Adaptive Moving Window Method for 3D P-Velocity Tomography and Its Application in China

2004 ◽  
Vol 94 (2) ◽  
pp. 740-746 ◽  
Author(s):  
Y. Sun
Keyword(s):  
Moving Window ◽  
Window Method ◽  
Velocity Tomography

scholarly journals Random Material Property Fields of 3D Concrete Microstructures Based on CT Image Reconstruction

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1423
Author(s):  
George Stefanou ◽  
Dimitrios Savvas ◽  
Panagiotis Metsis
Keyword(s):  
Random Fields ◽  
Volume Fraction ◽  
Concrete Specimen ◽  
Elasticity Tensor ◽  
Statistical Characteristics ◽  
Moving Window ◽  
Window Method ◽  
Ct Image Reconstruction ◽  
Random Material Property ◽  
Spatially Varying

The purpose of this paper is to determine the random spatially varying elastic properties of concrete at various scales taking into account its highly heterogeneous microstructure. The reconstruction of concrete microstructure is based on computed tomography (CT) images of a cubic concrete specimen. The variability of the local volume fraction of the constituents (pores, cement paste and aggregates) is quantified and mesoscale random fields of the elasticity tensor are computed from a number of statistical volume elements obtained by applying the moving window method on the specimen along with computational homogenization. Based on the statistical characteristics of the mesoscale random fields, it is possible to assess the effect of randomness in microstructure on the mechanical behavior of concrete.

scholarly journals PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

2019 ◽  
Vol 9 (2) ◽  
pp. 621-630
Keyword(s):  
Global Positioning System ◽  
Gps Data ◽  
Moving Window ◽  
Location Service ◽  
Window Method ◽  
Global Positioning ◽  
Trajectory Mapping ◽  
Coefficient Corrélation ◽  
Navigational System ◽  
Processing Techniques

The Global Positioning System is extensively used in the various context and location service-based applications. Any kind of abnormalities requires an efficient and suitable pre-processing algorithm to be implemented on the data which provides accurate results when used in the application synchronizations. This paper illustrates a framework for various pre-processing techniques applied to the real-time GPS data and its effect on trajectory mapping. The technique used includes Prioritized pattern-based, Savitzky-Golay filtering, outlier elimination, de-trending, and coefficient correlation. The performance assessment of methods discussed in this study is calculated in terms of accuracy with the original and re-created trajectory after the pre-processing and found that the best result is given by moving window method.

scholarly journals Concatenation of the Moving Window Technique for Auditory-Perceptual Analysis of Voice Quality

2018 ◽  
Vol 27 (4) ◽  
pp. 1426-1433
Author(s):  
Benjamin Ehrlich ◽  
Liyu Lin ◽  
Jack Jiang
Keyword(s):  
Sensitivity And Specificity ◽  
Characteristic Curve ◽  
Voice Quality ◽  
Moving Window ◽  
Perceptual Analysis ◽  
Perceptual Evaluation ◽  
Window Method ◽  
Window Technique ◽  
Pathological Voice ◽  
Higher Sensitivity

Purpose The purpose of this study is to develop a program to concatenate acoustic vowel segments that were selected with the moving window technique, a previously developed technique used to segment and select the least perturbed segment from a sustained vowel segment. The concatenated acoustic segments were compared with the nonconcatenated, short, individual acoustic segments for their ability to differentiate normal and pathological voices. The concatenation process sometimes created a clicking noise or beat, which was also analyzed to determine any confounding effects. Method A program was developed to concatenate the moving window segments. Listeners with no previous rating experience were trained and, then, rated 20 normal and 20 pathological voice segments, both concatenated (2 s) and short (0.2 s) for a total of 80 segments. Listeners evaluated these segments on both the Grade, Roughness, Breathiness, Asthenia, and Strain scale (GRBAS; 8 listeners) and the Consensus Auditory-Perceptual Evaluation of Voice (Kempster, Gerratt, Abbott, Barkmeier-Kraemer, & Hillman, 2009) scale (7 listeners). The sensitivity and specificity of these ratings were analyzed using a receiver-operating characteristic curve. To evaluate if there were increases in particular criteria due to the beat, differences between beat and nonbeat ratings were compared using a 2-tailed analysis of variance. Results Concatenated segments had a higher sensitivity and specificity for distinguishing pathological and normal voices than short segments. Compared with nonbeat segments, the beat had statistically similar increases for all criteria across Consensus Auditory-Perceptual Evaluation of Voice and GRBAS scales, except pitch and loudness. Conclusions The concatenated moving window method showed improved sensitivity and specificity for detecting voice disorders using auditory-perceptual analysis, compared with the short moving window segment. It is a helpful tool for perceptual analytic protocols, allowing for voice evaluation using standardized and automated voice-segmenting procedures. Supplemental Material https://doi.org/10.23641/asha.7100951

scholarly journals Coupling Normalization with Moving Window in Backpropagation Neural Network (BNN) for Passive Microwave Soil Moisture Retrieval

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Soo See Chai ◽  
Kok Luong Goh ◽  
Yee Hui Robin Chang ◽  
Kwan Yong Sim
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Standard Deviation ◽  
Time Series Data ◽  
Back Propagation ◽  
Training Data ◽  
Series Data ◽  
Moving Window ◽  
Window Method ◽  
Testing Data

AbstractA common practice to capture the non-stationary characteristics of the time series data in Artificial Neural Network (ANN) is by randomly dividing the whole set of available data into training, validation and testing, i.e. the data in validation and testing are represented in the training data. Consequently, the usability of the developed model on data not represented by the training data used during the network model development process is always doubtful. In this work, we present a back-propagation neural network (BNN) model trained using one-day history data to predict soil moisture data at 1 km resolution for two future dates. Specifically, high soil moisture values were observed in the training data while the testing data were characterized by drier conditions due to minimal or no rainfall. Our model uses separate mean and standard deviation statistics values from the training and testing data, respectively, to the z-normalized data. With data pre-processed using this method, the BNN model next uses a moving window of size 4 km × 4 km to capture the spatial variability of the soil moisture throughout the 40 km × 40 km study area. The coupling of the normalization and moving window method managed to achieve average soil moisture with Root Mean Square (RMSE) of 3.67% and correlation coefficient, R2 of 0.89. By only using the suggested normalization without the moving window method, the BNN model managed to achieve an average RMSE of barely 5.82% with R2 = 0.83. When comparing with the normal practice of using the same mean and standard deviation statistics of the training data in the testing data, the retrieval accuracy of the BNN model deteriorates to 8.86% with R2 = 0.32. The experiment results demonstrated that the proposed coupling method performed better in terms of both RMSE and R2 for soil moisture retrieval.

Sign in / Sign up

Export Citation Format

Share Document