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Reducing Line Loss01:18

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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.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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A Novel Twin Support-Vector Machine With Pinball Loss.

Yitian Xu, Zhiji Yang, Xianli Pan

    IEEE Transactions on Neural Networks and Learning Systems
    |January 15, 2016
    PubMed
    Summary

    This study introduces a novel Twin Support Vector Machine (TSVM) using pinball loss, enhancing performance and noise insensitivity for large datasets. The new Pin-TSVM offers improved stability and accuracy compared to traditional methods.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Pattern Recognition

    Background:

    • Traditional Twin Support Vector Machines (TSVM) offer faster classification than standard SVMs, especially for large datasets, by solving two smaller quadratic programming problems.
    • However, TSVM's reliance on hinge loss makes it sensitive to noisy data and unstable under resampling.

    Purpose of the Study:

    • To develop a more robust and noise-insensitive TSVM algorithm.
    • To enhance the stability and performance of TSVM for classification tasks.

    Main Methods:

    • Proposed a novel Twin Support Vector Machine with pinball loss (Pin-TSVM).
    • Investigated Pin-TSVM properties: noise insensitivity, between-class distance maximization, and within-class scatter minimization.
    • Conducted theoretical comparisons with twin parametric-margin SVM and SVM with pinball loss.

    Main Results:

    • Pin-TSVM demonstrates reduced sensitivity to noise points compared to traditional TSVM.
    • Numerical experiments on synthetic and benchmark datasets confirm the feasibility and validity of Pin-TSVM.
    • The proposed method shows improved performance in the presence of varying noise levels.

    Conclusions:

    • The novel Pin-TSVM effectively addresses the noise sensitivity and instability issues of traditional TSVM.
    • Pin-TSVM offers a more robust alternative for classification tasks, particularly with noisy datasets.
    • The method's ability to maximize between-class distance and minimize within-class scatter contributes to its enhanced performance.