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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Related Experiment Videos

Sampled-Data Consensus of Linear Multi-agent Systems With Packet Losses.

Wenbing Zhang1, Yang Tang2, Tingwen Huang3

  • 1Department of Mathematics, Yangzhou University, Yangzhou, China.

IEEE Transactions on Neural Networks and Learning Systems
|August 20, 2016
PubMed
Summary
This summary is machine-generated.

This study addresses consensus in multi-agent systems facing data sampling and packet losses. It develops criteria for achieving consensus under random and deterministic packet loss conditions, enhancing system reliability.

Keywords:
Control systemsEigenvalues and eigenfunctionsLaplace equationsMulti-agent systemsPacket lossSwitched systems

Related Experiment Videos

Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Distributed Computing

Background:

  • Multi-agent systems (MAS) are crucial for distributed tasks.
  • Achieving consensus in MAS is challenging due to data sampling and packet losses.
  • Existing methods often struggle with stochastic and deterministic packet loss scenarios.

Purpose of the Study:

  • To derive consensus criteria for linear MAS with sampled data and packet losses.
  • To develop a distributed controller design method using convex optimization.
  • To analyze the impact of sampling intervals and packet loss rates on consensus.

Main Methods:

  • Utilizing the Lyapunov function approach for stability analysis.
  • Employing a decomposition method for controller design.
  • Modeling random packet losses with Bernoulli sequences and deterministic losses with switched systems.

Main Results:

  • Consensus criteria derived for systems with both random and deterministic packet losses.
  • A distributed controller design is achieved via convex optimization.
  • Explicit relationships established between sampling bounds, packet loss probabilities, and consensus conditions.

Conclusions:

  • The derived criteria reveal the impact of communication topology on consensus performance.
  • The proposed methods effectively enable linear MAS with sampled data and packet losses to reach consensus.
  • Simulation results validate the effectiveness of the developed consensus criteria and controller design.