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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.
Sensors (Basel, Switzerland)
|June 10, 2023
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.
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.
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