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Related Concept Videos

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

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Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

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Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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A Lightweight and Privacy-Friendly Data Aggregation Scheme against Abnormal Data.

Jianhong Zhang1,2, Haoting Han1

  • 1School of Information Sciences and Technology, North China University of Technology, Beijing 100043, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary

This study introduces a new data aggregation scheme for smart meters that automatically filters out abnormal electricity data caused by theft or meter failure. This ensures accurate data aggregation while protecting user privacy and improving power system decision-making.

Keywords:
abnormal datadata aggregationlightweightmatrix encryptionsource

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Abnormal electricity data from theft or meter failure compromises data aggregation accuracy.
  • Inaccurate aggregation impacts user interests and power system decision-making.
  • Existing schemes lack mechanisms to handle abnormal data.

Purpose of the Study:

  • To propose a lightweight and privacy-friendly data aggregation scheme.
  • To automatically filter abnormal data during aggregation.
  • To address the challenge of inaccurate data in power systems.

Main Methods:

  • Developed a novel data aggregation scheme using lightweight matrix encryption.
  • Implemented automatic filtering of abnormal data within the aggregation process.
  • Incorporated detection of abnormal data sources.

Main Results:

  • The proposed scheme effectively aggregates valid data while filtering abnormal data.
  • Lightweight matrix encryption makes it suitable for resource-limited smart meters.
  • Security analysis confirms user data privacy protection.

Conclusions:

  • The proposed scheme offers an effective solution for accurate data aggregation in smart grids.
  • It enhances data integrity by filtering abnormal electricity data.
  • The scheme is efficient and privacy-preserving, suitable for practical deployment.