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Aggregates Classification01:29

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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.
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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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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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Maximum Size of Aggregate01:12

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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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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification.

Dong-Hyuk Yang1, Yong-Shin Kang1

  • 1Advanced Institute of Convergence Technology, Suwon 16229, Korea.

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|July 27, 2022
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Summary

This study introduces Distance- and Momentum-based Symbolic Aggregate Approximation (DM-SAX), a novel time-series representation method. DM-SAX improves upon existing techniques by considering data distribution and trends, leading to superior performance in classification tasks.

Keywords:
highly imbalanced classificationmomentumsymbolic aggregate approximationtime-series representation

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Area of Science:

  • Data Science
  • Machine Learning
  • Time Series Analysis

Background:

  • Symbolic Aggregate Approximation (SAX) is a common time-series representation method.
  • SAX primarily uses the mean value of time segments, potentially overlooking distribution and trend information.
  • Existing SAX variations have limitations in capturing comprehensive time-series characteristics.

Purpose of the Study:

  • To propose a novel time-series representation method, Distance- and Momentum-based Symbolic Aggregate Approximation (DM-SAX).
  • To enhance time-series analysis by incorporating data distribution and trend information.
  • To improve the performance of time-series classification and outlier detection.

Main Methods:

  • Developed DM-SAX, which calculates perpendicular distances from the time-axis to capture data distribution.
  • Integrated a momentum factor into DM-SAX to account for time-series trends and the direction of previous data points.
  • Evaluated DM-SAX on 29 imbalanced UCR datasets and a real-world wire cutting/crimping process dataset.

Main Results:

  • DM-SAX achieved the optimal Area Under the Curve (AUC) compared to SAX, extreme-SAX, overlap-SAX, and distance-based SAX on UCR datasets.
  • Statistical analysis confirmed significant performance improvements and ranking differences for DM-SAX.
  • DM-SAX also demonstrated optimal AUC on the real-world wire cutting and crimping process dataset.
  • The method effectively identified meaningful data points, such as outliers, within a time-series outlier detection framework.

Conclusions:

  • DM-SAX offers a superior approach to time-series representation by integrating distribution and trend information.
  • The proposed method significantly enhances classification performance, particularly on imbalanced datasets.
  • DM-SAX shows promise for various time-series analysis tasks, including outlier detection.