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Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Model of random process"

1

Rudzitis, J., V. Padamans, E. Bordo, and R. Haytham. "Random process model of rough surfaces contact." Measurement Science and Technology 9, no. 7 (July 1, 1998): 1093–97. http://dx.doi.org/10.1088/0957-0233/9/7/015.

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2

Jiang, C., B. Y. Ni, N. Y. Liu, X. Han, and J. Liu. "Interval process model and non-random vibration analysis." Journal of Sound and Vibration 373 (July 2016): 104–31. http://dx.doi.org/10.1016/j.jsv.2016.03.019.

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3

Chen, Siyan, Yougui Wang, Keqiang Li, and Jinshan Wu. "Money creation process in a random redistribution model." Physica A: Statistical Mechanics and its Applications 394 (January 2014): 217–25. http://dx.doi.org/10.1016/j.physa.2013.09.036.

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4

Sanping Chen and S. Mills. "A binary Markov process model for random testing." IEEE Transactions on Software Engineering 22, no. 3 (March 1996): 218–23. http://dx.doi.org/10.1109/32.489081.

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5

Wang, Victoria, Kanak Agarwal, Sani R. Nassif, Kevin J. Nowka, and Dejan Markovic. "A Simplified Design Model for Random Process Variability." IEEE Transactions on Semiconductor Manufacturing 22, no. 1 (February 2009): 12–21. http://dx.doi.org/10.1109/tsm.2008.2011630.

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6

Fitzwater, LeRoy M., and Steven R. Winterstein. "Predicting Design Wind Turbine Loads from Limited Data: Comparing Random Process and Random Peak Models." Journal of Solar Energy Engineering 123, no. 4 (July 1, 2001): 364–71. http://dx.doi.org/10.1115/1.1409561.

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This paper considers two distinct topics that arise in reliability-based wind turbine design. First, it illustrates how general probability models can be used to predict long-term design loads from a set of limited-duration, short-term load histories. Second, it considers in detail the precise choice of probability model to be adopted, for both flap and edge bending loads in both parked and operating turbine conditions. In particular, a 3-moment random peak model and a 3- or 4-moment random process model are applied and compared. For a parked turbine, all models are found to be virtually unbiased and to notably reduce uncertainty in estimating extreme loads (e.g., by roughly 50%). For an operating turbine, however, only the random peak model is found to retain these beneficial features. This suggests the advantage of the random peak model, which appears to capture the rotating blade behavior sufficiently well to accurately predict extremes.
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7

KRIVELEVICH, MICHAEL, BENNY SUDAKOV, and DAN VILENCHIK. "On the Random Satisfiable Process." Combinatorics, Probability and Computing 18, no. 5 (September 2009): 775–801. http://dx.doi.org/10.1017/s0963548309990356.

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In this work we suggest a new model for generating random satisfiable k-CNF formulas. To generate such formulas. randomly permute all $2^k\binom{n}{k}$ possible clauses over the variables x1,. . .,xn, and starting from the empty formula, go over the clauses one by one, including each new clause as you go along if, after its addition, the formula remains satisfiable. We study the evolution of this process, namely the distribution over formulas obtained after scanning through the first m clauses (in the random permutation's order).Random processes with conditioning on a certain property being respected are widely studied in the context of graph properties. This study was pioneered by Ruciński and Wormald in 1992 for graphs with a fixed degree sequence, and also by Erdős, Suen and Winkler in 1995 for triangle-free and bipartite graphs. Since then many other graph properties have been studied, such as planarity and H-freeness. Thus our model is a natural extension of this approach to the satisfiability setting.Our main contribution is as follows. For m ≥ cn, c = c(k) a sufficiently large constant, we are able to characterize the structure of the solution space of a typical formula in this distribution. Specifically, we show that typically all satisfying assignments are essentially clustered in one cluster, and all but e−Ω(m/n)n of the variables take the same value in all satisfying assignments. We also describe a polynomial-time algorithm that finds w.h.p. a satisfying assignment for such formulas.
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8

Glushkov, A. N., M. Y. Kalinin, V. P. Litvinenko, and Y. V. Litvinenko. "Digital simulator of a random process using Markov model." Journal of Physics: Conference Series 1479 (March 2020): 012056. http://dx.doi.org/10.1088/1742-6596/1479/1/012056.

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9

Reimann, S. "Price dynamics from a simple multiplicative random process model." European Physical Journal B 56, no. 4 (April 2007): 381–94. http://dx.doi.org/10.1140/epjb/e2007-00141-4.

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RAWAL, S., and G. J. RODGERS. "MODELLING INFLATION AS A RANDOM PROCESS." International Journal of Theoretical and Applied Finance 06, no. 08 (December 2003): 821–27. http://dx.doi.org/10.1142/s0219024903002225.

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Анотація:
We introduce a model of inflation in which the prices of commodities are inflated by a random process. At each time step a price x is selected with a rate of xα and is inflated by a factor of 1/β where 0<β<1. For α=0, we obtain a general time-dependent solution, where the initial price distribution can be of any form. When α>0, in the long time limit, only the highest price inflates. For α<0, the model exhibits asymptotic scaling behavior. We also consider the effects of a time dependent β, where 0<β(t)<1, for the case α=0. We find that the price distribution approaches a steady state if β(t)-1~0 faster than 1/t.
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Дисертації з теми "Model of random process"

1

Keller, Peter, Sylvie Roelly, and Angelo Valleriani. "A quasi-random-walk to model a biological transport process." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6358/.

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Transport Molecules play a crucial role for cell viability. Amongst others, linear motors transport cargos along rope-like structures from one location of the cell to another in a stochastic fashion. Thereby each step of the motor, either forwards or backwards, bridges a fixed distance. While moving along the rope the motor can also detach and is lost. We give here a mathematical formalization of such dynamics as a random process which is an extension of Random Walks, to which we add an absorbing state to model the detachment of the motor from the rope. We derive particular properties of such processes that have not been available before. Our results include description of the maximal distance reached from the starting point and the position from which detachment takes place. Finally, we apply our theoretical results to a concrete established model of the transport molecule Kinesin V.
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2

Auret, Lidia. "Process monitoring and fault diagnosis using random forests." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5360.

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Thesis (PhD (Process Engineering))--University of Stellenbosch, 2010.
Dissertation presented for the Degree of DOCTOR OF PHILOSOPHY (Extractive Metallurgical Engineering) in the Department of Process Engineering at the University of Stellenbosch
ENGLISH ABSTRACT: Fault diagnosis is an important component of process monitoring, relevant in the greater context of developing safer, cleaner and more cost efficient processes. Data-driven unsupervised (or feature extractive) approaches to fault diagnosis exploit the many measurements available on modern plants. Certain current unsupervised approaches are hampered by their linearity assumptions, motivating the investigation of nonlinear methods. The diversity of data structures also motivates the investigation of novel feature extraction methodologies in process monitoring. Random forests are recently proposed statistical inference tools, deriving their predictive accuracy from the nonlinear nature of their constituent decision tree members and the power of ensembles. Random forest committees provide more than just predictions; model information on data proximities can be exploited to provide random forest features. Variable importance measures show which variables are closely associated with a chosen response variable, while partial dependencies indicate the relation of important variables to said response variable. The purpose of this study was therefore to investigate the feasibility of a new unsupervised method based on random forests as a potentially viable contender in the process monitoring statistical tool family. The hypothesis investigated was that unsupervised process monitoring and fault diagnosis can be improved by using features extracted from data with random forests, with further interpretation of fault conditions aided by random forest tools. The experimental results presented in this work support this hypothesis. An initial study was performed to assess the quality of random forest features. Random forest features were shown to be generally difficult to interpret in terms of geometry present in the original variable space. Random forest mapping and demapping models were shown to be very accurate on training data, and to extrapolate weakly to unseen data that do not fall within regions populated by training data. Random forest feature extraction was applied to unsupervised fault diagnosis for process data, and compared to linear and nonlinear methods. Random forest results were comparable to existing techniques, with the majority of random forest detections due to variable reconstruction errors. Further investigation revealed that the residual detection success of random forests originates from the constrained responses and poor generalization artifacts of decision trees. Random forest variable importance measures and partial dependencies were incorporated in a visualization tool to allow for the interpretation of fault conditions. A dynamic change point detection application with random forests proved more successful than an existing principal component analysis-based approach, with the success of the random forest method again residing in reconstruction errors. The addition of random forest fault diagnosis and change point detection algorithms to a suite of abnormal event detection techniques is recommended. The distance-to-model diagnostic based on random forest mapping and demapping proved successful in this work, and the theoretical understanding gained supports the application of this method to further data sets.
AFRIKAANSE OPSOMMING: Foutdiagnose is ’n belangrike komponent van prosesmonitering, en is relevant binne die groter konteks van die ontwikkeling van veiliger, skoner en meer koste-effektiewe prosesse. Data-gedrewe toesigvrye of kenmerkekstraksie-benaderings tot foutdiagnose benut die vele metings wat op moderne prosesaanlegte beskikbaar is. Party van die huidige toesigvrye benaderings word deur aannames rakende liniariteit belemmer, wat as motivering dien om nie-liniêre metodes te ondersoek. Die diversiteit van datastrukture is ook verdere motivering vir ondersoek na nuwe kenmerkekstraksiemetodes in prosesmonitering. Lukrake-woude is ’n nuwe statistiese inferensie-tegniek, waarvan die akkuraatheid toegeskryf kan word aan die nie-liniêre aard van besluitnemingsboomlede en die bekwaamheid van ensembles. Lukrake-woudkomitees verskaf meer as net voorspellings; modelinligting oor datapuntnabyheid kan benut word om lukrakewoudkenmerke te verskaf. Metingbelangrikheidsaanduiers wys watter metings in ’n noue verhouding met ’n gekose uitsetveranderlike verkeer, terwyl parsiële afhanklikhede aandui wat die verhouding van ’n belangrike meting tot die gekose uitsetveranderlike is. Die doel van hierdie studie was dus om die uitvoerbaarheid van ’n nuwe toesigvrye metode vir prosesmonitering gebaseer op lukrake-woude te ondersoek. Die ondersoekte hipotese lui: toesigvrye prosesmonitering en foutdiagnose kan verbeter word deur kenmerke te gebruik wat met lukrake-woude geëkstraheer is, waar die verdere interpretasie van foutkondisies deur addisionele lukrake-woude-tegnieke bygestaan word. Eksperimentele resultate wat in hierdie werkstuk voorgelê is, ondersteun hierdie hipotese. ’n Intreestudie is gedoen om die gehalte van lukrake-woudkenmerke te assesseer. Daar is bevind dat dit moeilik is om lukrake-woudkenmerke in terme van die geometrie van die oorspronklike metingspasie te interpreteer. Verder is daar bevind dat lukrake-woudkartering en -dekartering baie akkuraat is vir opleidingsdata, maar dat dit swak ekstrapolasie-eienskappe toon vir ongesiene data wat in gebiede buite dié van die opleidingsdata val. Lukrake-woudkenmerkekstraksie is in toesigvrye-foutdiagnose vir gestadigde-toestandprosesse toegepas, en is met liniêre en nie-liniêre metodes vergelyk. Resultate met lukrake-woude is vergelykbaar met dié van bestaande metodes, en die meerderheid lukrake-woudopsporings is aan metingrekonstruksiefoute toe te skryf. Verdere ondersoek het getoon dat die sukses van res-opsporing op die beperkte uitsetwaardes en swak veralgemenende eienskappe van besluitnemingsbome berus. Lukrake-woude-metingbelangrikheidsaanduiers en parsiële afhanklikhede is ingelyf in ’n visualiseringstegniek wat vir die interpretasie van foutkondisies voorsiening maak. ’n Dinamiese aanwending van veranderingspuntopsporing met lukrake-woude is as meer suksesvol bewys as ’n bestaande metode gebaseer op hoofkomponentanalise. Die sukses van die lukrake-woudmetode is weereens aan rekonstruksie-reswaardes toe te skryf. ’n Voorstel wat na aanleiding van hierde studie gemaak is, is dat die lukrake-woudveranderingspunt- en foutopsporingsmetodes by ’n soortgelyke stel metodes gevoeg kan word. Daar is in hierdie werk bevind dat die afstand-vanaf-modeldiagnostiek gebaseer op lukrake-woudkartering en -dekartering suksesvol is vir foutopsporing. Die teoretiese begrippe wat ontsluier is, ondersteun die toepassing van hierdie metodes op verdere datastelle.
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3

Li, Zheng. "Approximation to random process by wavelet basis." View abstract/electronic edition; access limited to Brown University users, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3318378.

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4

Gupta, Resmi. "Flexible Multivariate Joint Model of Longitudinal Intensity and Binary Process for Medical Monitoring of Frequently Collected Data." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561393989215645.

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5

Mardoukhi, Yousof [Verfasser], and Ralf [Akademischer Betreuer] Metzler. "Random environments and the percolation model : non-dissipative fluctuations of random walk process on finite size clusters / Yousof Mardoukhi ; Betreuer: Ralf Metzler." Potsdam : Universität Potsdam, 2020. http://d-nb.info/1219580082/34.

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6

Sarnoff, Tamar Jill. "METAPHOR, COGNITIVE ELABORATION AND PERSUASION." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/194626.

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Metaphors have long been a subject of interest to philosophers, scholars and researchers. Recent insights into the nature and function of metaphor have spurred new interest in the persuasive effects of metaphor. To date, research on the relation between metaphors and attitudes has produced mixed findings. This paper argues that there are several limitations in previous models and designs and this work attempted to resolve several of them. The rationale for the study is based on the Elaboration Likelihood Model (ELM) of persuasion, which argues that cognitive elaboration is a strong predictor of attitudes. Researchers have posited that metaphors should evoke more cognitive elaboration than literal counterparts. This paper reports the results of a study that tested the relationship between metaphors, cognitive elaboration, and attitudes. Participants were exposed to one of 72 message conditions and responded to a set of psychological and attitude scales. Many of the hypotheses were not supported, including tests of the amount of cognitive effort that subjects reported and results related to attitude change by metaphor type. Results indicated that attitudes were stable across time, which is consistent with the ELM.
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7

Shykula, Mykola. "Quantization of Random Processes and Related Statistical Problems." Doctoral thesis, Umeå : Department of Mathematics and Mathematical Statistics, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-883.

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8

Lutsyk, Nadiia. "Modeling and methods of biomechanical heart signals processing using the conditional cyclic random process." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22726.

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Ce travail a été réalisé en cotutelle entre l'Université Nationale de Technologie de Ternopil Ivan Pul'uj (TNTU, Ukraine) et l’Université Blaise Pascal (France). Il appartient au domaine scientifique de la biomécanique et de l'informatique. Le but de l'étude est de développer les modèles et les méthodes de traitement des signaux biomécaniques cardiaques par les systèmes de diagnostic assisté par ordinateur avec une précision accrue, informativité et de la complexité de calcul inférieure. La méthode d'analyse statistique du rythme cardiaque a été mise au point. Cette méthode possède une plus grande précision et informativité par rapport aux méthodes connues d'analyse du rythme cardiaque. Dans cette thèse, le logiciel existant de l'analyse des signaux cardiaques biomécaniques a été améliorée par l'ajout de nouveaux modules logiciels, qui mettent en œuvre les nouvelles méthodes de l'analyse du rythme cardiaque et de l'analyse morphologique des signaux cardiaques biomécaniques
This work has been performed under the co-tutelle agreement between Ternopil Ivan Pul’uj National Technical University in Ternopil (TNTU, Ukraine) and the University Blaise Pascal in Clermont-Ferrand (France). It belongs to the scientific field of biomechanics and informatics. The aim of the study is to develop the mathematical models and methods of the processing of biomechanical heart signals in computer-based diagnostic systems with increased accuracy, informativeness and lower computational complexity. The method of statistical analysis of heart rhythm was developed, which is characterised by higher accuracy and informativeness compared with the known methods of heart rhythm analysis. In this thesis, the existing software of the analysis of biomechanical heart signals was improved by means of adding new software modules that implement the new methods of the analysis of heart rhythm and morphologic analysis of biomechanical heart signals
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9

Erich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.

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Петранова, Марина Юрiївна. "Випадковi гауссовi процеси зi стiйкими кореляцiйними функцiями". Doctoral thesis, Київ, 2021. https://ela.kpi.ua/handle/123456789/40592.

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Робота виконана на кафедрi прикладної математики Донецького нацiонального унiверситету iменi Василя Стуса Мiнiстерства освiти i науки України
иссертационная работа посвящена изучению случайных гауссо вых процессов с устойчивыми корреляционными функциями и их свойств.
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Книги з теми "Model of random process"

1

Schreiber, Sebastian J. Urn models, replicator process and random genetic drift. [Philadelphia, Pa.]: Society for Industrial and Applied Mathematics, 2001.

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2

Bleher, Pavel. Random matrices and the six-vertex model. Providence, Rhode Island, USA: American Mathematical Society, 2014.

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3

Srivastava, M. S. Economical on-line quality control procedures based on normal random walk model with measurement error. Toronto, Ont: University of Toronto, Dept. of Statistics, 1993.

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4

Srivastava, M. S. Economical quality control procedures based on integrated moving average process of order one. Toronto: University of Toronto, Dept. of Statistics, 1993.

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5

Gaver, Donald Paul. Random parameter Markov population process models and their likelihood, Bayes, and empirical Bayes analysis. Monterey, Calif: Naval Postgraduate School, 1985.

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6

Duflo, Marie. Random iterative models. Berlin: Springer, 1997.

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7

Random field models in earth sciences. Mineola, N.Y: Dover Publications, 2005.

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8

Random field models in earth sciences. San Diego: Academic Press, 1992.

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9

Grimmett, Geoffrey. The Random-Cluster Model. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-32891-9.

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10

I͡U︡, Kuznet͡s︡ov N., and Shurenkov V. M, eds. Models of random processes: Handbook for Mathematicians and Engineers. Boca Raton, Florida: CRC Press, 1996.

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Частини книг з теми "Model of random process"

1

Zhang, Wenbo, Huan Long, and Xian Xu. "Uniform Random Process Model Revisited." In Programming Languages and Systems, 388–404. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34175-6_20.

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2

Wójcik, Barbara. "Probabilistic Model of a Random Manufacturing Process." In Transactions of the Tenth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, 423–31. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-010-9913-4_53.

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3

Lv, Xiafei, and Lijun Chen. "Process Scheduling Model Based on Random Forest." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 791–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_85.

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4

Chen, Wen, HongGuang Sun, and Xicheng Li. "Fractional Diffusion Model, Anomalous Statistics and Random Process." In Fractional Derivative Modeling in Mechanics and Engineering, 115–57. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8802-7_4.

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5

Gani, J. "Random Allocation Methods in an Epidemic Model." In Stochastic Processes, 97–106. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4615-7909-0_12.

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6

Mueller, C., and R. Tribe. "A Measure-Valued Process Related to the Parabolic Anderson Model." In Seminar on Stochastic Analysis, Random Fields and Applications III, 219–27. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8209-5_15.

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7

Miyazaki, Kei, and Kazuo Shigemasu. "A Batesian Semiparametric Generalized Linear Model with Random Effects Using Dirichlet Process Priors." In Studies in Classification, Data Analysis, and Knowledge Organization, 391–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_42.

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8

Colcombet, Thomas, Nathanaël Fijalkow, and Pierre Ohlmann. "Controlling a Random Population." In Lecture Notes in Computer Science, 119–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_7.

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AbstractBertrand et al. introduced a model of parameterised systems, where each agent is represented by a finite state system, and studied the following control problem: for any number of agents, does there exist a controller able to bring all agents to a target state? They showed that the problem is decidable and EXPTIME-complete in the adversarial setting, and posed as an open problem the stochastic setting, where the agent is represented by a Markov decision process. In this paper, we show that the stochastic control problem is decidable. Our solution makes significant uses of well quasi orders, of the max-flow min-cut theorem, and of the theory of regular cost functions.
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Kesten, Harry. "Asymptotics in High Dimensions For the Fortuin-Kasteleyn Random Cluster Model." In Spatial Stochastic Processes, 57–85. Boston, MA: Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-1-4612-0451-0_4.

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Volf, Petr. "On Random Sums and Compound Process Models in Financial Mathematics." In Operations Research Proceedings, 403–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17022-5_52.

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Тези доповідей конференцій з теми "Model of random process"

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Wang, Guojiang, Shaodong Teng, and Kechang Fu. "Artificial Emotion Model Based on Random Process." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473432.

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Wang, Victoria, Kanak Agarwal, Sani Nassif, Kevin Nowka, and Dejan Markovic. "A Design Model for Random Process Variability." In 2008 9th International Symposium of Quality of Electronic Design (ISQED). IEEE, 2008. http://dx.doi.org/10.1109/isqed.2008.4479829.

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3

Fryz, Mykhailo. "Conditional linear random process and random coefficient autoregressive model for EEG analysis." In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, 2017. http://dx.doi.org/10.1109/ukrcon.2017.8100498.

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Hoenders, Bernhard J. "The (Quasi) Natural Mode Description of the Scattering Process by Dispersive Photonic Crystals." In Photonic Metamaterials: From Random to Periodic. Washington, D.C.: OSA, 2006. http://dx.doi.org/10.1364/meta.2006.thd20.

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5

Lytvynenko, Iaroslav, Serhii Lupenko, Oleg Nazarevych, Grigorii Shymchuk, and Volodymyr Hotovych. "Mathematical model of gas consumption process in the form of cyclic random process." In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2021. http://dx.doi.org/10.1109/csit52700.2021.9648621.

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6

Żyliński, Kamil, Aleksandra Korzec, Karol Winkelmann, and Jarosław Górski. "Random field model of foundations at the example of continuous footing." In 3RD NATIONAL CONFERENCE ON CURRENT AND EMERGING PROCESS TECHNOLOGIES – CONCEPT 2020. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0007811.

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7

Peng, Weiwen, Hong-Zhong Huang, Zhonglai Wang, Yuanjian Yang, and Yu Liu. "Degradation Analysis Using Inverse Gaussian Process Model With Random Effects: A Bayesian Perspective." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12884.

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The inverse Gaussian (IG) process is recently proposed as a flexible family of models for degradation modeling. This paper investigates Bayesian analysis of IG process model with random effects for degradation modeling. Novel features of Bayesian analysis are the natural manners for incorporating subjective information and pooling of random effects information between test specimens. An IG process model with random effects named as the random drift IG process model is investigated using the Bayesian method. In addition, a Bayesian χ2 goodness-of-fit test is developed for this Bayesian analysis. The applicability of the Bayesian method for degradation analysis with this IG process model is demonstrated with a classic example.
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8

Fryz, Mykhailo, and Leonid Scherbak. "Conditional linear random process as a mathematical model of radar noise." In 2011 Microwaves, Radar and Remote Sensing Symposium (MRRS). IEEE, 2011. http://dx.doi.org/10.1109/mrrs.2011.6053675.

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Goloubentsev, Alexander F., Valery M. Anikin, and Valery V. Tuchin. "Statistical model of 3D scattering medium generated by a random pulse process." In BiOS 2000 The International Symposium on Biomedical Optics, edited by Valery V. Tuchin, Joseph A. Izatt, and James G. Fujimoto. SPIE, 2000. http://dx.doi.org/10.1117/12.384166.

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Wong, C. N., W. D. Zhu, and N. A. Zheng. "A Stochastic Model for the Random Impact Series Method in Modal Testing." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42869.

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A novel stochastic model is developed to describe a random series of impacts in modal testing. The number of the force pulses in a finite time interval is modeled as a Poisson process. The force pulses in the series are assumed to have an arbitrary, deterministic shape function and random amplitudes and arrival times. A detailed stochastic analysis is undertaken and is validated by numerical simulation. The main advantages of the random impact series method are increased energy input to the test structure and improved signal-to-noise ratio. The method can also average out the slight nonlinearities that can exist in the structure and extract the linearized modal parameters. The model developed in this work can be used to describe a random series of pulses in other applications.
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Звіти організацій з теми "Model of random process"

1

Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.
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2

Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Анотація:
Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.
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3

Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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Анотація:
In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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4

Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
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Schmierer, Daniel, James Heckman, and Sergio Urzua. Testing the correlated random coefficient model. Institute for Fiscal Studies, April 2010. http://dx.doi.org/10.1920/wp.cem.2010.1010.

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Hoderlein, Stefan, Alexander Meister, and Hajo Holzmann. The triangular model with random coefficients. Institute for Fiscal Studies, June 2015. http://dx.doi.org/10.1920/wp.cem.2015.3315.

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Heckman, James, Daniel Schmierer, and Sergio Urzua. Testing the Correlated Random Coefficient Model. Cambridge, MA: National Bureau of Economic Research, October 2009. http://dx.doi.org/10.3386/w15463.

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Mayster, Penka, and Assen Tchorbadjieff. Supercritical Markov Branching Process with Random Initial Condition. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2019. http://dx.doi.org/10.7546/crabs.2019.01.03.

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Bajari, Patrick, Jeremy Fox, Kyoo il Kim, and Stephen Ryan. The Random Coefficients Logit Model Is Identified. Cambridge, MA: National Bureau of Economic Research, April 2009. http://dx.doi.org/10.3386/w14934.

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Hahn, Jinyong, Bryan S. Graham, Alexandre Poirier, and James L. Powell. A quantile correlated random coefficients panel data model. The IFS, August 2016. http://dx.doi.org/10.1920/wp.cem.2016.3414.

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