large fluctuation, which usually think grid is resistive obvious or perceptual characteristics of different. Frequency domain analysis of impedance values As can be seen from the test station area topology, in the Tai area, power line wiring is complex, with many branches, and longer. In such a high frequency, signal propagation environment can large fluctuation, which usually think large fluctuation, which usually think grid is r te i sc i ss t o if v e d if ofe b rv e in o tu . s or perceptual characteristics of different. Frequency doma in analysis of es d nn c vm al u ee s Apo s ne f mi a th t es sw ta ae r ea ori p og l o Ti a im ar ay , oa w eh r , i ne wd i r nn g sr . c o ec x mh a ye q b ancy h agn d go e . g I sn u ch av ir h gm h rt e qu y, ss i n al pm ro p nip at e nr m er n tce c es a l fn o r aq ue m ly-ti

2015 ◽  
pp. 152-153
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Signal Propagation ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Power Line ◽  
Large Fluctuation ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Perceptual Characteristics

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

High-Frequency TSV Failure Detection Method with Z Parameter

2012 ◽  
Author(s):  
Joohee Kim ◽  
Daniel H. Jung ◽  
Jonghyun Cho ◽  
Jun So Pak ◽  
Joungho Kim ◽  
...  
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Detection Method ◽  
Failure Detection ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Z Parameter ◽  
Die Stacking ◽  
Failure Types

Abstract As the TSV count increases, chip yield can be severely degraded due to failures during the TSV or die-stacking processes. This paper will present and discuss on the usage of failure masks designed to detect and differentiate failure types such as connection failure and insulator failure based on frequency-domain one point probing measurement. The failure masks are proposed on the basis of the frequency domain analysis of TSV failures with Z11 magnitudes.

scholarly journals Relevance of Frequency-Domain Analyses to Relate Shoe Cushioning, Ground Impact Forces and Running Injury Risk: A Secondary Analysis of a Randomized Trial With 800+ Recreational Runners

2021 ◽  
Vol 3 ◽  
Author(s):  
Laurent Malisoux ◽  
Paul Gette ◽  
Anne Backes ◽  
Nicolas Delattre ◽  
Jan Cabri ◽  
...  
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Injury Risk ◽  
Peak Force ◽  
Recent Trial ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Impact Forces ◽  
Impact Peak ◽  
Force Characteristics

Cushioning systems in running shoes are used assuming that ground impact forces relate to injury risk and that cushioning materials reduce these impact forces. In our recent trial, the more cushioned shoe version was associated with lower injury risk. However, vertical impact peak force was higher in participants with the Soft shoe version. The primary objective of this study was to investigate the effect of shoe cushioning on the time, magnitude and frequency characteristics of peak forces using frequency-domain analysis by comparing the two study groups from our recent trial (Hard and Soft shoe group, respectively). The secondary objective was to investigate if force characteristics are prospectively associated with the risk of running-related injury. This is a secondary analysis of a double-blinded randomized trial on shoe cushioning with a biomechanical running analysis at baseline and a 6-month follow-up on running exposure and injury. Participants (n = 848) were tested on an instrumented treadmill at their preferred running speed in their randomly allocated shoe condition. The vertical ground reaction force signal for each stance phase was decomposed into the frequency domain using the discrete Fourier transform. Both components were recomposed into the time domain using the inverse Fourier transform. An analysis of variance was used to compare force characteristics between the two study groups. Cox regression analysis was used to investigate the association between force characteristics and injury risk. Participants using the Soft shoes displayed lower impact peak force (p < 0.001, d = 0.23), longer time to peak force (p < 0.001, d = 0.25), and lower average loading rate (p < 0.001, d = 0.18) of the high frequency signal compared to those using the Hard shoes. Participants with low average and instantaneous loading rate of the high frequency signal had lower injury risk [Sub hazard rate ratio (SHR) = 0.49 and 0.55; 95% Confidence Interval (CI) = 0.25–0.97 and 0.30–0.99, respectively], and those with early occurrence of impact peak force (high frequency signal) had greater injury risk (SHR = 1.60; 95% CI = 1.05–2.53). Our findings may explain the protective effect of the Soft shoe version previously observed. The present study also demonstrates that frequency-domain analyses may provide clinically relevant impact force characteristics.Clinical Trial Registration:https://clinicaltrials.gov/, identifier: 9NCT03115437.

scholarly journals A frequency domain analysis of the error distribution from noisy high-frequency data

Biometrika ◽  
2018 ◽  
Vol 105 (2) ◽  
pp. 353-369
Author(s):  
Jinyuan Chang ◽  
Aurore Delaigle ◽  
Peter Hall ◽  
Cheng Yong Tang
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Error Distribution ◽  
High Frequency Data ◽  
Frequency Data
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