增强协作边缘智能用于可解释和可转移的图像识别在6G辅助IIoT中
Chen Chen1,2, Ze Sun1,2, Jiale Zhang1,2
1Taihu Laboratory of Deepsea Technological Science, Wuxi 214082, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了6G辅助的工业物联网 (IIoT) 图像识别的协作边缘智能识别 (IRCE). IRCE 增强了可解释性,并使边缘服务器的协作学习成为可能,以改善工业AI.
科学领域:
- 人工智能的人工智能
- 计算机工程 计算机工程
- 电信 电信服务 电信服务 电信服务
背景情况:
- 工业物联网 (IIoT) 集成了互联设备和智能应用程序,与第六代蜂网络 (6G) 增强性能,用于像图像识别等低延迟,高可靠性任务.
- 当前的IIoT图像识别算法往往缺乏可解释性,并且无法利用边缘计算的协作潜力.
- 解决这些局限性对于在工业环境中推进AI至关重要.
研究的目的:
- 提出一种新的方法,即具有协作边缘的智能识别 (IRCE),以提高6G辅助的IIoT图像识别中的可解释性和可转移性.
- 通过AI决策的视觉解释,提高对IIoT系统的透明度和信任.
- 在分布式边缘服务器上实现有效的域名适应和协作智能.
主要方法:
- 在功能提取网络中,IRCE 整合了一个可解释的层,以生成可视化原型,以提高决策透明度.
- 整合了本地最大平均差异 (LMMD) 损失,以促进跨地理分布的边缘服务器的无传输学习.
- 该方法利用边缘智能进行实时性能优化,降低计算成本和增强可扩展性.
主要成果:
- 模拟显示,与传统方法相比,IRCE实现了更高的准确性,可解释性和适应性.
- 该方法在各种环境中展示了高效的操作,突出了其实际适用性.
- IRCE有效地优化实时性能,同时降低计算负载并提高可扩展性.
结论:
- 在IIoT生态系统中,IRCE代表了可扩展,可解释和可转移AI的重大进步.
- 该方法提高了对人工智能驱动的工业应用的信任和透明度.
- IRCE的功能非常适合关键的工业应用,如智能制造,远程诊断和智能运输系统.
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