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Related Concept Videos

State Space to Transfer Function01:21

State Space to Transfer Function

610
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
610
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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State Space Representation01:27

State Space Representation

625
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Transfer Function to State Space01:23

Transfer Function to State Space

834
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
834
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

811
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

360
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Related Experiment Video

Updated: Feb 21, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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GENESIS - The GENEric SImulation System for Modelling State Transitions.

Matthew S Gillman1

  • 1Wolfson Institute, Queen Mary University of London, UK.

Journal of Open Research Software
|October 10, 2017
PubMed
Summary
This summary is machine-generated.

This software models state transitions using a discrete time Markov chain (DTMC) model. It generates configurable C++ code for processes with known transition probabilities, applicable to disease and environmental modeling.

Keywords:
C++Markov chainMarkov processPerldisease progressionmodellingprobabilitiesrandom number generationsimulationstate machinestate transitions

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Area of Science:

  • Computational Biology
  • Environmental Science
  • Software Engineering

Background:

  • Markov chain models are valuable for simulating systems with discrete states and probabilistic transitions.
  • Accurate modeling of disease progression and environmental changes requires flexible computational tools.

Purpose of the Study:

  • To develop a configurable software tool for implementing discrete time Markov chain (DTMC) models.
  • To generate adaptable C++ code for simulating processes with known state transition probabilities.

Main Methods:

  • The software utilizes a Perl script (genesis.pl) that reads user-defined state transition tables and configuration files.
  • It generates C++ classes using the State design pattern and a main program for simulation.
  • The model supports multiple branching and bi-directional transitions.

Main Results:

  • The software successfully models natural histories, exemplified by colorectal cancer in Mexico.
  • Generated C++ code is based on specified text file inputs, ensuring fidelity to the model.
  • The tool demonstrates high configurability and flexibility for diverse applications.

Conclusions:

  • This DTMC modeling software offers a reusable and flexible solution for simulating various processes.
  • Its applicability extends beyond disease modeling to areas like environmental modeling.
  • The software is available on the Figshare repository for broader scientific use.