scholarly journals What Causes the Common‐Mode Error in Array GPS Displacement Fields: Case Study for Taiwan in Relation to Atmospheric Mass Loading

2020 ◽  
Vol 7 (11) ◽  
Author(s):  
Utpal Kumar ◽  
Benjamin Fong Chao ◽  
Emmy T. Y. Chang
Keyword(s):  
Mass Loading ◽  
Displacement Fields ◽  
Common Mode ◽  
Common Mode Error ◽  
The Common ◽  
Atmospheric Mass

scholarly journals Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5408
Author(s):  
Keliang Zhang ◽  
Yuebing Wang ◽  
Weijun Gan ◽  
Shiming Liang
Keyword(s):  
Time Series ◽  
Yunnan Province ◽  
Vertical Component ◽  
Mass Loading ◽  
Gps Measurements ◽  
Local Effects ◽  
Common Mode ◽  
Common Mode Error ◽  
Grace Data ◽  
The Common

While seasonal hydrological mass loading, derived from Gravity Recovery and Climate Experiment (GRACE) measurements, shows coherent spatial patterns and is an important source for the common mode error (CME) in continuous global positioning system (cGPS) measurements in Yunnan, it is a challenge to quantify local effects and detailed changes in daily GPS measurements by using GRACE data due to its low time and spatial resolutions. In this study, we computed and compared two groups of CMEs for nine cGPS sites in the northwest Yunnan province; rCMEs were computed with the residual cGPS time series having high inter-station correlations, while oCMEs were computed with all the GPS time series. The rCMEs-filtered time series had smaller variances and larger root mean square (RMS) reductions than those that were oCMEs-filtered, and when the stations local effects were not removed, spurious transient-like signals occurred. Compared with hydrological mass loading (HYDL), its combination with non-tidal atmosphere pressure and ocean mass reached a better agreement with the CME in the vertical component, with the Nash–Sutcliffe efficiency (NSE) increasing from 0.28 to 0.55 and the RMS reduction increasing from 15.19% to 33.4%, respectively. Our results suggest that it is necessary to evaluate the inter-station correlation and remove the possible noisy stations before conducting CME filtering, and that one should carefully choose surface loading models to correct the raw cGPS time series if CME filtering is not conducted.

scholarly journals Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

2020 ◽  
Vol 12 (5) ◽  
pp. 751
Author(s):  
Weijie Tan ◽  
Junping Chen ◽  
Danan Dong ◽  
Weijing Qu ◽  
Xueqing Xu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Principal Component ◽  
Mass Loading ◽  
Common Mode ◽  
Surface Mass ◽  
Common Mode Error ◽  
Comparison Results ◽  
Wide Range ◽  
Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

The Common Mode Failure Analysis Based on Markov Model

2012 ◽  
Vol 605-607 ◽  
pp. 697-702
Author(s):  
Yue Zhao ◽  
Jian Jiao
Keyword(s):  
Markov Model ◽  
Analysis Method ◽  
System Complexity ◽  
Common Mode ◽  
Modeling And Analysis ◽  
Important Method ◽  
Analysis Process ◽  
The Common ◽  
Mode Failure

Common Mode Failure (CMF) analysis is an important method for evaluating the reliability, safety and risk of complex systems. As the increasing in the system complexity, the common-mode question has become an important factor for conditioning the reliability and security of the control system. Based the discussion on the concepts of CMF, this paper provided a CMF analysis method using Markov model, including the modeling and analysis process. A case study was also presented to verify the feasibility of the analysis method.

scholarly journals Potential Contributors to Common Mode Error in Array GPS Displacement Fields in Taiwan Island

2021 ◽  
Vol 13 (21) ◽  
pp. 4221
Author(s):  
Xiaojun Ma ◽  
Bin Liu ◽  
Wujiao Dai ◽  
Cuilin Kuang ◽  
Xuemin Xing
Keyword(s):  
Time Series ◽  
Component Analysis ◽  
Mass Loading ◽  
Common Mode ◽  
Temporal Filtering ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Common Mode Error ◽  
Taiwan Island ◽  
Spatio Temporal

The existence of the common mode error (CME) in the continuous global navigation satellite system (GNSS) coordinate time series affects geophysical studies that use GNSS observations. To understand the potential contributors of CME in GNSS networks in Taiwan and their effect on velocity estimations, we used the principal component analysis (PCA) and independent component analysis (ICA) to filter the vertical coordinate time series from 44 high-quality GNSS stations in Taiwan island in China, with a span of 10 years. The filtering effects have been evaluated and the potential causes of the CME are analyzed. The root-mean-square values decreased by approximately 14% and 17% after spatio-temporal filtering using PCA and ICA, respectively. We then discuss the relationship between the CME sources obtained by ICA and the environmental loads. The results reveal that the independent displacements extracted by ICA correlate with the atmospheric mass loading (ATML) and land water storage mass loading (LWS) of Taiwan in terms of both its amplitude and phase. We then use the white noise plus power law noise model to quantitatively estimate the noise characteristics of the pre- and post-filtered coordinate time series based on the maximum likelihood estimation criterion. The results indicate that spatio-temporal filtering reduces the amplitude of the PL and the periodic terms in the GPS time series.

Methodology for Analysis of Schottky Diode Failures

2010 ◽  
Author(s):  
Bhanu P. Sood ◽  
Michael Pecht ◽  
John Miker ◽  
Tom Wanek
Keyword(s):  
Failure Mode ◽  
Schottky Diode ◽  
Failure Modes ◽  
Schottky Diodes ◽  
Failure Mechanisms ◽  
Forward Voltage ◽  
Fast Switching ◽  
Voltage Drops ◽  
The Common

Abstract Schottky diodes are semiconductor switching devices with low forward voltage drops and very fast switching speeds. This paper provides an overview of the common failure modes in Schottky diodes and corresponding failure mechanisms associated with each failure mode. Results of material level evaluation on diodes and packages as well as manufacturing and assembly processes are analyzed to identify a set of possible failure sites with associated failure modes, mechanisms, and causes. A case study is then presented to illustrate the application of a systematic FMMEA methodology to the analysis of a specific failure in a Schottky diode package.

scholarly journals Getting information on suspended sediments in a large river from acoustic backscatter

2018 ◽  
Vol 40 ◽  
pp. 04017
Author(s):  
Adrien Vergne ◽  
Céline Berni ◽  
Jérôme Le Coz
Keyword(s):  
Suspended Sediment Concentration ◽  
Spatial Information ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Large River ◽  
Acoustic Backscatter ◽  
Qualitative Information ◽  
Sediment Size ◽  
The Common

There has been a growing interest in the last decade in extracting information on Suspended Sediment Concentration (SSC) from acoustic backscatter in rivers. Quantitative techniques are not yet effective, but acoustic backscatter already provides qualitative information on suspended sediments. In particular, in the common case of a bi-modal sediment size distribution, corrected acoustic backscatter can be used to look for sand particles in suspension and provide spatial information on their distribution throughout a river crosssection. This paper presents a case-study where these techniques have been applied.

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