通信感知一体化(Integrated Sensing and Communication,ISAC)技术允许设备在相同的硬件设备与频谱上进行雷达感知与数据通信,是6G的关键技术之一。另外,工业自动化以及智能驾驶等业务越发依赖高可靠低时延通信。因此,可以将ISAC技术与...通信感知一体化(Integrated Sensing and Communication,ISAC)技术允许设备在相同的硬件设备与频谱上进行雷达感知与数据通信,是6G的关键技术之一。另外,工业自动化以及智能驾驶等业务越发依赖高可靠低时延通信。因此,可以将ISAC技术与超可靠低时延通信(Ultra-Reliable Low Latency Communication,URLLC)技术进行融合,并联合设计雷达感知与URLLC预编码。基于此,提出了一个URLLC下的用户设备和速率最大化问题,且满足雷达感知的性能要求。为了解决该优化问题,首先利用基于二次变换的分式规划方法和连续凸近似方法处理短包容量公式,其次利用一阶泰勒展开方法处理雷达感知性能约束。仿真结果表明,所提设计能够同时满足雷达感知与URLLC要求,具有较好的性能。展开更多
Avalanches and landslides,induced by the Wenchuan Earthquake on May 12,2008,resulted in a lot of disaggregated,solid material on slopes that could be readily mobilized as source material for debris flows.Rainstorms tr...Avalanches and landslides,induced by the Wenchuan Earthquake on May 12,2008,resulted in a lot of disaggregated,solid material on slopes that could be readily mobilized as source material for debris flows.Rainstorms triggered numerous slope debris flows with great damage to highways and rivers over the subsequent two years.Slope debris flows(as opposed to channelized debris flows) are defined as phenomena in which high-concentration mixtures of debris and water flow down slopes for short distances to highways and river banks.Based on field investigations and measurements of 19 slope debris flows,their main characteristics and potential mitigation strategies were studied.High rainfall intensity is the main triggering factor.Critical rainfall intensities for simultaneous occurrence of single,several and numerous slope debris flow events were 20 mm/day,30mm/day,and 90 mm/day,respectively.Field investigations also revealed that slope debris flows consist of high concentrations of cobbles,boulders and gravel.They are two-phase debris flows.The liquid phase plays the role of lubrication instead of transporting medium.Solid particles collide with each other and consume a lot of energy.The velocities of slope debris flows are very low,and their transport distances are only several tens of meters.Slope debris flows may be controlled by construction of drainage systems and by reforestation.展开更多
随着物联网和通信技术的快速发展,现代工业装备海量运行数据被实时监测传输,推动装备服役阶段的故障预测与健康管理进入大数据时代。面对具有不确定性强、价值密度低及多源异构特点的装备运行大数据,传统浅层模型算法存在难以自主挖掘...随着物联网和通信技术的快速发展,现代工业装备海量运行数据被实时监测传输,推动装备服役阶段的故障预测与健康管理进入大数据时代。面对具有不确定性强、价值密度低及多源异构特点的装备运行大数据,传统浅层模型算法存在难以自主挖掘数据蕴含特征、对装备健康状态表征能力弱的先天不足。近年来,作为机器学习领域的研究热点,深度学习理论得到了学术界与工业界的广泛关注,相关的工业装备故障预测与健康管理(prognostics and health management,简称PHM)研究与应用层出不穷,为解决大数据背景下的故障预测与健康管理难题提供了新的思路和技术手段。为此,笔者回顾了工业装备故障预测与健康管理技术发展历程;从异常检测、故障诊断以及故障预测3个方面综述了深度学习已取得的研究成果;讨论了深度学习在当下工业装备故障预测与健康管理中的热点话题;分析了该研究方向在工程实际中面临的挑战,并探讨应对这些挑战的有效措施和未来发展趋势。展开更多
基金supported by the Ministry of Science and Technology of China (2008CB425803)the State Key Laboratory of Hydroscience and Engineering at Tsinghua University (50823005,2009-ZY-2)
文摘Avalanches and landslides,induced by the Wenchuan Earthquake on May 12,2008,resulted in a lot of disaggregated,solid material on slopes that could be readily mobilized as source material for debris flows.Rainstorms triggered numerous slope debris flows with great damage to highways and rivers over the subsequent two years.Slope debris flows(as opposed to channelized debris flows) are defined as phenomena in which high-concentration mixtures of debris and water flow down slopes for short distances to highways and river banks.Based on field investigations and measurements of 19 slope debris flows,their main characteristics and potential mitigation strategies were studied.High rainfall intensity is the main triggering factor.Critical rainfall intensities for simultaneous occurrence of single,several and numerous slope debris flow events were 20 mm/day,30mm/day,and 90 mm/day,respectively.Field investigations also revealed that slope debris flows consist of high concentrations of cobbles,boulders and gravel.They are two-phase debris flows.The liquid phase plays the role of lubrication instead of transporting medium.Solid particles collide with each other and consume a lot of energy.The velocities of slope debris flows are very low,and their transport distances are only several tens of meters.Slope debris flows may be controlled by construction of drainage systems and by reforestation.
文摘随着物联网和通信技术的快速发展,现代工业装备海量运行数据被实时监测传输,推动装备服役阶段的故障预测与健康管理进入大数据时代。面对具有不确定性强、价值密度低及多源异构特点的装备运行大数据,传统浅层模型算法存在难以自主挖掘数据蕴含特征、对装备健康状态表征能力弱的先天不足。近年来,作为机器学习领域的研究热点,深度学习理论得到了学术界与工业界的广泛关注,相关的工业装备故障预测与健康管理(prognostics and health management,简称PHM)研究与应用层出不穷,为解决大数据背景下的故障预测与健康管理难题提供了新的思路和技术手段。为此,笔者回顾了工业装备故障预测与健康管理技术发展历程;从异常检测、故障诊断以及故障预测3个方面综述了深度学习已取得的研究成果;讨论了深度学习在当下工业装备故障预测与健康管理中的热点话题;分析了该研究方向在工程实际中面临的挑战,并探讨应对这些挑战的有效措施和未来发展趋势。