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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Electron Configurations02:46

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Electron configurations and orbital diagrams can be determined by applying the Aufbau principle (each added electron occupies the subshell of lowest energy available), Pauli exclusion principle (no two electrons can have the same set of four quantum numbers), and Hund’s rule of maximum multiplicity (whenever possible, electrons retain unpaired spins in degenerate orbitals).
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The alkali metal sodium (atomic number 11) has one more electron than the neon atom. This electron must go into the lowest-energy subshell available, the 3s orbital, giving a 1s22s22p63s1 configuration. The electrons occupying the outermost shell orbital(s) (highest value of n) are called valence electrons, and those occupying the inner shell orbitals are called core electrons. Since the core electron shells correspond to noble gas electron configurations, we can abbreviate electron...
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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
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Related Experiment Video

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Municipal solid waste incineration state recognition system based on deep convolutional stochastic configuration

Jiankang Yang1, Weitao Li1, Jian Tang2

  • 1School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.

Waste Management (New York, N.Y.)
|January 30, 2026
PubMed
Summary

This study introduces a deep convolutional stochastic configuration machine (DCSCM) for recognizing municipal solid waste incineration (MSWI) combustion states. The novel system achieves high accuracy, improving control and reducing emissions.

Keywords:
Deep convolutional stochastic configuration machineMunicipal solid waste incinerationSelf-optimizing modelState recognition

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence
  • Combustion Science

Background:

  • Municipal solid waste incineration (MSWI) faces combustion instability due to waste composition variability.
  • Accurate monitoring of combustion states is crucial for efficient operation and emission control.

Purpose of the Study:

  • To develop an intelligent state recognition system for MSWI combustion.
  • To enhance the precision of combustion parameter control and reduce pollutant emissions.

Main Methods:

  • A deep convolutional stochastic configuration machine (DCSCM) was developed for combustion state recognition.
  • The system integrated high-temperature cameras, industrial control hardware, and a server.
  • DCSCM incorporated expert knowledge, adaptive optimization, and error feedback for dynamic model construction.

Main Results:

  • The trained DCSCM achieved a high recognition accuracy of 97.32%.
  • The system demonstrated operational deployment and self-optimization, improving average accuracy by 1.20%.
  • The model achieved a compact parameter size of 376 KB.

Conclusions:

  • The DCSCM-based system effectively recognizes MSWI combustion states.
  • The technology supports precise combustion control, automated monitoring, and reduced environmental impact.
  • This approach offers a viable solution for optimizing MSWI processes.