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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Population Growth00:57

Population Growth

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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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一次拍摄任何场景的人群计数与本地到全球指导.

Jiwei Chen, Zengfu Wang

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    概括
    此摘要是机器生成的。

    这项研究引入了一种新的一次性学习方法,用于在各种监控场景中准确地计数人群. 该方法有效地将人群特征从单一图像中转移,提高在未见的环境中的性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能

    背景情况:

    • 由于摄像头安装的不同,人群计数模型与未见的监视场景作斗争.
    • 从任何场景使用单个注释图像进行准确的人群计数是一个重大挑战.

    研究的目的:

    • 为任何监视场景开发一个强大的人群计数方法,使用一次性学习.
    • 为了使在多样化,无注释的环境中,从有限的数据中准确地估计人群.

    主要方法:

    • 群众计数被定义为使用指标学习的一次性学习任务.
    • 多原型学习者通过预期最大化从单个支持图像估计前景和密度原型.
    • 局部特征被卷积神经网络 (CNN) 激活,全球特征使用变压器进行相关联.

    主要成果:

    • 与最先进的 (SOTA) 方法相比,提出的方法在少数射击群众计数场景中表现出更高的性能.
    • 在三个监控数据集上的实验验验证了本地到全球指导策略的有效性.

    结论:

    • 开发的一次性学习方法有效地解决了在看不见的监控场景中进行人群计数的挑战.
    • 多原型学习者和局部到全球指导机制提供了一个强大的解决方案,以有限的数据准确估计人群.