scholarly journals SOM-Based Approach for the Analysis and Classification of Synchronous Impulsive Noise of an In-Ship PLC System

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
G. Acciani ◽  
V. Amoruso ◽  
G. Fornarelli ◽  
A. Giaquinto
Keyword(s):  
Impulsive Noise ◽  
Principal Component ◽  
Operating Conditions ◽  
Critical Conditions ◽  
Power Line Communications ◽  
High Data ◽  
Wideband Signals ◽  
Communication Frameworks

The interest in wideband data transmission over power line communications has increased rapidly. This technology offers a convenient and inexpensive medium to transmit data, reducing the number of cables. This advantage is particularly appealing in many fields, like the railway, naval, and aeronautical ones. Nevertheless, several problems have to be faced to obtain a high data rate. In particular, the presence of noise makes the transmission difficult, degrading the quality of received signals and prohibiting the full application of these communication frameworks. In this paper the behaviour of an in-ship powerline communication system is analyzed in the presence of synchronous periodic impulsive noise. Such noise is modelled at source and its effects on the transmission of wideband signals are evaluated by means of a simulation circuit model. The obtained results allow to identify the characteristics of the channel and the critical conditions due to noise. Subsequently, an unsupervised technique based on principal component analysis and fuzzy c-mean classifier detects the presence and classifies the specific noises. Numerical results show that the proposed approach enables to achieve this target accurately under different operating conditions, proving to be an effective tool to enhance the performances of the considered technology.

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

scholarly journals A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5097 ◽  
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Changes ◽  
Dimensional Space ◽  
Principal Component ◽  
Majority Voting ◽  
Data Set ◽  
Centroid Distance ◽  
Low Dimensional ◽  
Reduced Data

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

scholarly journals Classification of Sediment Quality according to Its Behavior in the Accelerated Particle Wear Test (APW)

2021 ◽  
Vol 13 (5) ◽  
pp. 2633
Author(s):  
José I. Pagán ◽  
Antonio J. Tenza-Abril ◽  
Luis Aragonés ◽  
Yolanda Villacampa ◽  
Isabel López
Keyword(s):  
Coastal Erosion ◽  
Sediment Quality ◽  
Principal Component ◽  
Wear Test ◽  
Wear Process ◽  
Simple Tool ◽  
Quality Classification ◽  
Classification Index

One of the main problems faced in coastal management is the loss or destruction of beaches due to erosion. A considerable diversity of factors is involved in coastal erosion, which makes it a complex system to study. The quality of the material that constitutes the beach, as well as the choice of appropriate materials for its nourishment are two of the main ones. Therefore, to make future nourishment projects more sustainable and durable, this work proposes a sediment quality classification based on the physical properties and wear process obtained through laboratory tests. The study of these variables, using principal component analysis, discriminant analysis and ANOVA, has divided the quality of 70 samples into three main groups. A Sediment Quality Classification Index (SQCI) is proposed, which categorizes the quality of the material into poor, regular or good, providing the coastal engineer with a simple tool to ensure more sustainable beach nourishments.

Image Quality Assessment for Fused Remote Sensing Imageries

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Mohd Nadzri Md Reba ◽  
Ong Juey C’uang
Keyword(s):  
Image Quality Assessment ◽  
Principal Component ◽  
High Accuracy ◽  
Fusion Image ◽  
Precise Information ◽  
Qualitative And Quantitative ◽  
Fused Image ◽  
Quantitative Assessments

Image fusion provides precise information in both spatial and spectral resolutions that benefit significantly in high accuracy mapping. Yet, there is less intention withdrawn in justifying the performance of the fused image. In this study, qualitative and quantitative assessments were carried out to test the quality of fusion image. Principal Component Analysis (PCA), Gram-Schmidt and Ehlers were applied to fuse the hyperspectral and Lidar image. Ehlers fusion showed good in preserving the color of image and contained the most information. Besides, the classification of Ehlers fused image showed the highest accuracy.

scholarly journals Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1269
Author(s):  
Elmer A. G. Peñaloza ◽  
Vilma A. Oliveira ◽  
Paulo E. Cruvinel
Keyword(s):  
Large Scale ◽  
Principal Component ◽  
Soft Sensor ◽  
Operating Conditions ◽  
K Nearest Neighbor ◽  
Pesticide Application ◽  
Soft Sensors ◽  
Agricultural Pesticides ◽  
Pcr Method

One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k−NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.

scholarly journals Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6992
Author(s):  
Rana Zia Ur Rehman ◽  
Yuhan Zhou ◽  
Silvia Del Din ◽  
Lisa Alcock ◽  
Clint Hansen ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Fall Risk ◽  
Clinical Decision Making ◽  
Principal Component ◽  
Clinical Decision ◽  
Poor Quality ◽  
Neurological Patients ◽  
Stride Duration

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Dynamics of the processes in metal machining

1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė
Keyword(s):  
High Frequency ◽  
Machine Tools ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Low Frequency ◽  
Metal Cutting Machine ◽  
Cutting Machine ◽  
Metal Machining

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.

Paepalanthus fabianeae (Eriocaulaceae): A New Microendemic Species from a Morphologically Coherent Clade

2020 ◽  
Vol 45 (4) ◽  
pp. 794-801
Author(s):  
Caroline Oliveira Andrino ◽  
Marcelo Fragomeni Simon ◽  
Jair Eustáquio Quintino Faria ◽  
André Luiz da Costa Moreira ◽  
Paulo Takeo Sano
Keyword(s):  
New Species ◽  
Iucn Red List ◽  
Phylogenetic Position ◽  
Similar Species ◽  
Infrageneric Classification ◽  
Espinhaço Range ◽  
Nuclear Its ◽  
Two Populations

Abstract—We describe and illustrate Paepalanthus fabianeae, a new species of Eriocaulaceae from the central portion of the Espinhaço Range in Minas Gerais, Brazil. Previous phylogenetic evidence based on analyses of nuclear (ITS and ETS) and plastid (trnL-trnF and psba-trnH) sequences revealed P. fabianeae as belonging to a strongly supported and morphologically coherent clade containing five other species, all of them microendemic, restricted to the Espinhaço range. Due to the infrageneric classification of Paepalanthus being highly artificial, we preferred not assigning P. fabianeae to any infrageneric group. Paepalanthus fabianeae is known from two populations growing in campos rupestres (highland rocky fields) in the meridional Espinhaço Range. The species is characterized by pseudodichotomously branched stems, small, linear, recurved, and reflexed leaves, urceolate capitula, and bifid stigmas. Illustrations, photos, the phylogenetic position, and a detailed description, as well as comments on habitat, morphology, and affinities with similar species are provided. The restricted area of occurrence allied with threats to the quality of the habitat, mainly due to quartzite mining, justifies the preliminary classification of the new species in the Critically Endangered (CR) category using the guidelines and criteria of the IUCN Red List.

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

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.

MEDIATEXT IS A TOOL THAT DETERMINES THE QUALITY OF INFORMATION

2020 ◽  
Vol 6 (3) ◽  
pp. 158-164
Author(s):  
Navruza Yakhyayeva ◽  
Keyword(s):  
Functional Properties ◽  
Linguistic Features ◽  
Text Linguistics ◽  
Quality Of Information ◽  
The Creation ◽  
Linguistic Units ◽  
Scientific Classification

The quality and content of information in the article media text is based on scientific classification of linguistic features. The study of functional styles of speech, the identification of their linguistic signs, the discovery of the functional properties of linguistic units and their separation on the basis of linguistic facts is one of thetasks that modern linguistics is waiting for a solution. Text Linguistics, which deals with the creation, modeling of its structure and the study of the process of such activity, is of interest to journalists today as a science.

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