摘要翻译：
现代智慧配电系统要求对安装在电表中的传感器产生的大数据进行存储、传输和处理。一方面，这些数据本质上是智能电网进行智能决策所必需的，但另一方面，海量数据的存储、传输和处理也是一个挑战。现有的信息压缩方法仅依赖于传统的矩阵分解技术，利用较少的主成分来表示整个数据。本文提出了一种级联数据压缩技术，将三种不同的压缩方法相结合，以获得高效的存储和传输所需的高压缩率。第一和第二阶段采用两种有损数据压缩技术，即奇异值分解(SVD)和归一化；第三阶段通过使用稀疏编码(SE)技术来实现进一步的压缩，稀疏编码是一种无损压缩技术，但仅对稀疏数据集有明显的好处。我们的仿真结果表明，在可接受的平均绝对误差(MAE)下，这三种技术的联合使用使数据压缩比在小的稀疏数据集上比现有的SVD提高了15%，在大的非稀疏数据集上比现有的SVD提高了28%。
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英文标题：
《Tri-Compress: A Cascaded Data Compression Framework for Smart
  Electricity Distribution Systems》
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作者：
Syed Muhammad Atif, Anees Ahmed, Sameer Qazi
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Modern smart distribution system requires storage, transmission and processing of big data generated by sensors installed in electric meters. On one hand, this data is essentially required for intelligent decision making by smart grid but on the other hand storage, transmission and processing of that huge amount of data is also a challenge. Present approaches to compress this information have only relied on the traditional matrix decomposition techniques benefitting from low number of principal components to represent the entire data. This paper proposes a cascaded data compression technique that blends three different methods in order to achieve high compression rate for efficient storage and transmission. In the first and second stages, two lossy data compression techniques are used, namely Singular Value Decomposition (SVD) and Normalization; Third stage achieves further compression by using the technique of Sparsity Encoding (SE) which is a lossless compression technique but only having appreciable benefits for sparse data sets. Our simulation results show that the combined use of the 3 techniques achieves data compression ratio to be 15% higher than state of the art SVD for small, sparse datasets and up to 28% higher in large, non-sparse datasets with acceptable Mean Absolute Error (MAE).
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PDF链接：
https://arxiv.org/pdf/1807.06811