Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì最新文献

The paper considers the problem of interpreting the Allan deviation plot for signals from sensors polled more frequently than data are refreshed. The Allan variance is a standard tool for analysis of noise terms inevitably present in signals of inertial sensors. There exists a well-defined algorithm for its calculation both for time domain and frequency domain. Having calculated the Allan variance as a function of time (or frequency) one fetches its square root, called Allan deviation, and builds its plot in a logarithmic format. Each region of the Allan deviation plot characterizes a specific noise kind (white noise, flicker noise, random walk, etc). The plot is expected to have a well recognizable, predefined shape. However, in practice it may be that a plot obtained for real time series does not follow its textbook pattern. In this case it is unobvious how to interpret the plot and whether it is applicable or not. We observed quite untypical Allan deviation plots for signals of a magnetometer sampled too frequently, which suggested that the sample rate can be responsible for the unusual shape of the plot. Our work is aimed at analyzing the influence of the sample rate on the Allan deviation plot and evaluating the applicability of such a plot obtained for signals sampled too frequently. We reproduced experimental results by simulation and detected that the sample rate for synthesized white noise signals impacts the shape of the Allan deviation plot. The same idea was corroborated by filtering out repeated measurement points from experimentally obtained magnetometer signals. The simulation results are backed up by analytical calculations. Therefore, all the applied approaches such as simulation, filtering reading of a real sensor and analytical considerations confirmed that the shape of the Allan deviation plot depends on the signal sample rate. Moreover, we have shown that the Allan deviation plot built under these conditions is completely inapplicable unless all repeated measurement points are filtered out. Our analytical explanation of this fact is confirmed by a set of experiments. We provide a detailed description of a procedure for evaluation of the applicability of the Allan deviation plot using a magnetometer.

本文考虑了轮询频率大于数据刷新频率的传感器信号的Allan偏差图解释问题。Allan方差是分析惯性传感器信号中不可避免存在的噪声项的标准工具。在时域和频域上都存在一种定义良好的计算算法。在计算了艾伦方差作为时间(或频率)的函数后，我们得到了它的平方根，称为艾伦偏差，并以对数格式构建了它的图。Allan偏差图的每个区域都表示一种特定的噪声类型(白噪声、闪烁噪声、随机游走等)。情节应该有一个很好识别的，预定义的形状。然而，在实践中，为实时时间序列获得的图可能不遵循其教科书模式。在这种情况下，如何解释情节以及它是否适用是不明显的。我们观察到采样频率过高的磁力计信号的非典型艾伦偏差图，这表明采样率可能是导致图不寻常形状的原因。我们的工作旨在分析采样率对Allan偏差图的影响，并评估这种图对采样频率过高的信号的适用性。通过仿真再现实验结果，发现合成白噪声信号的采样率对Allan偏差图的形状有影响。通过从实验获得的磁强计信号中过滤掉重复的测量点，证实了同样的想法。仿真结果得到了解析计算的支持。因此，所有应用的方法，如仿真、真实传感器的滤波读数和分析考虑，都证实了Allan偏差图的形状取决于信号采样率。此外，我们已经证明，在这些条件下建立的艾伦偏差图是完全不适用的，除非所有重复的测量点被过滤掉。我们对这一事实的分析解释得到了一系列实验的证实。我们提供了一个程序的详细描述，以评估适用性的艾伦偏差图使用磁力计。

{"title":"Analysis of the influence of sample rates on the Allan variance","authors":"Т. Marusenkova","doi":"10.23939/sisn2019.01.053","DOIUrl":"https://doi.org/10.23939/sisn2019.01.053","url":null,"abstract":"The paper considers the problem of interpreting the Allan deviation plot for signals from sensors polled more frequently than data are refreshed. The Allan variance is a standard tool for analysis of noise terms inevitably present in signals of inertial sensors. There exists a well-defined algorithm for its calculation both for time domain and frequency domain. Having calculated the Allan variance as a function of time (or frequency) one fetches its square root, called Allan deviation, and builds its plot in a logarithmic format. Each region of the Allan deviation plot characterizes a specific noise kind (white noise, flicker noise, random walk, etc). The plot is expected to have a well recognizable, predefined shape. However, in practice it may be that a plot obtained for real time series does not follow its textbook pattern. In this case it is unobvious how to interpret the plot and whether it is applicable or not. We observed quite untypical Allan deviation plots for signals of a magnetometer sampled too frequently, which suggested that the sample rate can be responsible for the unusual shape of the plot. Our work is aimed at analyzing the influence of the sample rate on the Allan deviation plot and evaluating the applicability of such a plot obtained for signals sampled too frequently. We reproduced experimental results by simulation and detected that the sample rate for synthesized white noise signals impacts the shape of the Allan deviation plot. The same idea was corroborated by filtering out repeated measurement points from experimentally obtained magnetometer signals. The simulation results are backed up by analytical calculations. Therefore, all the applied approaches such as simulation, filtering reading of a real sensor and analytical considerations confirmed that the shape of the Allan deviation plot depends on the signal sample rate. Moreover, we have shown that the Allan deviation plot built under these conditions is completely inapplicable unless all repeated measurement points are filtered out. Our analytical explanation of this fact is confirmed by a set of experiments. We provide a detailed description of a procedure for evaluation of the applicability of the Allan deviation plot using a magnetometer.","PeriodicalId":444399,"journal":{"name":"Vìsnik Nacìonalʹnogo unìversitetu \"Lʹvìvsʹka polìtehnìka\". Serìâ Ìnformacìjnì sistemi ta merežì","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115446074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Process discovery method for distributed software systems with web interface 基于web界面的分布式软件系统过程发现方法

Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì

Pub Date : 2019-06-10 DOI: 10.23939/sisn2019.01.070

A. Batyuk, V. Voityshyn

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀