1,874 research outputs found
Continuous Order Identification of PHWR Models Under Step-back for the Design of Hyper-damped Power Tracking Controller with Enhanced Reactor Safety
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In this paper, discrete time higher integer order linear transfer function models have been identified first for a 500 MWe Pressurized Heavy Water Reactor (PHWR) which has highly nonlinear dynamical nature. Linear discrete time models of the nonlinear nuclear reactor have been identified around eight different operating points (power reduction or step-back conditions) with least square estimator (LSE) and its four variants. From the synthetic frequency domain data of these identified discrete time models, fractional order (FO) models with sampled continuous order distribution are identified for the nuclear reactor. This enables design of continuous order Proportional-Integral-Derivative (PID) like compensators in the complex w-plane for global power tracking at a wide range of operating conditions. Modeling of the PHWR is attempted with various levels of discrete commensurate-orders and the achievable accuracies are also elucidated along with the hidden issues, regarding modeling and controller design. Credible simulation studies are presented to show the effectiveness of the proposed reactor modeling and power level controller design. The controller pushes the reactor poles in higher Riemann sheets and thus makes the closed loop system hyper-damped which ensures safer reactor operation at varying dc-gain while making the power tracking temporal response slightly sluggish; but ensuring greater safety margin.This work has been supported by Department of Science and Technology (DST), Govt. of India, under the PURSE programme
A Stochastic Fractional Dynamics Model of Space-time Variability of Rain
Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment
Securing IoT communications: at what cost?
IoT systems use wireless links for local communication, where locality depends on the
transmission range and include many devices with low computational power such as sensors.
In IoT systems, security is a crucial requirement, but difficult to obtain, because standard cryptographic techniques have a cost
that is usually unaffordable.
We resort to an extended version of the process calculus LySa, called IoTLySa,
to model the patterns of communication of IoT devices.
Moreover, we assign rates to each transition
to infer quantitative measures on the specified systems.
The derived performance evaluation can be exploited to
establish the cost of the possible security countermeasures
Quantum calcium-ion interactions with EEG
Previous papers have developed a statistical mechanics of neocortical
interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated
Annealing (ASA) has been developed to perform fits to such nonlinear stochastic
systems. An N-dimensional path-integral algorithm for quantum systems,
qPATHINT, has been developed from classical PATHINT. Both fold short-time
propagators (distributions or wave functions) over long times. Previous papers
applied qPATHINT to two systems, in neocortical interactions and financial
options. \textbf{Objective}: In this paper the quantum path-integral for
Calcium ions is used to derive a closed-form analytic solution at arbitrary
time that is used to calculate interactions with classical-physics SMNI
interactions among scales. Using fits of this SMNI model to EEG data, including
these effects, will help determine if this is a reasonable approach.
\textbf{Method}: Methods of mathematical-physics for optimization and for path
integrals in classical and quantum spaces are used for this project. Studies
using supercomputer resources tested various dimensions for their scaling
limits. In this paper the quantum path-integral is used to derive a closed-form
analytic solution at arbitrary time that is used to calculate interactions with
classical-physics SMNI interactions among scales. \textbf{Results}: The
mathematical-physics and computer parts of the study are successful, in that
there is modest improvement of cost/objective functions used to fit EEG data
using these models. \textbf{Conclusion}: This project points to directions for
more detailed calculations using more EEG data and qPATHINT at each time slice
to propagate quantum calcium waves, synchronized with PATHINT propagation of
classical SMNI.Comment: published in Sc
Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces
Nonlinear non-Gaussian state-space models arise in numerous applications in
statistics and signal processing. In this context, one of the most successful
and popular approximation techniques is the Sequential Monte Carlo (SMC)
algorithm, also known as particle filtering. Nevertheless, this method tends to
be inefficient when applied to high dimensional problems. In this paper, we
focus on another class of sequential inference methods, namely the Sequential
Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising
alternative to SMC methods. After providing a unifying framework for the class
of SMCMC approaches, we propose novel efficient strategies based on the
principle of Langevin diffusion and Hamiltonian dynamics in order to cope with
the increasing number of high-dimensional applications. Simulation results show
that the proposed algorithms achieve significantly better performance compared
to existing algorithms
Fractional derivatives of random walks: Time series with long-time memory
We review statistical properties of models generated by the application of a
(positive and negative order) fractional derivative operator to a standard
random walk and show that the resulting stochastic walks display
slowly-decaying autocorrelation functions. The relation between these
correlated walks and the well-known fractionally integrated autoregressive
(FIGARCH) models, commonly used in econometric studies, is discussed. The
application of correlated random walks to simulate empirical financial times
series is considered and compared with the predictions from FIGARCH and the
simpler FIARCH processes. A comparison with empirical data is performed.Comment: 10 pages, 14 figure
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