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相关概念视频

Reducing Line Loss01:18

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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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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.
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...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
472
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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相关实验视频

Updated: Jul 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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轻量级的像素差异网络为高效的视觉表示学习学习.

Zhuo Su, Jiehua Zhang, Longguang Wang

    IEEE transactions on pattern analysis and machine intelligence
    |August 1, 2023
    PubMed
    概括
    此摘要是机器生成的。

    研究人员开发了像素差异网络 (PiDiNet) 和二进制PiDiNet (Bi-PiDiNet) 以实现高效的深度神经网络 (DNN). 这些模型为边缘设备在物体识别和边缘检测等任务中提供了卓越的准确性和效率.

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    相关实验视频

    Last Updated: Jul 20, 2025

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    Deep Neural Networks for Image-Based Dietary Assessment
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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 机器学习 机器学习

    背景情况:

    • 为边缘设备开发轻量级深度神经网络 (DNN) 需要平衡精度和效率.
    • 现有的方法在优化高精度和高计算效率方面面临挑战.

    研究的目的:

    • 引入新的卷积方法,像素差异卷积 (PDC) 和二进制 PDC (Bi-PDC),以创建高效的 DNN.
    • 介绍两个轻量级网络,PiDiNet和Bi-PiDiNet,它们利用PDC和Bi-PDC来提高视觉任务的性能.

    主要方法:

    • 拟议的像素差异卷积 (PDC) 和二进制PDC (Bi-PDC) 以高效地捕获更高阶的局部差异信息.
    • 开发了PiDiNet和Bi-PiDiNet,集成PDC和Bi-PDC用于边缘检测和对象识别.
    • 在BSDS500,ImageNet,LFW和YTF等数据集上进行了广泛的实验.

    主要成果:

    • 在测试的模型中,PiDiNet和Bi-PiDiNet展示了最好的准确性-效率权衡.
    • 在没有 ImageNet 预训练的情况下,PiDiNet 在 BSDS500 (100 FPS,1M参数) 上实现了人类级别的边缘检测性能.
    • Bi-PiDiNet 在二进制 DNN 之间实现了最先进的准确性,以降低计算成本进行对象识别.

    结论:

    • PDC和Bi-PDC提供了一个计算高效的方法来设计准确和轻量级的DNN.
    • PiDiNet 和 Bi-PiDiNet 在边缘计算的高效视觉表示学习方面取得了重大进展.
    • 提出的方法可以在资源有限的设备上实现高性能视觉任务.