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相关概念视频

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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Distance Measurements by Taping01:18

Distance Measurements by Taping

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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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多传感器数据融合在物联网环境中的Dempster-Shafer理论设置:一个改进的证据基于距离的方法.

Nour El Imane Hamda1,2, Allel Hadjali2, Mohand Lagha1

  • 1ASL, Aeronautics and Spatial Studies Institute, Blida 1 University, Blida 09000, Algeria.

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概括

这项研究引入了一种改进的Dempster-Shafer (D-S) 理论方法,用于物联网环境中的多传感器数据融合. 该方法有效地管理相互矛盾的数据,提高决策准确性和可靠性.

关键词:
德姆斯特·沙弗理论这就是为什么物联网物联网物联网.信念是的 .远距离证据 远距离证据多传感器数据融合技术不确定性是一种不确定性.

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 环境产生了大量不完美的数据,包括不确定的,相互矛盾的或不正确的信息.
  • 多传感器数据融合对于整合异质数据源和改善决策至关重要.
  • 斯特-沙弗 (D-S) 理论是处理不确定性的强有力的工具,但与高度冲突的数据作斗争.

研究的目的:

  • 为德姆斯特-沙弗 (D-S) 理论提出一个改进的证据组合方法.
  • 在物联网环境中的多传感器数据融合中有效管理冲突和不确定性.
  • 提高物联网应用中的决策准确性和可靠性.

主要方法:

  • 开发了一种改进的证据组合方法,该方法基于Hellinger距离和Deng.
  • 将该方法应用于基准目标识别示例.
  • 使用两个现实世界物联网应用案例验证了该方法:故障诊断和决策.

主要成果:

  • 与现有方法相比,拟议的方法在冲突管理和融合速度方面表现优越.
  • 实现了高准确率:目标识别99.32%,故障诊断96.14%,物联网决策99.54%.
  • 融合结果显示,可靠性增加,决策准确性提高.

结论:

  • 改进的证据结合方法有效地解决了在DS理论中结合矛盾数据的挑战.
  • 该方法显著提高了物联网应用程序的多传感器数据融合,从而导致更准确的决策.
  • 该方法为管理复杂数据环境中的不确定性和冲突提供了强大的解决方案.