Information criterion for determination time window length of dynamic PCA for process monitoring

2003 ◽  
pp. 461-466
Author(s):  
Xiuxi Li ◽  
Yu Qian ◽  
Junfeng Wang ◽  
S Joe Qin
Keyword(s):  
Process Monitoring ◽  
Time Window ◽  
Information Criterion ◽  
Window Length

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

2012 ◽  
Author(s):  
Marco Grasso ◽  
Bianca Maria Colosimo ◽  
Giovanni Moroni
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Reference Model ◽  
Time Window ◽  
End Milling ◽  
Monitoring Methods ◽  
Time Data ◽  
Machining Processes ◽  
Adaptive Approach ◽  
Adaptive Monitoring

In different manufacturing applications the assessment of the health conditions of a machine tool, together with the quality and stability of the process, requires the capability of dealing with response variables described in terms of profile data. In the frame of in-process monitoring of sensor signals this is the case, for instance, of monitoring either series production of large lots of parts or machining processes characterized by cyclic signals, where both the condition of the machine components and the final quality of the worked piece may be correlated with the stability of repeating signal profiles in time. However, as far as real time data acquisition is concerned, and when measurements are performed with high sampling frequency, data are likely to be auto-correlated, and hence it is of fundamental importance to develop adaptive monitoring tools robust with respect to non-steady state conditions. The paper deals with the utilization of profile monitoring approaches for in-process monitoring of manufacturing operations and investigates their applicability to the problem of monitoring auto-correlated signals. In particular Principal Component Analysis (PCA) is applied in combination with an adaptive approach based on a moving time window for continuously revise the reference model is evaluated and discussed. A real case study is used to test the performances of the method: the task is to detect tool chipping and breakage in end milling operations by means of real-time monitoring of cutting force signals. The evolution of tool wear imposes a trend in observed signals which leads to the need for an adaptive approach to properly isolate the breakage event from the slow pattern change due to wear mechanism.

Automatic Picking of Teleseismic P- and S-Phases using an Autoregressive Prediction Approach

2020 ◽  
Author(s):  
Johannes Stampa ◽  
Máté Timkó ◽  
Marcel Tesch ◽  
Thomas Meier
Keyword(s):  
Characteristic Function ◽  
Time Window ◽  
Large Data ◽  
Information Criterion ◽  
Recent Decade ◽  
Moving Window ◽  
Data Set ◽  
Arrival Times ◽  
Automatic Picking ◽  
Prediction Approach

<div> <div> <div> <p>In the recent decade, the amount of available seismological broadband data has increased steeply. Picking later arriving phases such as S-phases is difficult, and there are few manual picks available for these phases. Data sets of manual picks can also be problematic, since phase arrival picks are sensitive to the parameters of the filtering, which are often unknown, and the individual picking behavior of the analysts. This neccesitates the adoption of automatic techniques for determining teleseismic phase arrival times consistently over a large data set.</p> <p>In this work, a robust automatic picking algorithm based on autoregressive prediction in a moving window is explained. In this algorithm, a characteristic function is calculated as the autoregressive prediction error in a moving window. This characteristic function is then transformed with the Akaike-Information Criterion to obtain the phase arrival time estimate. This estimate is further improved in a second iteration of a similiar scheme in a smaller time window.</p> <p>The algorithm is applied to a global data set including AlpArray stations, covering a time period from 1995 to present, to obtain arrival times for teleseis- mic P- and S-phases. Residuals to theoretical travel times and to local averages are shown. Different methods for automatically evaluating the quality of indi- vidual picks are used, based on signal to noise ratio of the seismic trace and impulsiveness of the arrival. The picking errors are estimated by comparision with manual picks and neighboring stations as well as statistical methods. The quality evaluations suggest potential of using these automatically determined phase arrival times for a travel time tomography.</p> </div> </div> </div>

Adaptive time-window length based on online performance measurement in SSVEP-based BCIs

2015 ◽  
Vol 149 ◽  
pp. 93-99 ◽  
Author(s):  
Janir Nuno da Cruz ◽  
Feng Wan ◽  
Chi Man Wong ◽  
Teng Cao
Keyword(s):  
Performance Measurement ◽  
Time Window ◽  
Window Length ◽  
Adaptive Time

scholarly journals A study of acoustic characteristics of voluntary expiratory sounds produced before and immediately after swallowing

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shoma Hattori ◽  
Shinji Nozue ◽  
Yoshiaki Ihara ◽  
Koji Takahashi
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Time Window ◽  
Maximum Amplitude ◽  
Comparison Method ◽  
Window Length ◽  
Acoustic Characteristics ◽  
The Mean ◽  
Time Waveform ◽  
The Relationship

AbstractTo evaluate the expiratory sounds produced during swallowing recorded simultaneously with videofluorographic examination of swallowing (VF) using fast Fourier transform (FFT), and to examine the relationship between dysphagia and its acoustic characteristics. A total of 348 samples of expiratory sounds were collected from 61 patients with dysphagia whose expiratory sounds were recorded during VF. The VF results were evaluated by one dentist and categorized into three groups: safe group (SG), penetration group (PG), and aspiration group (AG). The duration and maximum amplitude of expiratory sounds produced were measured as the domain characteristics on the time waveform of these sounds and compared among the groups. Time window-length appropriate for FFT and acoustic discriminate values (AD values) of SG, PG, and AG were also investigated. The groups were analyzed using analysis of variance and Scheffé's multiple comparison method. The maximum amplitude of SG was significantly smaller than those of PG and AG. The mean duration in SG (2.05 s) was significantly longer than those in PG (0.84 s) and AG (0.96 s). The AD value in SG was significantly lower than those in PG and AG. AD value detects penetration or aspiration, and can be useful in screening for dysphagia.

A first arrival picking method of microseismic data based on single time window with window length independent

2018 ◽  
Vol 22 (6) ◽  
pp. 1613-1627
Author(s):  
Tong Shen ◽  
Xianguo Tuo ◽  
Huailiang Li ◽  
Yong Liu ◽  
Wenzheng Rong
Keyword(s):  
Time Window ◽  
Window Length ◽  
Single Time ◽  
Microseismic Data ◽  
First Arrival
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