scholarly journals Estimation of Hydrologic Alteration in Kaligandaki River Using Representative Hydrologic Indices

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 688 ◽  
Author(s):  
Gao Yuqin ◽  
Kamal Prasad Pandey ◽  
Xianfeng Huang ◽  
Naresh Suwal ◽  
Khem Prasad Bhattarai
Keyword(s):  
Anthropogenic Activities ◽  
Principal Component ◽  
Data Sets ◽  
Large Set ◽  
River Section ◽  
Comparison Approach ◽  
Daunting Task ◽  
Natural Flow ◽  
Hydrologic Indices ◽  
Value Changes

Anthropogenic activities have led to the transformation of river basins and natural flow alteration around the world. Alteration in flow regimes have adverse effects on river ecosystems. Flow value changes signify the alteration extent and a number of flow related indices can be used to assess the extent of alteration in a river ecosystem. Selection of a few and ecologically relevant indices from a large set of available indices is a daunting task. Principal Component Analysis helps to reduce these large indices to a few ecologically significant indices and removes statistical redundancy of data to give uncorrelated data sets. These representative indices are useful in the primary investigation of a less studied area like the Kaligandaki River basin, Nepal. This paper uses reduced indices from the Kaligandaki River to calculate the alteration on the river section downstream of a hydropower facility using the Histogram Comparison Approach (HCA) combined with Hydrologic Year Types (HYT). The combined approach eliminates the potential underestimation of alteration values which may occur due to the exemption of hydrologic year types from the analysis, a feature equally relevant in river ecology. A new metric is used for the calculation of combined alteration using HCA-HYT in this paper. The analysis showed 60.71 percent alteration in the natural flow regime in the area past a hydropower construction, which is classified in the high alteration category. The study can be a guide for further analysis of the ecological flow management of a river section and a parsimonious approach to other areas where hydrological data is limited to historical flow records only.

Makeup Invariant Face Recognition using Features from Accelerated Segment Test and Eigen Vectors

2017 ◽  
Vol 17 (01) ◽  
pp. 1750005 ◽  
Author(s):  
Aruna Bhat
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Training Data ◽  
Data Sets ◽  
Security Breaches ◽  
Daunting Task ◽  
Eigen Values ◽  
The Face ◽  
Robust Face Recognition

A methodology for makeup invariant robust face recognition based on features from accelerated segment test and Eigen vectors is proposed. Makeup and cosmetic changes in face have been a major cause of security breaches since long time. It is not only difficult for human eyes to catch an imposter but also an equally daunting task for a face recognition system to correctly identify an individual owing to changes brought about in face due to makeup. As a crucial pre-processing step, the face is first divided into various segments centered on the eyes, nose, lips and cheeks. FAST algorithm is then applied over the face images. The features thus derived from the facial image act as the fiducial points for that face. Thereafter principal component analysis is applied over the set of fiducial points in each segment of every face image present in the data sets in order to compute the Eigen vectors and the Eigen values. The resultant principal component which is the Eigen vector with the highest Eigen value yields the direction of the features in that segment. The principal components thus obtained using fiducial points generated from FAST in each segment of the test and the training data are compared in order to get the best match or no match.

scholarly journals Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
And Cluster Analysis

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.

scholarly journals Review and Assessment of the Effect of Hydrogen Gas Pressure on the Embrittlement of Steels in Gaseous Hydrogen Environment

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 637
Author(s):  
Thorsten Michler ◽  
Ken Wackermann ◽  
Frank Schweizer
Keyword(s):  
Hydrogen Gas ◽  
Gaseous Hydrogen ◽  
Gas Pressure ◽  
Data Sets ◽  
Large Set ◽  
Test Parameter ◽  
Rate Limiting ◽  
Important Test ◽  
Pressure Hydrogen ◽  
Reaction Chain

Hydrogen gas pressure is an important test parameter when considering materials for high-pressure hydrogen applications. A large set of data on the effect of hydrogen gas pressure on mechanical properties in gaseous hydrogen experiments was reviewed. The data were analyzed by converting pressures into fugacities (f) and by fitting the data using an f|n| power law. For 95% of the data sets, |n| was smaller than 0.37, which was discussed in the context of (i) rate-limiting steps in the hydrogen reaction chain and (ii) statistical aspects. This analysis might contribute to defining the appropriate test fugacities (pressures) to qualify materials for gaseous hydrogen applications.

scholarly journals Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1573
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Gianluca Maguolo ◽  
Alessandra Lumini
Keyword(s):  
Vector Space ◽  
Image Classification ◽  
Ad Hoc ◽  
Feature Space ◽  
Medical Data ◽  
Training Data ◽  
Data Sets ◽  
Large Set ◽  
Clustering Methods ◽  
Siamese Networks

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system’s performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Assessment of Damage Progression in Automotive Electronics Assemblies Subjected to Temperature and Vibration

2018 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Instantaneous Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Large Data ◽  
Data Sets ◽  
Automotive Electronics ◽  
Damage Progression ◽  
Mode Decomposition ◽  
Electronic Assemblies ◽  
Strain Signal

Electronics in automotive underhood environments is used for a number of safety critical functions. Reliable continued operation of electronic safety systems without catastrophic failure is important for safe operation of the vehicle. There is need for prognostication methods, which can be integrated, with on-board sensors for assessment of accrued damage and impending failure. In this paper, leadfree electronic assemblies consisting of daisy-chained parts have been subjected to high temperature vibration at 5g and 155°C. Spectrogram has been used to identify the emergence of new low frequency components with damage progression in electronic assemblies. Principal component analysis has been used to reduce the dimensionality of large data-sets and identify patterns without the loss of features that signify damage progression and impending failure. Variance of the principal components of the instantaneous frequency has been shown to exhibit an increasing trend during the initial damage progression, attaining a maximum value and decreasing prior to failure. The unique behavior of the instantaneous frequency over the period of vibration can be used as a health-monitoring feature for identifying the impending failures in automotive electronics. Further, damage progression has been studied using Empirical Mode Decomposition (EMD) technique in order to decompose the signals into Independent Mode Functions (IMF). The IMF’s were investigated based on their kurtosis values and a reconstructed strain signal was formulated with all IMF’s greater than a kurtosis value of three. PCA analysis on the reconstructed strain signal gave better patterns that can be used for prognostication of the life of the components.

scholarly journals Geochemical and statistical evaluation of groundwater in the Thamirabarani river basin, south India

2021 ◽  
Author(s):  
Gajendran Chellaiah ◽  
Basker ◽  
Hima Pravin ◽  
Suneel Kumar Joshi ◽  
Sneha Gautam
Keyword(s):  
River Basin ◽  
Tamil Nadu ◽  
Statistical Evaluation ◽  
Anthropogenic Activities ◽  
Monsoon Season ◽  
Principal Component ◽  
Chloride Ion ◽  
Temporal Analysis ◽  
Point Sources ◽  
International Standards

Abstract In the present study, an attempt has been made to develop the dictate metrics using a multi-proxy approach, i.e., spatial-temporal analysis, statistical evaluation, and hydrogeochemical analysis for 45 water samples located in the Thamirabarani river basin in Tamil Nadu, India. In order to evaluate the aptness of developed metrics for agriculture and domestic needs, eleven years dataset was analyzed and compared with national and international standards. Monitoring and analysis results revealed that the concentration of calcium and chloride ion was on the higher side in all the selected locations. These higher values may be attributed to the regional point sources such as untreated water disposal and off-peak sources such as agriculture practices. The principal component analysis resulted in 84.2% of the total variance in the post-monsoon season dataset. The major analyzed cations and anions were observed in the following order: Na+> Ca2+> Mg2+> K+ and Cl−> HCO3−> SO42−> NO3−, respectively. Overall, this study revealed that the studied area's groundwater quality was significantly affected by the high salinity in the region, probably due to anthropogenic activities and unprotected river sites.

scholarly journals Input data preprocessing method for exchange rate forecasting via neural network

2014 ◽  
Vol 11 (4) ◽  
pp. 597-608
Author(s):  
Dragan Antic ◽  
Miroslav Milovanovic ◽  
Stanisa Peric ◽  
Sasa Nikolic ◽  
Marko Milojkovic
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Principal Component ◽  
Economic Systems ◽  
Data Sets ◽  
Foreign Exchange Rates ◽  
Analysis Method ◽  
Feed Forward Neural Network ◽  
Parameters Selection ◽  
Neural Network Input

The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data.

scholarly journals Giovanni: The Bridge Between Data and Science

Eos ◽  
2017 ◽  
Author(s):  
Zhong Liu ◽  
James Acker
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Science Data ◽  
Web Based ◽  
Sensing Data ◽  
Daunting Task ◽  
Earth Science Data

Using satellite remote sensing data sets can be a daunting task. Giovanni, a Web-based tool, facilitates access, visualization, and exploration for many of NASA’s Earth science data sets.

scholarly journals Spatial Distribution of Fluoride in Drinking Water in Dhamar City, Yemen

2021 ◽  
Vol 10 (1) ◽  
pp. 49-63
Author(s):  
Hefdhallah Al Aizari ◽  
Rachida Fegrouche ◽  
Ali Al Aizari ◽  
Saeed S. Albaseer
Keyword(s):  
Drinking Water ◽  
Anthropogenic Activities ◽  
Groundwater Resources ◽  
Principal Component ◽  
World Health ◽  
Residential Areas ◽  
Central Plateau ◽  
The World ◽  
Pca Method ◽  
Health Organization

The fact that groundwater is the only source of drinking water in Yemen mandates strict monitoring of its quality. The aim of this study was to measure the levels of fluoride in the groundwater resources of Dhamar city. Dhamar city is the capital of Dhamar governorate located in the central plateau of Yemen. For this purpose, fluoride content in the groundwater from 16 wells located around Dhamar city was measured. The results showed that 75% of the investigated wells contain fluoride at or below the permissible level set by the World Health Organization (0.5 – 1.5 mg/L), whereas 25% of the wells have relatively higher fluoride concentrations (1.59 – 184 mg/L). The high levels of fluoride have been attributed to the anthropogenic activities in the residential areas near the contaminated wells. Interestingly, some wells contain very low fluoride concentrations (0.30 – 0.50 mg/L).  Data were statistically treated using the principal component analysis (PCA) method to investigate any possible correlations between various factors. PCA shows a high correlation between well depth and its content of fluoride. On the other hand, health problems dominating in the study area necessitate further studies to investigate any correlation with imbalanced fluoride intake.

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