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Efficient seismic response data storage and transmission using ARX model-based sensor data compression algorithm

机译:使用基于ARX模型的传感器数据压缩算法进行有效的地震响应数据存储和传输

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摘要

This paper presents a linear predictor (LP)-based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto-Regressive with exogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data.
机译:本文提出了一种基于线性预测器(LP)的无损传感器数据压缩算法,用于有效传输,存储和检索地震数据。选择具有外部输入的自回归(ARX)模型作为LP的模型结构。由于地震地震动通常是在受监视结构的基础上测量的,因此ARX模型参数是在系统识别框架中使用传感器网络数据和测量的输入信号进行计算的。以此方式,传感器数据压缩利用结构系统信息来最大化传感器数据压缩性能。数值仿真结果表明,LP阶数,测量噪声,输入和有限的传感器数量等因素影响所提出的无损传感器数据压缩算法的性能。通常,无损数据压缩算法能够减小原始传感器数据的大小,同时在传感器数据中不造成任何信息丢失。

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