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跨领域的知识转移,以实现可持续的多元化工业物联网网络
Zhenzhen Gong1, Qimei Cui1, Wei Ni2
1National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了智能工厂的新跨领域知识传输方法,优化能源使用和数据新鲜性,用于各种能源. 这种方法改善了设备设备.
科学领域:
- 智能工厂技术 智能工厂技术
- 能源系统工程 能源系统工程
- 无线通信网络 无线通信网络
背景情况:
- 智能工厂面临着平衡能源消耗和信息新鲜性的挑战.
- 智能工厂中的设备通常使用异构的能源 (电网,绿色,混合).
- 现有的方法在不同能源组的知识转移方面存在困难.
研究的目的:
- 开发一种新的跨领域知识转移方法,以优化能源消耗和信息新鲜度.
- 为了使不同能源和运行参数的设备之间有效的知识转移.
- 为了促进自给自足的无线网络中更广泛的应用.
主要方法:
- 实施了一种新的跨领域知识转移技术.
- 考虑了三个不同能源的用例:电网,绿色和混合.
- 利用多层知识提取进行轻量级传输,避免神经网络.
主要成果:
- 实现了能源消耗和信息新鲜度之间的权衡的显著优化.
- 在不同状态和/或行动空间中证明了成功的知识转移,与主流算法不同.
- 与随机政策相比,模拟结果显示",热启动"政策的初始奖励增加了51.32%.
结论:
- 提出的方法为智能工厂的能源和信息管理提供了有效的解决方案.
- 没有神经网络的轻量级知识传输扩大了自我可持续无线网络的适用性.
- 该方法通过改进的"热启动"政策,显著提高了设备的初始性能.
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