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Reducing Line Loss

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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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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
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Related Experiment Video

Updated: Sep 23, 2025

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
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EDCompress: Energy-Aware Model Compression for Dataflows.

Zhehui Wang, Tao Luo, Rick Siow Mong Goh

    IEEE Transactions on Neural Networks and Learning Systems
    |May 13, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Energy-aware model compression is crucial for edge devices. EDCompress (EDC) optimizes convolutional neural network (CNN) deployment across diverse hardware dataflows, significantly boosting energy efficiency with minimal accuracy loss.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Hardware Architecture

    Background:

    • Edge devices require low energy consumption, cost, and size.
    • Efficient deployment of convolutional neural networks (CNNs) on edge devices necessitates energy-aware model compression.
    • Existing methods overlook hardware dataflow diversity, limiting compression effectiveness.

    Purpose of the Study:

    • To propose EDCompress (EDC), an energy-aware model compression method adaptable to various hardware dataflows.
    • To enhance energy efficiency for CNN models on diverse edge devices.
    • To address the limitations of current compression techniques by considering dataflow variations.

    Main Methods:

    • Recasting the model compression optimization as a multistep problem.
    • Employing reinforcement learning algorithms to solve the optimization problem.
    • Introducing a multidimensional multistep (MDMS) optimization method for enhanced compression capability.

    Main Results:

    • EDCompress (EDC) achieves significant energy efficiency improvements: 20x for VGG-16, 17x for MobileNet, and 26x for LeNet-5.
    • Negligible loss of accuracy was observed across tested CNN models.
    • EDC effectively identifies optimal dataflow types for specific neural networks, guiding hardware deployment.

    Conclusions:

    • EDCompress (EDC) offers a robust solution for energy-aware CNN model compression on edge devices.
    • The method's adaptability to diverse dataflows and its reinforcement learning-based optimization provide superior compression capabilities.
    • EDC facilitates efficient CNN deployment by optimizing energy consumption and guiding hardware selection.