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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Fast Reactions01:27

Fast Reactions

Fast reactions occurring in times shorter than the time needed to mix reactants pose a unique challenge for investigation. In a liquid-phase continuous-flow system, reactants A and B are swiftly pushed into the mixing chamber, where mixing occurs within 1 ms. The reaction mixture then flows through an observation tube, and one measures light absorption to determine species concentrations at various points of the tube. This method is most appropriate when relatively large volumes of reactants...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
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Published on: June 24, 2019

Non-stationary forward flux sampling.

Nils B Becker1, Rosalind J Allen, Pieter Rein ten Wolde

  • 1FOM Institute for Atomic and Molecular Physics (AMOLF), Science Park 104, 1098 XG Amsterdam, The Netherlands.

The Journal of Chemical Physics
|May 16, 2012
PubMed
Summary
This summary is machine-generated.

We developed Non-Stationary Forward Flux Sampling for efficient rare event simulation in complex systems. This method accurately estimates time-dependent properties in both stationary and non-stationary conditions.

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Area of Science:

  • Computational physics
  • Chemical kinetics
  • Stochastic processes

Background:

  • Simulating rare events in complex systems is computationally challenging.
  • Existing methods often struggle with non-stationary or non-equilibrium conditions.
  • Accurate estimation of time-dependent properties is crucial for understanding dynamic systems.

Purpose of the Study:

  • To introduce an efficient simulation method for rare events in both stationary and non-stationary stochastic systems.
  • To enable accurate estimation of time-dependent switching propensities and phase space probability densities.
  • To provide a versatile tool applicable to a wide range of dynamic systems.

Main Methods:

  • Non-Stationary Forward Flux Sampling (NSFFS) utilizes stochastic branching and pruning.
  • This approach ensures uniform sampling of trajectories in both phase space and time.
  • The method is designed for equilibrium and non-equilibrium systems, including non-Markovian and externally driven ones.

Main Results:

  • NSFFS accurately estimates time-dependent switching propensities.
  • The method provides accurate time-dependent phase space probability densities.
  • Validation was performed on a one-dimensional barrier crossing problem with exact solutions.

Conclusions:

  • Non-Stationary Forward Flux Sampling is a powerful and efficient method for rare event simulation.
  • The technique is broadly applicable to diverse stationary and non-stationary stochastic systems.
  • Demonstrated utility in modeling the time-dependent switching of a genetic toggle switch.