11,448 Hits in 11.7 sec

Performance characterization and regulatory feedback control design for time-invariant discrete-time stochastic processes

Michael Gregory Forbes
With motivations from the processing and manufacturing industries, this thesis investigates the relationship between the dynamics and stationary probability density function (PDF) for a class of time-invariant  ...  For stationary stochastic processes, long-term behaviour is concisely summarized by the stationary PDF and quantities derived from the PDF, such as mean and variance, are often key targets for process  ...  This thesis investigates the relationship between the dynamics and the stationary probability density function (PDF) for a class of time-invariant discrete-time stochastic nonlinear process models.  ... 
doi:10.7939/r3-ysp8-kc91 fatcat:vwxs6ufqwrhbbl4kjlntulzr54

Neural PID Control Strategy for Networked Process Control

Jianhua Zhang, Junghui Chen
2013 Mathematical Problems in Engineering  
To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control  ...  The convergence in the mean square sense is analysed for closed-loop networked control systems.  ...  controller design is to determine control parameters that minimize the shape of error PDF, for the output over NCS is stochastic.  ... 
doi:10.1155/2013/752489 fatcat:3fheksy5oresrl2alwcsiq3mm4

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling [chapter]

Olivia Angelin-Bonnet, Patrick J. Biggs, Matthieu Vignes
2018 Msphere  
We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay.  ...  We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.  ...  We would like to thanks reviewers of this chapter for their useful comments. MV was partly supported by a visiting professor scholarship from Aix-Marseille University.  ... 
doi:10.1007/978-1-4939-8882-2_15 fatcat:h4o2e4uf2bbtbj733a3mifiicy

Research issues in genomic signal processing

E.R. Dougherty, A. Datta, C. Sima
2005 IEEE Signal Processing Magazine  
Since the network complexity is explicitly penalized in the cost function during the inference process itself, the designed network cannot be too complex.  ...  The Boolean functions represent the rules of regulatory interaction between genes.  ... 
doi:10.1109/msp.2005.1550189 fatcat:wmbkm5i6lraifperqjytwyrmd4

Multi-objective minimum entropy controller design for stochastic processes

P Afshar, A Nobakhti, Hong Wang, Tianyou Chai
2010 Proceedings of the 2010 American Control Conference  
In these cases, the Entropy is proposed as a generalisation of the variance measure and the control objective becomes that of minimising the Entropy.  ...  In these cases, as it is not possible to directly control the actual value of the system variables, one aims to reduce the variations instead.  ...  ( ) is the probability distribution function (PDF) of the tracking error.  ... 
doi:10.1109/acc.2010.5530823 fatcat:u4224gkukzea5mane3ghb66rtu

Simulating non-Markovian stochastic processes

Marian Boguñá, Luis F. Lafuerza, Raúl Toral, M. Ángeles Serrano
2014 Physical Review E  
We give the exact analytical solution and a practical an efficient algorithm alike the Gillespie algorithm for Markovian processes, with the difference that now the occurrence rates of the events depend  ...  Strikingly, our results unveil the drastic effects that very subtle differences in the modeling of non-Markovian processes have on the global behavior of complex systems, with important implications for  ...  This probability density can be expressed as [30] ψ i (τ |t i ) = ψ i (τ + t i ) i (t i ) , (2) where i (τ ) is the survival probability of process i, that is, the probability that the time until the  ... 
doi:10.1103/physreve.90.042108 pmid:25375439 fatcat:ac7bjka3m5budkbqjnrthx3jyu


Valter Melkumyan, Tetiana Maliutenko, Ivan Ostroumov
2015 Vìsnik Nacìonalʹnogo Avìacìjnogo Unìversitetu  
The article explores some aspects of the performance management of designed complex polyergatic service technological systems.  ...  Dynamic state of a service technological system is considered in the context of estimation, decision making and realization.  ...  τ+ τ τ+ τ+ ∫ ∫ ∫ Ωτ Ω Ωτ+ τ+ − where ω n -n-dimensional probability density function of statistic sample; Ω τ ...  ... 
doi:10.18372/2306-1472.62.7767 fatcat:rbq77cbzlnfdzbvoe3wnztbbse

Scaling Methods of Sediment Bioremediation Processes and Applications

P. Adriaens, M.-Y. Li, A. M. Michalak
2006 Engineering in Life Sciences  
for the implementation of in-place strategies.  ...  Geostatistics has been used for the characterization of multi-scale spatial patterns for the last few decades, and the analysis of microbial attributes has shown significant spatial structures on microbial  ...  Acknowledgements The authors acknowledge support from the DOD/DOE/ EPA Strategic Environmental Research and Development Program (SERDP) through a grant awarded to P.A.  ... 
doi:10.1002/elsc.200520127 fatcat:45dgbb4u3rh7hadyrmria6zs7i

miRNA Processing: Dicer-1 Meets Its Match

2005 PLoS Biology  
Drake calls this variable density-dependent demographic stochasticity.  ...  Zamore's group looked for genes resembling other dsRBD-encoding genes, while the Siomi lab did a functional screen for new genes specifi cally implicated in miRNA processing.  ... 
doi:10.1371/journal.pbio.0030244 fatcat:mnm23rmj65aflffq5efjyzzf4i

Mechanochemical models of processive molecular motors

Ganhui Lan, Sean X. Sun
2012 Molecular Physics  
The linker domains of the motor dimer again play a regulatory role in determining motor processive dynamics.  ...  Two essentially equivalent approaches are the Fokker-Planck equation where one solves for a time-dependent probability distribution, and the Langevin equation where one solves for a stochastic trajectory  ...  Having defined the geometrical shape of the molecule, the free energy, E, as a function of change in shape is then defined as where V is the atomic interaction potential between atoms.  ... 
doi:10.1080/00268976.2012.677863 fatcat:nuyavovdvra4dm45howutu2swy

Modelling of Mammalian Cells and Cell Culture Processes

F.R. Sidoli, A. Mantalaris, S.P. Asprey
2004 Cytotechnology (Dordrecht)  
, which can be used for simulation, optimisation, and control purposes would contribute to efforts to increase productivity and control product quality.  ...  Furthermore, with mammalian cell technology dependent on experiments for information, model-based experiment design is formally introduced, which when applied can result in the acquisition of more informative  ...  Acknowledgements The authors would like to thank the UK EPSRC for FRS' PhD studentship support.  ... 
doi:10.1023/b:cyto.0000043397.94527.84 pmid:19003227 pmcid:PMC3449502 fatcat:2cs2tzjiyvactaariwabnhyfra

Population dynamics of the yellow clam Mesodesma mactroides: recruitment variability, density-dependence and stochastic processes

M Lima, A Brazeiro, O Defeo
2000 Marine Ecology Progress Series  
A combination of (uncorrelated) stochasticity in reproductive rates and asymmetric intercohort interactions (density-dependent recruitment and density-dependent survival rates) seems to be the key process  ...  The dynamics of the deterministic skeleton was markedly influenced by the addition of a relatively small amount of stochastic variability to fertility rates.  ...  Bjørnstad and 3 anonymous reviewers for their helpful comments that improved the manuscript.  ... 
doi:10.3354/meps207097 fatcat:ol7nbcw6vnfcrirmvvzxodjala

Incorporating Computational Challenges into a Multidisciplinary Course on Stochastic Processes [article]

Mark Jayson Cortez, Alan Eric Akil, Krešimir Josić, Alexander J. Stewart
2021 arXiv   pre-print
Here we describe a course on stochastic processes in biology, delivered between September and December 2020 to a mixed audience of mathematicians and biologists.  ...  We also discuss feedback from students, and describe the content of the challenges presented in the course. We provide all materials, along with example code for a number of challenges.  ...  Acknowledgments We would like to thank the students in the course for their enthusiasm and willingness to participate in an experimental course, all during a pandemic.  ... 
arXiv:2109.04431v1 fatcat:ywrl4qwegzbdviadozpe7w7qne

A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters [article]

Sim Kuan Goh, Narendra Pratap Singh, Zhi Jun Lim, Sameer Alam
2021 arXiv   pre-print
TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates.  ...  These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers' displays during the approach and landing procedures, enabling  ...  Given the probability density functions (PDF) approximated by GMM, mGMM, and TGP, we can compute the V.  ... 
arXiv:2011.09335v3 fatcat:l7rrl52ss5erfbwef5bk57spmu

Deep Hawkes Process for High-Frequency Market Making [article]

Pankaj Kumar
2021 arXiv   pre-print
The experimental results show that our trading agent outperforms the baseline strategy, which uses a probability density estimate of the fundamental price.  ...  We design realistic simulations of limit order markets and develop a high-frequency market making strategy in which agents process order book information to post the optimal price, order type and execution  ...  The derivative of the earlier gives probability density function (PDF) F ∆t = f ∆t , empirically estimated for normalized simulated return, as illustrated in Figure 9a .  ... 
arXiv:2109.15110v1 fatcat:akkcpm3psbautdaj66wlb5jvk4
« Previous Showing results 1 — 15 out of 11,448 results