Compressive sensing with sparse reporting for energy

M. Simonov, G. Zanetto, G. Chicco
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引用次数: 1

Abstract

The Event-Driven Metering (EDM) method for electricity enabled the detection and better understanding of variable energy-transforming processes from real life. This article generalizes the above-mentioned Lebesgue-based approach by translating it to the accounting of other energy vectors in the form of gases or liquids. Given the behavioral difference between the physical forms of vectors of energy, authors made an effort to keep newly proposed method compatible with the process knowledge being extracted and shared grid-wide by the EDM method previously declined to electricity. Authors exploit the symplectic properties deriving from the energy conservation law. Authors set up new compressive and sparse sensing method by moving from the point-like data to the segment-like entities remaining valid over longer finite time intervals.
具有能量稀疏报告的压缩感知
事件驱动的电力计量(EDM)方法能够更好地检测和理解现实生活中的可变能量转换过程。本文将上述基于勒贝格的方法推广,将其转化为气体或液体形式的其他能量矢量的计算。考虑到能量矢量的物理形式之间的行为差异,作者努力使新提出的方法与之前拒绝电力的EDM方法提取和在电网范围内共享的过程知识兼容。作者利用了由能量守恒定律导出的辛性质。作者建立了一种新的压缩和稀疏感知方法,从点状数据移动到在较长有限时间间隔内保持有效的段状实体。
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