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Reducing Line Loss01:18

Reducing Line Loss

156
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
156
Weighted Mean00:57

Weighted Mean

5.2K
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.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
Aggregates Classification01:29

Aggregates Classification

329
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...
329
Survival Tree01:19

Survival Tree

89
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
89
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

540
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
540
Line Loss01:10

Line Loss

250
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
250

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Related Experiment Video

Updated: Jul 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification.

Zehua Du, Hao Zhang, Zhiqiang Wei

    IEEE Transactions on Neural Networks and Learning Systems
    |October 11, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to improve classification models dealing with imbalanced datasets. The novel approach ensures balanced performance across all classes, outperforming existing techniques.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Real-world classification tasks often involve imbalanced datasets, where sample distribution is disproportionate.
    • Existing classification methods struggle to achieve comprehensive model performance on imbalanced data.

    Purpose of the Study:

    • To propose a novel theoretical framework for imbalanced classification.
    • To develop a general merge loss calculation method that is independent of class distribution.
    • To enhance model performance on imbalanced datasets.

    Main Methods:

    • Established a proportion coefficient independent of sample number distribution.
    • Developed a general merge loss calculation method independent of class distribution.
    • Introduced true-positive rate (TPR) and false-positive rate (FPR) into loss function calculation for class balance.
    • Generated global and local loss weight coefficients for multiclass classification.
    • Calculated a merge weight loss function after unifying coefficient scales.

    Main Results:

    • The proposed loss function demonstrated better performance on imbalanced datasets compared to state-of-the-art methods.
    • The method was successfully applied to various neural network models and datasets.
    • Achieved independence and balance in loss calculation for each class.

    Conclusions:

    • The novel framework and merge loss function effectively address the challenges of imbalanced classification.
    • The proposed method offers a significant improvement over existing techniques for imbalanced datasets.
    • The approach is versatile and applicable across different models and datasets.