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Добірка наукової літератури з теми "Shot-noise processes"

Автор: Grafiati

Опубліковано: 17 серпня 2022

Оновлено: 14 вересня 2022

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Статті в журналах з теми "Shot-noise processes"

1

Møller, Jesper. "Shot noise Cox processes." Advances in Applied Probability 35, no. 03 (September 2003): 614–40. http://dx.doi.org/10.1017/s0001867800012465.

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Shot noise Cox processes constitute a large class of Cox and Poisson cluster processes in ℝd, including Neyman-Scott, Poisson-gamma and shot noise G Cox processes. It is demonstrated that, due to the structure of such models, a number of useful and general results can easily be established. The focus is on the probabilistic aspects with a view to statistical applications, particularly results for summary statistics, reduced Palm distributions, simulation with or without edge effects, conditional simulation of the intensity function and local and spatial Markov properties.

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2

Møller, Jesper. "Shot noise Cox processes." Advances in Applied Probability 35, no. 3 (September 2003): 614–40. http://dx.doi.org/10.1239/aap/1059486821.

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Анотація:

Shot noise Cox processes constitute a large class of Cox and Poisson cluster processes in ℝd, including Neyman-Scott, Poisson-gamma and shot noise G Cox processes. It is demonstrated that, due to the structure of such models, a number of useful and general results can easily be established. The focus is on the probabilistic aspects with a view to statistical applications, particularly results for summary statistics, reduced Palm distributions, simulation with or without edge effects, conditional simulation of the intensity function and local and spatial Markov properties.

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3

Møller, Jesper, and Giovanni Luca Torrisi. "Generalised shot noise Cox processes." Advances in Applied Probability 37, no. 01 (March 2005): 48–74. http://dx.doi.org/10.1017/s0001867800000033.

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Анотація:

We introduce a class of Cox cluster processes called generalised shot noise Cox processes (GSNCPs), which extends the definition of shot noise Cox processes (SNCPs) in two directions: the point process that drives the shot noise is not necessarily Poisson, and the kernel of the shot noise can be random. Thereby, a very large class of models for aggregated or clustered point patterns is obtained. Due to the structure of GSNCPs, a number of useful results can be established. We focus first on deriving summary statistics for GSNCPs and, second, on how to simulate such processes. In particular, results on first- and second-order moment measures, reduced Palm distributions, the J-function, simulation with or without edge effects, and conditional simulation of the intensity function driving a GSNCP are given. Our results are exemplified in important special cases of GSNCPs, and we discuss their relation to the corresponding results for SNCPs.

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4

Møller, Jesper, and Giovanni Luca Torrisi. "Generalised shot noise Cox processes." Advances in Applied Probability 37, no. 1 (March 2005): 48–74. http://dx.doi.org/10.1239/aap/1113402399.

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Анотація:

We introduce a class of Cox cluster processes called generalised shot noise Cox processes (GSNCPs), which extends the definition of shot noise Cox processes (SNCPs) in two directions: the point process that drives the shot noise is not necessarily Poisson, and the kernel of the shot noise can be random. Thereby, a very large class of models for aggregated or clustered point patterns is obtained. Due to the structure of GSNCPs, a number of useful results can be established. We focus first on deriving summary statistics for GSNCPs and, second, on how to simulate such processes. In particular, results on first- and second-order moment measures, reduced Palm distributions, the J-function, simulation with or without edge effects, and conditional simulation of the intensity function driving a GSNCP are given. Our results are exemplified in important special cases of GSNCPs, and we discuss their relation to the corresponding results for SNCPs.

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5

Hsing, Tailen, and J. L. Teugels. "Extremal properties of shot noise processes." Advances in Applied Probability 21, no. 03 (September 1989): 513–25. http://dx.doi.org/10.1017/s0001867800018784.

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Анотація:

Consider the shot noise process X(t):= Σi h(t – τ i ), , where h is a bounded positive non-increasing function supported on a finite interval, and the are the points of a renewal process η on [0, ). In this paper, the extremal properties of {X(t)} are studied. It is shown that these properties can be investigated in a natural way through a discrete-time process which records the states of {X(t)} at the points of η. The important special case where η is Poisson is treated in detail, and a domain-of-attraction result for the compound Poisson distribution is obtained as a by-product.

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6

Verovkin, G. K., and A. V. Marynych. "Stationary limits of shot noise processes." Theory of Probability and Mathematical Statistics 101 (January 5, 2021): 67–83. http://dx.doi.org/10.1090/tpms/1112.

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7

Hsing, Tailen, and J. L. Teugels. "Extremal properties of shot noise processes." Advances in Applied Probability 21, no. 3 (September 1989): 513–25. http://dx.doi.org/10.2307/1427633.

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Анотація:

Consider the shot noise process X(t):= Σih(t – τi), , where h is a bounded positive non-increasing function supported on a finite interval, and the are the points of a renewal process η on [0, ). In this paper, the extremal properties of {X(t)} are studied. It is shown that these properties can be investigated in a natural way through a discrete-time process which records the states of {X(t)} at the points of η. The important special case where η is Poisson is treated in detail, and a domain-of-attraction result for the compound Poisson distribution is obtained as a by-product.

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8

Biermé, Hermine, and Agnès Desolneux. "Crossings of smooth shot noise processes." Annals of Applied Probability 22, no. 6 (December 2012): 2240–81. http://dx.doi.org/10.1214/11-aap807.

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9

Miyoshi, Naoto. "CORRECTION TO “A NOTE ON BOUNDS AND MONOTONICITY OF SPATIAL STATIONARY COX SHOT NOISES”." Probability in the Engineering and Informational Sciences 19, no. 3 (June 22, 2005): 405–7. http://dx.doi.org/10.1017/s0269964805050242.

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Анотація:

In the Note published last year [1], bounds and monotonicity of shot-noise and max-shot-noise processes driven by spatial stationary Cox point processes are discussed in terms of some stochastic order. Although all the statements concerning the shot-noise processes remain valid, those concerning the max-shot-noise processes have to be corrected.

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10

Iksanov, Alexander, and Bohdan Rashytov. "A functional limit theorem for general shot noise processes." Journal of Applied Probability 57, no. 1 (March 2020): 280–94. http://dx.doi.org/10.1017/jpr.2019.95.

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Анотація:

AbstractBy a general shot noise process we mean a shot noise process in which the counting process of shots is arbitrary locally finite. Assuming that the counting process of shots satisfies a functional limit theorem in the Skorokhod space with a locally Hölder continuous Gaussian limit process, and that the response function is regularly varying at infinity, we prove that the corresponding general shot noise process satisfies a similar functional limit theorem with a different limit process and different normalization and centering functions. For instance, if the limit process for the counting process of shots is a Brownian motion, then the limit process for the general shot noise process is a Riemann–Liouville process. We specialize our result for five particular counting processes. Also, we investigate Hölder continuity of the limit processes for general shot noise processes.

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Більше джерел

Дисертації з теми "Shot-noise processes"

1

DeMino, Kenneth William. "Shot noise approach to stochastic resonance." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/27968.

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2

Ilhe, Paul. "Estimation statistique des éléments d'un processus shot-noise." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0052.

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Анотація:

Dans le cadre de la spectrométrie gamma, cette thèse introduit de nouveaux estimateurs non paramétriques de l’intensité et de la densité des marques d’un processus shot-noise à partir d’un nombre fini d’observations du processus échantillonné à basse fréquence. Les méthodes proposées utilisent une relation non linéaire reliant la fonction caractéristique de la loi marginale du processus à la densité des marques. Elles sont particulièrement rapides et possèdent l’avantage d’être efficaces mêmes pour des intensités élevées. Les performances de ces méthodes sont étudiées quantitativement et illustrées à la fois pour des données simulées et réelles provenant du CEA Saclay. En particulier, les estimateurs de la densité des marques permettent de corriger les artefacts de pics multiples
In the context of gamma-spectroscopy, this thesis introduces new nonparametric estimators of the intensity and the mark’s density of a shot-noise process based on a finite sample of low-frequency observations of this stochastic process. The methods developed exploit a nonlinear functional equation linking the characteristic function of the marginal law of the shot-noise with the mark’s density function. They are particularly time-efficient and perform well even for processes with high intensity. The performances of the methods are quantitatively studied and illustrations are provided both on simulated datasets and real datasets stemming from the CEA. In particular, our methods corrects the multiple peak artefacts that arises with classical techniques

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3

Hutton, Jane Louise. "Non-negative time series and shot noise processes as models for dry rivers." Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38044.

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4

Launay, Claire. "Discrete determinantal point processes and their application to image processing." Thesis, Université de Paris (2019-....), 2020. http://www.theses.fr/2020UNIP7034.

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Анотація:

Les processus ponctuels déterminantaux (Determinantal Point Processes ou DPP en anglais) sont des modèles probabilistes qui modélisent les corrélations négatives ou la répulsion à l'intérieur d'un ensemble d'éléments. Ils ont tendance à générer des sous-ensembles d'éléments diversifiés ou éloignés les uns des autres. Cette notion de similarité ou de proximité entre les points de l'ensemble est définie et conservée dans le noyau associé à chaque DPP. Cette thèse étudie ces modèles dans un cadre discret, définis dans un ensemble discret et fini d'éléments. Nous nous sommes intéressés à leur application à des questions de traitement d'images, lorsque l'ensemble de points de départ correspond aux pixels ou aux patchs d'une image. Les Chapitres 1 et 2 introduisent les processus ponctuels déterminantaux dans un cadre discret général, leurs propriétés principales et les algorithmes régulièrement utilisés pour les échantillonner, c'est-à-dire pour sélectionner un sous-ensemble de points distribué selon le DPP choisi. Dans ce cadre, le noyau d'un DPP est une matrice. L'algorithme le plus utilisé est un algorithme spectral qui repose sur le calcul des valeurs propres et des vecteurs propres du noyau du DPP. Dans le Chapitre 2, nous présentons un algorithme d'échantillonnage qui repose sur une procédure de thinning (ou amincissement) et sur une décomposition de Cholesky mais qui n'a pas besoin de la décomposition spectrale du noyau. Cet algorithme est exact et, sous certaines conditions, compétitif avec l'algorithme spectral. Le Chapitre 3 présente les DPP définis sur l'ensemble des pixels d'une image, appelés processus pixelliques déterminantaux (Determinantal Pixel Processes ou DPixP en anglais). Ce nouveau cadre impose des hypothèses de périodicité et de stationnarité qui ont des conséquences sur le noyau du processus et sur les propriétés de répulsion générée par ce noyau. Nous étudions aussi ce modèle appliqué à la synthèse de textures gaussiennes, grâce à l'utilisation de modèles shot noise. Nous nous intéressons également à l'estimation du noyau de DPixP à partir d'un ou plusieurs échantillons. Le Chapitre 4 explore les processus ponctuels déterminantaux définis sur l'ensemble des patchs d'une image, c'est-à-dire la famille des sous-images carrées d'une taille donnée dans une image. L'objectif est de sélectionner une proportion de ces patchs, suffisamment diversifiée pour être représentative de l'information contenue dans l'image. Une telle sélection peut permettre d'accélérer certains algorithmes de traitements d'images basés sur les patchs, voire d'améliorer la qualité d'algorithmes existants ayant besoin d'un sous-échantillonnage des patchs. Nous présentons une application de cette question à un algorithme de synthèse de textures
Determinantal point processes (DPPs in short) are probabilistic models that capture negative correlations or repulsion within a set of elements. They tend to generate diverse or distant subsets of elements. This notion of similarity or proximity between elements is defined and stored in the kernel associated with each DPP. This thesis studies these models in a discrete framework, defined on a discrete and finite set of elements. We are interested in their application to image processing, when the initial set of points corresponds to the pixels or the patches of an image. Chapter 1 and 2 introduce determinantal point processes in a general discrete framework, their main properties and the algorithms usually used to sample them, i.e. used to select a subset of points distributed according to the chosen DPP. In this framework, the kernel of a DPP is a matrix. The main algorithm is a spectral algorithm based on the computation of the eigenvalues and the eigenvectors of the DPP kernel. In Chapter 2, we present a sampling algorithm based on a thinning procedure and a Cholesky decomposition but which does not require the spectral decomposition of the kernel. This algorithm is exact and, under certain conditions, competitive with the spectral algorithm. Chapter 3 studies DPPs defined over all the pixels of an image, called Determinantal Pixel Processes (DPixPs). This new framework imposes periodicity and stationarity assumptions that have consequences on the kernel of the process and on properties of the repulsion generated by this kernel. We study this model applied to Gaussian textures synthesis, using shot noise models. In this chapter, we are also interested in the estimation of the DPixP kernel from one or several samples. Chapter 4 explores DPPs defined on the set of patches of an image, that is the family of small square images contained in the image. The aim is to select a proportion of these patches, diverse enough to be representative of the information contained in the image. Such a selection can speed up certain patch-based image processing algorithms, or even improve the quality of existing algorithms that require patch subsampling. We present an application of this question to a texture synthesis algorithm

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5

Rusudan, Kevkhishvili. "A Study of Approximations and Transformations of Markov Processes and their Applications to Credit Risk Analysis." Kyoto University, 2019. http://hdl.handle.net/2433/242462.

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6

Ferreira, Brigham Marco Paulo. "Nonstationary Stochastic Dynamics of Neuronal Membranes." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066111/document.

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Анотація:

Les neurones interagissent à travers leur potentiel de membrane qui a en général une évolution temporelle complexe due aux nombreuses entrées synaptiques irrégulières reçues. Cette évolution est mieux décrite en termes probabilistes, en raison de ces entrées irrégulières ou «bruit synaptique». L'évolution temporelle du potentiel de membrane est stochastique mais aussi déterministe: stochastique, car conduite par des entrées synaptiques qui arrivent de façon aléatoire dans le temps, et déterministe, car un neurone biologique a une évolution temporelle très similaire quand soumis à une même séquence d'entrées synaptiques. Nous étudions les propriétés statistiques d'un modèle simplifié de neurone soumis à des entrées à taux variable d'où en résulte l'évolution non-stationnaire du potentiel de membrane. Nous considérons un modèle passif de membrane neuronale, sans mécanisme de décharge neuronale, soumis à des entrées à courant ou à conductance sous la forme d'un processus de «shot noise». Les fluctuations du potentiel de membrane sont aussi modélisées par un processus stochastique similaire, de «shot noise» filtré. Nous avons analysé les propriétés statistiques de ces processus dans le cadre des transformations de processus ponctuels de Poisson. Des propriétés de ces transformations sont dérivées les statistiques non-stationnaires du processus. Nous obtenons ainsi des expressions analytiques exactes pour les moments et cumulants du processus filtré dans le cas général des taux d'entrée variables. Ce travail ouvre de nombreuses perspectives pour l'analyse de neurones dans les conditions in vivo, en présence d'entrées synaptiques intenses et bruitées
Neurons interact through their membrane potential that generally has a complex time evolution due to numerous irregular synaptic inputs received. This complex time evolution is best described in probabilistic terms due to this irregular or "noisy" activity. The time evolution of the membrane potential is therefore both stochastic and deterministic: it is stochastic since it is driven by random input arrival times, but also deterministic, since subjecting a biological neuron to the same sequence of input arrival times often results in very similar membrane potential traces. In this thesis, we investigated key statistical properties of a simplified neuron model under nonstationary input from other neurons that results in nonstationary evolution of membrane potential statistics. We considered a passive neuron model without spiking mechanism that is driven by input currents or conductances in the form of shot noise processes. Under such input, membrane potential fluctuations can be modeled as filtered shot noise currents or conductances. We analyzed the statistical properties of these filtered processes in the framework of Poisson Point Processes transformations. The key idea is to express filtered shot noise as a transformation of random input arrival times and to apply the properties of these transformations to derive its nonstationary statistics. Using this formalism we derive exact analytical expressions, and useful approximations, for the mean and joint cumulants of the filtered process in the general case of variable input rate. This work opens many perspectives for analyzing neurons under in vivo conditions, in the presence of intense and noisy synaptic inputs

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7

Constant, Camille. "Modélisation stochastique et analyse statistique de la pulsatilité en neuroendocrinologie." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2330.

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L’objectif de cette thèse est de proposer plusieurs modèles probabilistes pour représenter l’activité calcique des neurones et comprendre son implication dans la sécrétion d’hormone GnRH. Ce travail s’appuie sur des expériences réalisées à l’INRA Centre Val-de-Loire. Le Chapitre 1 propose une modélisation continue, où nous étudions un processus markovien de type shot-noise. Le Chapitre 2 étudie un modèle discret de type AR(1) basé sur la discrétisation du modèle du Chapitre 1 et propose une première estimation des paramètres. Le Chapitre 3 propose un autre modèle discret de type AR(1) où les innovations sont la somme d’une variable de Bernouilli et d’une variable gaussienne représentant un bruit, avec prise en compte d’une tendance linéaire. Des estimations des paramètres sont proposées dans le but d’une détection des sauts dans les trajectoires des neurones. Le Chapitre 4 étudie une expérience biologique comportant 33 neurones. Avec la modélisation du Chapitre 3, nous détectons des instants de synchronisation (saut simultané d’une grande proportion des neurones de l’expérience) puis à l’aide de simulations, nous testons la qualité de la méthode utilisée et la comparons à une méthode expérimentale
The aim of this thesis is to propose several models representing neuronal calcic activity and unsderstand its applicatition in the secretion of GnRH hormone. This work relies on experience realised in INRA Centre Val de Loire. Chapter 1 proposes a continuous model, in which we examine a Markov process of shot-noise type. Chapter 2 studies a discrete model type AR(1), based on a discretization of the model from Chapter 1 and proposes a first estimation of the parameters. Chapter 3 proposes another dicrete model, type AR(1), in which the innovations are the sum of a Bernouilli variable and a Gaussian variable representing a noise, and taking into account a linear drift . Estimations of the parameters are given in order to detect spikes in neuronal paths. Chapter 4 studies a biological experience involving 33 neurons. With the modelisation of Chapter 3, we detect synchronization instants (simultaneous spkike of a high proportion of neurons of the experience) and then, using simulations, we test the quality of the method that we used and we compare it to an experimental approach

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8

Marouby, Matthieu. "Trois études de processus fractionnaires." Toulouse 3, 2010. http://thesesups.ups-tlse.fr/946/.

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Анотація:

Une première partie concerne la simulation du processus nommé Local Time Fractional Stable Motion (LTFSM). Ce processus, introduit par Cohen et Samorodnitsky en 2006, est défini comme l'intégrale du temps local d'un mouvement brownien fractionnaire par rapport à une mesure aléatoire stable, les deux aléas étant définis sur deux espaces de probabilité indépendants. La méthode repose sur la représentation en série du processus et on obtient un contrôle de l'approximation. Dans une seconde partie, on étudie des processus obtenus comme limite de sommes de micropulsations. Dans ce cas on étudie en particulier le comportement si les "montées" et les "descentes" ne sont pas égales. Enfin, on généralise ces processus à des index multidimensionnels. Les processus limites obtenus sont variés, du mouvement brownien standard à des draps browniens multifractionnaires selon les cas étudiés. Enfin, on étudie un modèle physique correspondant au champ créé par des particules chargées distribuées dans un hyperplan. Le processus limite est fractionnaire, gaussien, centré et, dans certains cas, connu (notamment le mouvement brownien fractionnaire). On étudie ensuite certaines propriétés, comme le nombre de minimum locaux obtenus. Cette partie soulève de nombreuses questions encore ouvertes
The first part is devoted to the simulation of the Local Time Fractional Stable Motion (LTFSM). This process, which was introduced in 2006 by Cohen and Samorodnitsky, is defined as the integration of the local time of a fractional Brownian motion with respect to a random stable measure, the randomness of both objects being defined on two independent probability spaces. Using a series representation method to simulate it, I obtain a control of the approximation. In the second part, I study processes obtained as limits of sums of micropulses, specifically focusing on behavior when "ups" and "downs" of the micropulses are not equal. Then, I generalize the processes obtained to processes with multidimensional indices. Processes obtained in this work vary from standard Brownian motions to multifractional Brownian sheets. Finally, I study a model from physic theory, a field created by charged particles randomly distributed in a hyperplan. The limit process is fractional, centered, Gaussian and in some cases well-known like fractional Brownian motion. Eventually, I study some of its characteristics, such as the number of local minima. This part raises many questions that have yet to be resolved

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9

Xiao, Yuanhui. "Shot noise processes." 2003. http://purl.galileo.usg.edu/uga%5Fetd/xiao%5Fyuanhui%5F200308%5Fphd.

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Анотація:

Thesis (Ph. D.)--University of Georgia, 2003.
Directed by Robert Lund. Includes articles submitted to Statistical inference in stochastic processes, and Stochastic processes and their applications. Includes bibliographical references.

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10

Kučera, Petr. "Momentové metody odhadu parametrů časoprostorových shlukových bodových procesů." Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-397743.

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This paper is concerned with estimation of space-time shot-noise Cox process parametric models. We introduce the two-step estimation method, where in the second step we use composite likelihood or Palm likelihood. For the two-step estimation method based on Palm likelihood we prove consistency and asymptotic normality theorem. Finally we compare composite likelihood with Palm likelihood in simulation studies, where we add for comparison minimum contrast method. Results for minimum contrast method are taken from the literature. 1

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Частини книг з теми "Shot-noise processes"

1

Schmidt, Thorsten. "Shot-Noise Processes in Finance." In From Statistics to Mathematical Finance, 367–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50986-0_18.

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2

Levy, Bernard C. "Poisson Process and Shot Noise." In Random Processes with Applications to Circuits and Communications, 235–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22297-0_7.

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3

Jedidi, Wissem, Jalel Almhana, Vartan Choulakian, and Robert McGorman. "General Shot Noise Processes and Functional Convergence to Stable Processes." In Stochastic Differential Equations and Processes, 151–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22368-6_3.

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4

Giraitis, L., and D. Surgailis. "On shot noise processes attracted to fractional Lévy motion." In Stable Processes and Related Topics, 261–73. Boston, MA: Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-1-4684-6778-9_12.

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5

McCormick, W. P., and Lynne Seymour. "Extreme Values for a Class of Shot-Noise Processes." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 33–46. Beachwood, OH: Institute of Mathematical Statistics, 2001. http://dx.doi.org/10.1214/lnms/1215090682.

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6

Lax, Melvin, Wei Cai, and Min Xu. "Shot noise." In Random Processes in Physics and Finance, 93–112. Oxford University Press, 2006. http://dx.doi.org/10.1093/acprof:oso/9780198567769.003.0006.

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7

"Fractal Shot Noise." In Fractal-Based Point Processes, 185–99. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471754722.ch9.

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8

"Poisson Processes and Shot Noise." In Introduction to Random Signals and Noise, 193–210. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470024135.ch8.

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9

"Fractal-Shot-Noise-Driven Point Processes." In Fractal-Based Point Processes, 201–24. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471754722.ch10.

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10

"Shot noise processes and their properties." In Translations of Mathematical Monographs, 145–66. Providence, Rhode Island: American Mathematical Society, 2000. http://dx.doi.org/10.1090/mmono/188/05.

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Тези доповідей конференцій з теми "Shot-noise processes"

1

Klapwijk, Teun M. "Higher-order tunneling processes and enhanced shot noise in superconducting tunnel devices." In SPIE's First International Symposium on Fluctuations and Noise, edited by Michael B. Weissman, Nathan E. Israeloff, and A. Shulim Kogan. SPIE, 2003. http://dx.doi.org/10.1117/12.496957.

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2

Arendt, Paul D., Wei Chen, and Daniel W. Apley. "Objective–Oriented Sequential Sampling for Simulation Based Robust Design Considering Multiple Sources of Uncertainty." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70639.

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Анотація:

Sequential sampling strategies have been developed for managing complexity when using computationally expensive computer simulations in engineering design. However, much of the literature has focused on objective-oriented sequential sampling methods for deterministic optimization. These methods cannot be directly applied to robust design which must account for uncontrollable variations in certain input variables (i.e., noise variables). Obtaining a robust design that is insensitive to variations in the noise variables is more challenging. Even though methods exist for sequential sampling in design under uncertainty, the majority of the existing literature does not systematically take into account the interpolation uncertainty that results from limitations on the number of simulation runs, the effect of which is inherently more severe than in deterministic design. In this paper, we develop a systematic objective-oriented sequential sampling approach to robust design with consideration of both noise variable uncertainty and interpolation uncertainty. The method uses Gaussian processes to model the costly simulator and quantify the interpolation uncertainty within a robust design objective. We examine several criteria, including our own proposed criteria, for sampling the design and noise variables and provide insight into their performance behaviors. We show that for both of the examples considered in this paper the proposed sequential algorithm is more efficient in finding the robust design solution than a one-shot space filling design.

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3

Ilhe, Paul, Francois Roueff, Eric Moulines, and Antoine Souloumiac. "Nonparametric estimation of a shot-noise process." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551709.

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4

Wang, Yejun, Tyler Paschal, and Waruna D. Kulatilaka. "Combustion Characterization of a Fuel-Flexible Piloted Liquid-Spray Flame Apparatus Using Advanced Laser Diagnostics." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91971.

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Abstract Primary energy sources for aviation gas turbines as well as direct-injection gasoline and diesel engines come in the form of liquid hydrocarbon fuels. These liquid fuels are atomized and mixed with air, prior to highly turbulent combustion and heat release processes inside engine hardware. Designing more efficient and cleaner gas turbine engines is hence dependent on the in-depth understanding of spray formation, mixing, heat release, combustion dynamics, and pollutant formation pathways in liquid-fuel spray flames. As compared to gaseous fuels, the additional steps of atomization, dispersion, and evaporation prior to turbulent mixing need to be considered for a variety of liquid fuels to enable fuel-flexible operation of these combustion hardware. Such studies can be largely facilitated by advanced laser diagnostics applied to simplified piloted liquid-spray flame configurations that can also be numerically modeled using well-defined boundary conditions. In this work, a modified configuration of a fuel-flexible piloted liquid-spray flame apparatus is used for detailed laser diagnostics studies using hydroxyl (OH) planar imaging. The configuration consists of a modified McKenna flat-flame burner fitted with a direct-injection high-efficiency nebulizer. OH radical is a primary marker of the reaction zone and a key indicator of the heat release process in flames. OH is abundant in the high-temperature combustion regions providing high signal-to-noise ratio single-laser-shot images revealing flame dynamics and instabilities. Therefore, OH planar laser-induced fluorescence (PLIF) is employed to characterize the dynamic structures of a range of piloted liquid-spray flames operated with methanol (CH3OH), n-Heptane (C7H16), iso-Octane (C8H18), dodecane (C12H26), gasoline (C4–C12), diesel (C12–C20), and kerosene (C6–C16). Single-shot and averaged OH-PLIF images show the presence of strong turbulence in the core region above the surface of the McKenna burner. The reaction zone mainly occurs around the periphery of this region, then it spreads more uniformly due to evaporation of liquid droplets downstream of the spray flame. Two-color OH PLIF thermometry in liquid spray flames operated with gasoline, diesel and kerosene, has been shown that the combustion temperature is in the range of 1200–2000 K. Overall, OH PLIF has been demonstrated to be an efficient approach for dynamic structures and temperature measurements in piloted liquid-spray flames operated with realistic fuels.

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5

Jenkins, Nicholas, Michael Tanksalvala, Yuka Esashi, Henry C. Kapteyn, and Margaret M. Murnane. "Towards shot-noise-limited EUV reflectometry: in a tabletop coherent EUV microscope." In Metrology, Inspection, and Process Control XXXVI, edited by John C. Robinson and Matthew J. Sendelbach. SPIE, 2022. http://dx.doi.org/10.1117/12.2612098.

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6

Kruidhof, Rik, Bastiaan Florijn, Wouter D. Koek, Stefan M. Bäumer, and Hamed Sadeghian. "Shot-noise limited throughput of soft x-ray ptychography for nanometrology applications." In Metrology, Inspection, and Process Control for Microlithography XXXII, edited by Ofer Adan and Vladimir A. Ukraintsev. SPIE, 2018. http://dx.doi.org/10.1117/12.2306488.

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7

Kim, Jong U., and Laszlo B. Kish. "Error rate in current-controlled logic processors with shot noise." In Second International Symposium on Fluctuations and Noise, edited by Janusz M. Smulko, Yaroslav Blanter, Mark I. Dykman, and Laszlo B. Kish. SPIE, 2004. http://dx.doi.org/10.1117/12.564330.

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8

Howard, R. M. "On the zero crossings of a generalized shot noise process." In 2011 21st International Conference on Noise and Fluctuations (ICNF). IEEE, 2011. http://dx.doi.org/10.1109/icnf.2011.5994290.

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9

Lorusso, Gian F., Rispens Gijsbert, Vito Rutigliani, Frieda Van Roey, Andreas Frommhold, and Guido Schiffelers. "Roughness decomposition: an on-wafer methodology to discriminate mask, metrology, and shot noise contributions." In Metrology, Inspection, and Process Control for Microlithography XXXIII, edited by Ofer Adan and Vladimir A. Ukraintsev. SPIE, 2019. http://dx.doi.org/10.1117/12.2515175.

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10

Han, Yoonseon, Taeyeol Jeong, Jae-Hyoung Yoo, and James Won-Ki Hong. "FLAME: Flow level traffic matrix estimation using poisson shot-noise process for SDN." In 2016 IEEE NetSoft Conference and Workshops (NetSoft). IEEE, 2016. http://dx.doi.org/10.1109/netsoft.2016.7502453.

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Звіти організацій з теми "Shot-noise processes"

1

Singpurwalla, Nozer D., and Mark A. Youngren. Multivariate Life Distributions Induced by Shot-Noise Process Environments,. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada293913.

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2

Hsing, Tailen. On the Intensity of Crossings by a Shot Noise Process. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada177077.

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