Academic literature on the topic 'Model of random process'
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Journal articles on the topic "Model of random process"
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.
Full textJiang, 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.
Full textChen, 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.
Full textSanping 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.
Full textWang, 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.
Full textFitzwater, 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.
Full textKRIVELEVICH, 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.
Full textGlushkov, 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.
Full textReimann, 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.
Full textRAWAL, 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.
Full textDissertations / Theses on the topic "Model of random process"
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/.
Full textAuret, Lidia. "Process monitoring and fault diagnosis using random forests." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5360.
Full textDissertation 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.
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.
Full textGupta, 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.
Full textMardoukhi, 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.
Full textSarnoff, Tamar Jill. "METAPHOR, COGNITIVE ELABORATION AND PERSUASION." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/194626.
Full textShykula, 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.
Full textLutsyk, 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.
Full textThis 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
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.
Full textПетранова, Марина Юрiївна. "Випадковi гауссовi процеси зi стiйкими кореляцiйними функцiями." Doctoral thesis, Київ, 2021. https://ela.kpi.ua/handle/123456789/40592.
Full textиссертационная работа посвящена изучению случайных гауссо вых процессов с устойчивыми корреляционными функциями и их свойств.
Books on the topic "Model of random process"
Schreiber, Sebastian J. Urn models, replicator process and random genetic drift. [Philadelphia, Pa.]: Society for Industrial and Applied Mathematics, 2001.
Find full textBleher, Pavel. Random matrices and the six-vertex model. Providence, Rhode Island, USA: American Mathematical Society, 2014.
Find full textSrivastava, 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.
Find full textSrivastava, M. S. Economical quality control procedures based on integrated moving average process of order one. Toronto: University of Toronto, Dept. of Statistics, 1993.
Find full textGaver, Donald Paul. Random parameter Markov population process models and their likelihood, Bayes, and empirical Bayes analysis. Monterey, Calif: Naval Postgraduate School, 1985.
Find full textDuflo, Marie. Random iterative models. Berlin: Springer, 1997.
Find full textRandom field models in earth sciences. Mineola, N.Y: Dover Publications, 2005.
Find full textRandom field models in earth sciences. San Diego: Academic Press, 1992.
Find full textGrimmett, Geoffrey. The Random-Cluster Model. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-32891-9.
Full textI͡U︡, Kuznet͡s︡ov N., and Shurenkov V. M, eds. Models of random processes: Handbook for Mathematicians and Engineers. Boca Raton, Florida: CRC Press, 1996.
Find full textBook chapters on the topic "Model of random process"
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.
Full textWójcik, 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.
Full textLv, 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.
Full textChen, 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.
Full textGani, 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.
Full textMueller, 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.
Full textMiyazaki, 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.
Full textColcombet, 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.
Full textKesten, 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.
Full textVolf, 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.
Full textConference papers on the topic "Model of random process"
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.
Full textWang, 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.
Full textFryz, 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.
Full textHoenders, 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.
Full textLytvynenko, 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.
Full textŻyliński, 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.
Full textPeng, 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.
Full textFryz, 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.
Full textGoloubentsev, 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.
Full textWong, 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.
Full textReports on the topic "Model of random process"
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.
Full textSelvaraju, 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.
Full textLiu, 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.
Full textPettit, 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.
Full textSchmierer, 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.
Full textHoderlein, 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.
Full textHeckman, 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.
Full textMayster, 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.
Full textBajari, 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.
Full textHahn, 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.
Full text