Evidence of pressure jump signatures linked to fast air-water displacement dynamics in macropores

Mapping Intimacies ◽

10.5194/egusphere-egu2020-22068 ◽

2020 ◽

Author(s):

Jose Eugenio López Periago ◽

Diego Soto Gomez ◽

Marcos Paradelo

Keyword(s):

Principal Component Analysis ◽

Pore Pressure ◽

Clustering Algorithms ◽

Principal Component ◽

High Sensitivity ◽

Pressure Jump ◽

Water Displacement ◽

Pressure Oscillations ◽

Different Types

<p>We present a method to identify the characteristics of pressure jumps in repeated pump-driven saturation and drainage cycles in packed sand columns, i.e.,&#160; pore pressure from zero to -80 hPa. This method is based on the registration of pore pressure oscillations using high sensitivity and rapid response tensiometers. The pressure signals were processed to identify and extract the pressure waveforms read by two tensiometers simultaneously.&#160; Then, the waveforms were classified by k-means clustering algorithms and principal component analysis. We obtained a classification of different types of pressure waveforms that are consistent throughout successive cycles of wetting and drainage cycles. This consistency can be associated with patterns of fast capillary displacements in macropores, and ultimately, to hydrodynamic features of the structure.</p>

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The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Studies in Logic, Grammar and Rhetoric ◽

10.2478/slgr-2014-0043 ◽

2014 ◽

Vol 39 (1) ◽

pp. 7-23 ◽

Cited By ~ 6

Author(s):

Anna Justyna Milewska ◽

Dorota Jankowska ◽

Dorota Citko ◽

Teresa Więsak ◽

Brian Acacio ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Data Preparation ◽

Vitro Fertilization ◽

Multidimensional Datasets ◽

Different Types ◽

Pca Method

Abstract Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.

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A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2019.63.5.050402 ◽

2019 ◽

Vol 63 (5) ◽

pp. 50402-1-50402-9 ◽

Cited By ~ 1

Author(s):

Ing-Jr Ding ◽

Chong-Min Ruan

Keyword(s):

Principal Component Analysis ◽

Automatic Speech Recognition ◽

Feature Fusion ◽

Three Dimensional ◽

Principal Component ◽

Recognition System ◽

Geometrical Characteristics ◽

3D Geometry ◽

Different Types ◽

The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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