This research investigated a mathematical model of earphone design with principal component analysis. Along with simplifying the design problem, a predictive model for the sound quality indicators, namely, total harmonic distortion, power of output,...
The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on...
Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracte...
In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes...
High-plasticity clay soils pose significant challenges in geotechnical engineering due to their poor mechanical properties, such as low strength and high compressibility. Lime–cement stabilization offers a sustainable solution, but optimizing a...
This article first selects the “Urban Statistical Yearbook” data of 264 prefecture-level cities in China from 2004 to 2018 as the raw data, and uses principal component analysis and the Wilson model to calculate the spatial information di...
Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect...
Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static proces...
The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nos...
Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on the maximum likelihood procedure provides a probabilistic manner to implement dimension reduction...
The present contribution is devoted to the theory of fuzzy sets, especially Atanassov Intuitionistic Fuzzy sets (IF sets) and their use in practice. We define the correlation between IF sets and the correlation coefficient, and we bring a new perspec...
Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyper...
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPC...
28 October 2019
In this paper, two-dimensional quantitative structure–activity relationship (2D-QSAR) and principal component analysis (PCA) methods were employed to screen the main parameters affecting the genotoxicity of fluoroquinolones (FQs), and the rules...
19 March 2021
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered r...
The method of measuring temperature using luminescence by analyzing the emission spectra of Pr3+-doped YF3 using principal component analysis is presented. The Pr3+-doped YF3 is synthesized using a solid-state technique, and its single-phase orthorho...
This study deals with the utilization of the pore pressure meter for evaluating the stability of a dam through the correlation between the porewater pressure installed in the fill dam and the water level of the dam. To this end, principal components...
Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt i...
Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-proc...
23 February 2024
Glyphosate is an herbicide used worldwide with harmful health effects, and efforts are currently being made to develop sensors capable of detecting its presence. In this work, an array of four interdigitated microwave sensors was used together with t...
During visual fixation on a target, humans perform miniature (or fixational) eye movements consisting of three components, i.e., tremor, drift, and microsaccades. Microsaccades are high velocity components with small amplitudes within fixational eye...
This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifu...
The presented paper introduces principal component analysis application for dimensionality reduction of variables describing speech signal and applicability of obtained results for the disturbed and fluent speech recognition process. A set of fluent...
To establish a coccidiosis resistance evaluation model for chicken selection, the different parameters were compared between infected and control Jinghai yellow chickens. Validation parameters were selected for principal component analysis (PCA), and...
This paper has proposed a comprehensive indicator based on principal component analysis (PCA) for diagnosing the state of anaerobic digestion. Various state and performance variables were monitored under different operational modes, including start-u...
The identification of a reduced dimensional representation of the data is among the main issues of exploratory multidimensional data analysis and several solutions had been proposed in the literature according to the method. Principal Component Analy...
Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eig...
Single-cell RNA-seq (scRNA-seq) is a powerful tool for analyzing heterogeneous and functionally diverse cell population. Visualizing scRNA-seq data can help us effectively extract meaningful biological information and identify novel cell subtypes. Cu...
Quartz crystal nanobalance (QCN) sensors are considered as powerful mass-sensitive sensors to determine materials in the sub-nanogram level. In this study, a singlepiezoelectric quartz crystal nanobalance modified with polystyrene was employed to det...
Traditional target detection (TD) algorithms for hyperspectral imagery (HSI) typically suffer from background interference. To alleviate this problem, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to sepa...
Background/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensional...
Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo”...
Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course...
Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery...
19 October 2020
The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal...
Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through...
Digital pulse shape analysis (DPSA) techniques are becoming increasingly important for the study of nuclear reactions since the development of fast digitizers. These techniques allow us to obtain the (A, Z) values of the reaction products impinging o...
Large price fluctuations have become a significant character and impede resource allocation in the electricity market. Negative prices and peak load spike prices coexist and represent over-supply and over-demand, respectively. It is important to inte...
Machine learning algorithms play an important role in fault detection and fault diagnosis of gas sensor arrays. Because the gas sensor array will see stability degradation and a shift in output signal amplitude under long-term operation, it is very i...
The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the tradition...
The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and...
This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal co...
Background and Objectives: This study aimed to group diseases classified by the International Classification of Diseases using principal component analysis, and discuss a systematic approach to reducing the preventable death rate from a perspective o...
The complex burst characteristic parameters of SCB were subjected to dimensionality reduction using principal component analysis (PCA), enabling accurate evaluation of the output performance of SCB. The accuracy and reliability of the PCA method were...
Rolling bearings are one of the most widely used parts in all kinds of rotating machinery (including wind power equipment) and also one of the most easily damaged parts, which makes fault diagnosis of rolling bearings a promising research field. To t...
In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit...
More than 55% of porcelain insulators installed throughout Korea have exceeded their service life. Hence, utilities are extremely interested in determining the robustness of insulators in their systems. In this study, the identification of the peak r...
Many factors influence the evaluation process of thief zones. The evaluation index contains very complex information. How to quickly obtain effective information is the key to improve the evaluation quality for thief zones. Considering that the corre...
Multiple benefit evaluation may become complicated due to varied and unquantifiable indicators. This study presents a multicriteria approach to evaluate Low Impact Development (LID) performance through an aggregation of principal component analysis a...
Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne...
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