures. Warrendale, Pa: Society of Automotive Engineers, 1999.

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6

Engineers, Society of Automotive, ed. Modelling and control of adaptive mechanical structures. Warrendale: Society of Automotive Engineers, 1997.

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7

Gerencséer, L., and P. E. Caines, eds. Topics in Stochastic Systems: Modelling, Estimation and Adaptive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0009295.

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8

Donnelly, Peter Gerard. Adaptive parametric modelling of narrowband signals for sonar applications. [S.l: The Author], 1993.

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9

1945-, Gerencsér L., and Caines Peter E. 1945-, eds. Topics in stochastic systems: Modelling, estimation, and adaptive control. Berlin: Springer-Verlag, 1991.

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10

Harris, Chris. Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.

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Частини книг з теми "Adaptive modelling"

1

Jakeman, Tony, Serena Chen, Lachlan Newham, and Carmel A. Pollino. "Modelling and Adaptive Environmental Management." In Adaptive Environmental Management, 173–87. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9632-7_9.

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2

Beltratti, Andrea, and Sergio Margarita. "An Artificial Adaptive Speculative Stock Market." In Financial Modelling, 155–78. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-86706-4_9.

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3

Heckel, B., A. E. Uva, B. Hamann, and K. I. Joy. "Surface Reconstruction Using Adaptive Clustering Methods." In Geometric Modelling, 199–218. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6270-5_11.

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4

Fliess, Michel. "Nonlinear control theory and differential algebra." In Modelling and Adaptive Control, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0043180.

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5

Harris, Chris, Xia Hong, and Qiang Gan. "Parsimonious neurofuzzy modelling." In Adaptive Modelling, Estimation and Fusion from Data, 103–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-18242-6_5.

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Harris, Chris, Xia Hong, and Qiang Gan. "Local neurofuzzy modelling." In Adaptive Modelling, Estimation and Fusion from Data, 153–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-18242-6_6.

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7

Makowski, Ryszard A., and Radoslaw Zimroz. "Adaptive Bearings Vibration Modelling for Diagnosis." In Adaptive and Intelligent Systems, 248–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23857-4_26.

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8

Barreteau, Olivier, François Bousquet, Michel Étienne, Véronique Souchère, and Patrick d’Aquino. "Companion Modelling: A Method of Adaptive and Participatory Research." In Companion Modelling, 13–40. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-017-8557-0_2.

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9

Carey, G. F., J. Schmidt, and M. Sharma. "Adaptive Grids for Semiconductor Modelling." In Computational Electronics, 37–41. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2124-9_6.

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10

Kingdon, Jason. "Adaptive Systems and Financial Modelling." In Perspectives in Neural Computing, 19–35. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_2.

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Тези доповідей конференцій з теми "Adaptive modelling"

1

Haguenauer, Pierre, and Ronald Holzloehner. "Seven years of sodium history at Paranal: temporal evolution, statistics and modelling." In Adaptive Optics Systems IX, edited by Dirk Schmidt, Elise Vernet, and Kathryn J. Jackson, 190. SPIE, 2024. http://dx.doi.org/10.1117/12.3017643.

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2

McGaughey, Donald, and George J. M. Aitken. "Temporal Characteristics and Modelling of Atmospherically-Distorted Wavefront Slopes." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.awc.5.

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Анотація:
The wavefronts distorted by a Kolmogorov turbulent atmosphere are fractal surfaces [1] and have the properties of fractional Brownian motion (FBM) with a self-similarity parameter of 5/6. Such a FBM process is nonstationary and has a power-law spectrum with spectral index -8/3. In an adaptive optics system a Shack-Hartmann (SH) wavefront sensor (WFS) delivers the time series of wavefront (WF) slopes measured at each lenslet subaperture. While the FBM wavefront exhibits persistence, and consequently has predictability, the derivatives or slopes of this FBM process are antipersistent [2] with spectral index -2/3. This means that the WF slopes would have limited predictability at least by conventional mean-square prediction methods. Thus the adaptive optics (AO) control strategy that treats WF slopes as an unpredictable random walk process would seem justified. Within a closed loop system the difference between the incident wavefront and the correction by the deformable mirror is equivalent to a differentiation or increments process because of the loop delay, in which case the wavefront sensor gives essentially the second derivative. The second derivative process of FBM is also antipersistent.
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3

Agapito, Guido, Daniele Vassallo, Cedric Plantet, Valentina Viotto, Enrico Pinna, Benoit Neichel, Thierry Fusco, and François Rigaut. "MAVIS: system modelling and performance prediction." In Adaptive Optics Systems VII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2020. http://dx.doi.org/10.1117/12.2561252.

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4

Blacknell, D. "Adaptive clutter models." In IET Seminar on Radar Clutter Modelling. IEE, 2008. http://dx.doi.org/10.1049/ic:20080151.

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5

Völlinger, U., M. Wewetzer, F. Hausser, and A. Ley. "An Adaptive Multiresolutional Approach for Interactive Simulation of Cloth." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-043.

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6

Kubalčík, M., and V. Bobál. "Adaptive Predictive Control of Three - Tank - System." In Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.675-037.

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7

Kuczewski, Myers, and Steck. "Adaptive modelling for cognitive structures." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118386.

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8

Druce, T., and M. Andrews. "Modelling adaptive control - a reality?" In IET Road Transport Information and Control Conference and the ITS United Kingdom Members' Conference (RTIC 2010). Better transport through technology. IET, 2010. http://dx.doi.org/10.1049/cp.2010.0385.

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9

Peng, Jia, Mingyang Ma, Miao Zhang, Xuebo Wu, Weihua Wang, and Yangyang Zhan. "Non-parametric point spread function modelling for adaptive optics systems." In Adaptive Optics Systems VII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2020. http://dx.doi.org/10.1117/12.2560119.

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10

Branigan, Emma, Suzanne Martin, Matthew Sheehan, and Kevin Murphy. "Modelling spherical aberration detection in an analog holographic wavefront sensor." In Adaptive Optics and Applications. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoa.2022.of2b.4.

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Анотація:
The analog holographic wavefront sensor (AHWFS) is a simple and robust solution to wavefront sensing in turbulent environments. Here, the ability of a photopolymer-based AHWFS to detect refractively generated spherical aberration is modelled and verified.
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Звіти організацій з теми "Adaptive modelling"

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Babuska, I., and Ch Schwab. Adaptive Hierarchic Modelling of Plates and Shells with A-Posteriori Error Estimation. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada259941.

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2

Sett, Dominic, Florian Waldschmidt, Alvaro Rojas-Ferreira, Saut Sagala, Teresa Arce Mojica, Preeti Koirala, Patrick Sanady, et al. Climate and disaster risk analytics tool for adaptive social protection. United Nations University - Institute for Environment and Human Security, March 2022. http://dx.doi.org/10.53324/wnsg2302.

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Анотація:
Adaptive Social Protection (ASP) as discussed in this report is an approach to enhance the well-being of communities at risk. As an integrated approach, ASP builds on the interface of Disaster Risk Management (DRM), Climate Change Adaptation (CCA) and Social Protection (SP) to address interconnected risks by building resilience, thereby overcoming the shortcomings of traditionally sectoral approaches. The design of meaningful ASP measures needs to be informed by specific information on risk, risk drivers and impacts on communities at risk. In contrast, a limited understanding of risk and its drivers can potentially lead to maladaptation practices. Therefore, multidimensional risk assessments are vital for the successful implementation of ASP. Although many sectoral tools to assess risks exist, available integrated risk assessment methods across sectors are still inadequate in the context of ASP, presenting an important research and implementation gap. ASP is now gaining international momentum, making the timely development of a comprehensive risk analytics tool even more important, including in Indonesia, where nationwide implementation of ASP is currently under way. OBJECTIVE: To address this gap, this study explores the feasibility of a climate and disaster risk analytics tool for ASP (CADRAT-ASP), combining sectoral risk assessment in the context of ASP with a more comprehensive risk analytics approach. Risk analytics improve the understanding of risks by locating and quantifying the potential impacts of disasters. For example, the Economics of Climate Adaptation (ECA) framework quantifies probable current and expected future impacts of extreme events and determines the monetary cost and benefits of specific risk management and adaptation measures. Using the ECA framework, this report examines the viability and practicality of applying a quantitative risk analytics approach for non-financial and non-tangible assets that were identified as central to ASP. This quantitative approach helps to identify cost-effective interventions to support risk-informed decision making for ASP. Therefore, we used Nusa Tenggara, Indonesia, as a case study, to identify potential entry points and examples for the further development and application of such an approach. METHODS & RESULTS: The report presents an analysis of central risks and related impacts on communities in the context of ASP. In addition, central social protection dimensions (SPD) necessary for the successful implementation of ASP and respective data needs from a theoretical perspective are identified. The application of the quantitative ECA framework is tested for tropical storms in the context of ASP, providing an operational perspective on technical feasibility. Finally, recommendations on further research for the potential application of a suitable ASP risk analytics tool in Indonesia are proposed. Results show that the ECA framework and its quantitative modelling platform CLIMADA successfully quantified the impact of tropical storms on four SPDs. These SPDs (income, access to health, access to education and mobility) were selected based on the results from the Hazard, Exposure and Vulnerability Assessment (HEVA) conducted to support the development of an ASP roadmap for the Republic of Indonesia (UNU-EHS 2022, forthcoming). The SPDs were modelled using remote sensing, gridded data and available global indices. The results illustrate the value of the outcome to inform decision making and a better allocation of resources to deliver ASP to the case study area. RECOMMENDATIONS: This report highlights strong potential for the application of the ECA framework in the ASP context. The impact of extreme weather events on four social protection dimensions, ranging from access to health care and income to education and mobility, were successfully quantified. In addition, further developments of CADRAT-ASP can be envisaged to improve modelling results and uptake of this tool in ASP implementation. Recommendations are provided for four central themes: mainstreaming the CADRAT approach into ASP, data and information needs for the application of CADRAT-ASP, methodological advancements of the ECA framework to support ASP and use of CADRAT-ASP for improved resilience-building. Specific recommendations are given, including the integration of additional hazards, such as flood, drought or heatwaves, for a more comprehensive outlook on potential risks. This would provide a broader overview and allow for multi-hazard risk planning. In addition, high-resolution local data and stakeholder involvement can increase both ownership and the relevance of SPDs. Further recommendations include the development of a database and the inclusion of climate and socioeconomic scenarios in analyses.
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3

Hasan, Abdulghani. Flood Modelling Tool : an integrated GIS and hydrological modelling tool for planning nature-based solutions in the urban environment. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.5s9t2ca774.

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The risk of pluvial flooding is going to increase as climate change causes an increase in intense precipitation along with urbanisation leading to an increase in impermeable surfaces. In the last decade, cities such as Malmö and Copenhagen have already experienced severe pluvial flooding that has caused extensive damage. Adapting to climate change by creating flood resilient urban areas is therefore important and blue-green infrastructure (BGI) may be one measure to accomplish this. A hydrological model called TFM-DYN has been used to investigate whether BGI can aid the mitigation of pluvial flooding. TFM-DYN can also assist in selecting the best locations of BGIs. The problem of modeling urban floods using distributed high resolution hydrological models while considering the hydrological process in the upstream area is difficult due to the limited current computation capacity. However, coupling a distributed hydrological model (TFM-DYN) with an other semi distributed models (HYPE) is crucial to enable simulate, predict and map floods with high-resolution for an urban area while considering its catchment area. With the using of the new suggested coupled hydrological model, it is possible to connect and use the output results from HYPE model as an input to a distributed model (TFM-DYN). The interaction between HYPE and TFM-DYN will consider the hydrologic process occurred outside the model boundary of the interested urban area. The coupling of the two models will help initiating the model with real water depth data that may lead to more realistic simulation. The procedure of input data manipulation using the two model interactions is explained in details. The model is tested on a selected urban area to dynamically simulate the changes in the water depth with time using high resolution gridded data. The new coupled model can be of a great tool for wide range of user and stakeholders as an example to municipalities, water experts, insurance companies and to all other interested water organizations who have access to regional catchment models and in need for a high-resolution, flood simulation and mapping model.
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