Principal Component Analysis Technique Research Articles

Within-Subject Principal Component Analysis of External Training Load and Intensity Measures in Youth Soccer Training.

Marynowicz, J, Lango, M, Horna, D, Kikut, K, Konefał, M, Chmura, P, and Andrzejewski, M. Within-participant principal component analysis of external training load and intensity measures in youth soccer training. J Strength Cond Res 37(12): 2411-2416, 2023-The aim of this study was to identify which combination of external training load (EL) and external intensity (EI) metrics during youth soccer training sessions captured similar or unique information. Data were collected from 18 youth soccer players during an 18-week in-season competition period using a 10-Hz global positioning system, rating of perceived exertion (RPE), and session-RPE (sRPE). External training load measures included total distance (TD, in meters), PlayerLoad (PL, in arbitrary units), high-speed running distance (HSR, in meters), and number of accelerations (ACC, n). All EL metrics were also divided by session duration (minutes) to obtain EI values. A total of 804 training observations were undertaken (43 ± 17 sessions per player). The analysis was performed by use of the principal component analysis technique. The first principal component (PC) captured 49-70% and 68-89% of the total variance in EI and EL, respectively. The findings show that from the 5 EI metrics, most of the information can be explained by either TD per minute or PL per minute, with a loading from 0.87 to 0.98 and from 0.76 to 0.95, respectively. The majority of EL information can be explained by PL (loading: 0.93-0.98), TD (loading: 0.95-0.99), ACC (loading: 0.71-0.91), or sRPE (loading: 0.70-0.93). The second PC for EL metrics is most strongly correlated with HSR, with loadings from 0.53 to 0.84. The results suggest that the majority of the information contained in the EL variables can be captured in 1 PC without losing much information. The findings suggest that stakeholders who intend to provide a fast and holistic view of EL information in a daily training environment should report TD, PL, ACC, or sRPE plus HSR to coaching staff as a metrics that provides additional unique information.

FeelFlow: Music Recommendation System Based on your Facial Expression

Abstract: This study introduces an innovative Emotion-Aware Music Recommender System that leverages facial feature analysis for real-time emotion detection, aiming to elevate the personalization and user engagement in music recommendations. Through the integration of computer vision and machine learning techniques, the system interprets facial expressions to discern user’s emotional states before recommending music to them. Integrating advanced deep learning and evolutionary techniques improves the accuracy and efficiency of facial expression detection. Implementation of the proposed recommender system is performed using Viola-Jones algorithm, CNN, Principal Component Analysis (PCA) techniques. Experimental results confirm the effectiveness of this method and demonstrate its potential to improve music recommendations

Groundwater quality evaluation of Narmada district, Gujarat using principal component analysis

In the present study, the ground water quality parameters were monitored during pre- and post-monsoon seasons across Narmada district, Gujarat, India. Monitoring was done in 89 drinking water samples collected by grid sampling method from the study area. Uranium and fluoride were analyzed along with associated parameters such as pH, dissolved oxygen, Cl−, NO3−, F−, SO42−, total alkalinity, total dissolved solids and hardness. In 4% samples the fluoride content was found to be above WHO permissible limits of 1.5 mg/L (2.36 mg/L in Undaimandava, 1.55 mg/L in Shira, 3.04 mg/L in Fatehpur and 1.83 mg/L in Dholivav) during pre-monsoon season (PRM) and 4.74 mg/L, 2.41 mg/L, 2.34 mg/L and 3.99 mg/L respectively in Bantawadi, Shira, Undai Mandava and Fatepur villages during post-monsoon (POM). The uranium level was within WHO limits in both POM and PRM seasons. The quality of the water was evaluated by Principal Component and Pearson Correlation statistical analysis techniques. The PRM and POM correlation study indicated a strong correlation of TDS with EC, Chloride, total alkalinity and bicarbonate and U while moderately strong correlation of TDS with fluoride were observed indicating that chloride, total alkalinity, bicarbonate, U and fluoride contributed to TDS and EC. Principal component analysis was applied for 14 variables, from which 3 factors were extracted during PRM and POM seasons. The extracted components, contributed 84.391% and 83.315%, to variation during PRM and POM seasons respectively. The study indicated that the analyzed water samples in Narmada district were safe for drinking purpose. However, Tilakwada tehsil groundwater was observed to be unsustainable for drinking, without further water treatment, but was appropriate for agricultural purposes. The study will help the residents of the district to understand the present water quality status and will also help in future management to protect the ground water of Narmada district.

Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

ABSTRACT Carbon fibre reinforced polymer (CFRP) composites are widely used in the aerospace industry; they are susceptible to impact damage from low-velocity impacts throughout their service life. Long pulse thermography (LPT) was proposed to detect defect in low-velocity impact test (LVIT). In this framework, the defects of matrix fractures, fibre cracks and interface delamination were produced by LVIT in the CFRP specimens. The halogen lamps LPT test system was applied to detect the low-velocity impact damage, and an A655sc infrared camera was applied to collect the surface temperature. In order to improve the efficiency of the impact damage detection, the infrared image sequences were processed by applying different post-processing techniques such as pulse phase thermography (PPT), thermal signal reconstruction (TSR), principal component analysis (PCA), total harmonic distortion (THD), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). The results show that the signal-to-noise ratio (SNR) was improved by PPT, TSR, PCA, THD, LDA and QDA post-processing techniques. The proposed supervised learning (SL) QDA algorithm achieves better defect identification results than the above post-processing techniques. The detectability of real defects can provide a realistic assessment by the above post-processing techniques.

Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

AbstractElectricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets.This article is categorized under: Energy and Power Systems > Energy Management Energy and Power Systems > Distributed Generation Emerging Technologies > Digitalization

Determinants of the successful signing of public–private partnership contracts for water and sanitation infrastructure financing in Zimbabwe

Abstract The private sector remains hesitant to invest in water and sanitation infrastructure in Zimbabwe. For policymakers and investors, it is pertinent to understand the factors that determine the signing of water and sanitation public–private partnership (PPP) contracts, in order to leverage expertise and resources to achieve the Sustainable Development Goal targets. This study applied count econometrics on data collected for the 25 years from 1996 ending in 2021, with the aim of investigating the determinants of the number of PPP contracts signed in Zimbabwe. Poisson regression estimations identified positive macroeconomic prospects, financial market development, and strong institutional governance environment as important determinants for PPP contracting in Zimbabwe. The number of PPP contracts is further confirmed to be dependent on the availability of inward foreign direct investment flows. The influence of institutional governance quality on the number of water and sanitation PPP contracts was tested using a composite index constructed using the principal components analysis technique. It is advised that the government of Zimbabwe should strengthen their governance institutions and further develop their capital and bank credit markets, so as to attract investors to take up the water and sanitation infrastructure PPP contract opportunities available in the country.

The Principal Component Analysis Filtering Method for an Unbiased Spectral Survey of Complex Organic Molecules

A variety of interstellar complex organic molecules (COMs) have been detected in various physical conditions. However, in the protostellar and protoplanetary environments, their complex kinematics make line profiles blend together and the line strength of weak lines weaker. In this paper, we utilize the principal component analysis technique to develop a filtering method that can extract COM spectra from the main kinematic component associated with COM emission and increase the signal-to-noise ratio (S/N) of spectra. This filtering method corrects non-Gaussian line profiles caused by the kinematics. For this development, we adopt the ALMA BAND 6 spectral survey data of V883 Ori, an eruptive young star with a Keplerian disk. A filter was, first, created using 34 strong and well-isolated COM lines and then applied to the entire spectral range of the data set. The first principal component (PC1) describes the most common emission structure of the selected lines, which is confined within the water sublimation radius (∼0.″3) in the Keplerian disk of V883 Ori. Using this PC1 filter, we extracted high-S/N kinematics-corrected spectra of V883 Ori over the entire spectral coverage of ∼50 GHz. The PC1-filtering method reduces the noise by a factor of ∼2 compared to the average spectra over the COM emission region. One important advantage of this PC1-filtering method over the previously developed matched-filtering method is the ability to preserve the original integrated intensities of COM lines.

Needlet Karhunen–Loève (NKL): a method for cleaning foregrounds from 21 cm intensity maps

ABSTRACT This paper introduces a technique called needlet Karhunen–Loéve (NKL), which cleans both polarized and unpolarized foregrounds from H i intensity maps by applying a Karhunen–Loéve transform on the needlet coefficients. In NKL, one takes advantage of correlations not only along the line of sight, but also between different angular regions, referred to as ‘chunks’. This provides a distinct advantage over many of the standard techniques applied to map space that one finds in the literature, which do not consider such spatial correlations. Moreover, the NKL technique does not require any priors on the nature of the foregrounds, which is important when considering polarized foregrounds. We also introduce a modified version of Generalized Needlet Internal Linear Combination (GNILC), referred to as MGNILC, which incorporates an approximation of the foregrounds to improve performance. The NKL and MGNILC techniques are tested on simulated maps which include polarized foregrounds. Their performance is compared to the GNILC, generalized morphological component analysis, independent component analysis, and principal component analysis techniques. Two separate tests were performed. One at 1.84 < z < 2.55 and the other at 0.31 < z < 0.45. NKL was found to provide the best performance in both tests, providing a factor of 10–50 improvement over GNILC at $k \lt 0.1\, {\rm hMpc^{-1}}$ in the higher redshift case and $k \lt 0.03 \, {\rm hMpc^{-1}}$ in the lower redshift case. However, none of the methods were found to recover the power spectrum satisfactorily at all baryon acoustic oscillations scales.

Towards explainable metaheuristics: Feature extraction from trajectory mining

AbstractExplaining the decisions made by population‐based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that increasingly require end‐user input and confirmation, the need to explain the internal decisions being made has grown. In this article, we present our approach to the extraction of explanation supporting features using trajectory mining. This is achieved through the application of principal components analysis techniques to identify new methods of tracking population diversity changes post‐runtime. The algorithm search trajectories were generated by solving a set of benchmark problems with a genetic algorithm and a univariate estimation of distribution algorithm and retaining all visited candidate solutions which were then projected to a lower dimensional sub‐space. We also varied the selection pressure placed on high fitness solutions by altering the selection operators. Our results show that metrics derived from the projected sub‐space algorithm search trajectories are capable of capturing key learning steps and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients. A comparative study of variable importance rankings derived from a surrogate model built on the same dataset was also performed. The results show that both approaches are capable of identifying key features regarding variable interactions and their influence on fitness in a complimentary fashion.

Cauchy robust principal component analysis with applications to high-dimensional data sets

Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA can be formulated in terms of operations on a multivariate Gaussian likelihood. As a consequence of the implied Gaussian formulation, the principal components are not robust to outliers. In this paper, we propose a modified formulation, based on the use of a multivariate Cauchy likelihood instead of the Gaussian likelihood, which has the effect of robustifying the principal components. We present an algorithm to compute these robustified principal components. We additionally derive the relevant influence function of the first component and examine its theoretical properties. Simulation experiments on high-dimensional datasets demonstrate that the estimated principal components based on the Cauchy likelihood typically outperform, or are on a par with, existing robust PCA techniques. Moreover, the Cauchy PCA algorithm we have used has much lower computational cost in very high dimensional settings than the other public domain robust PCA methods we consider.

The Monitoring of Abnormal Fluid Properties Based on PCA Technique as an Alternative Strategy to Support Autonomous Drilling Operations

Summary The well drilling process requires constant monitoring to ensure that the properties of the drilling fluids remain within acceptable ranges for safe and effective operation of the well drilling process. The present work developed a principal component analysis (PCA)-based methodology for diagnosing anomalies in drilling fluids, and detecting and identifying abnormal drilling fluid properties during well drilling operations. The main novelty of the present work regards the application of multivariate techniques for diagnosing anomalies (faults) in drilling fluids, increasing the literature on fault diagnosis techniques applied to the petroleum industry, and producing a promising methodology for field applications. The proposed technique was implemented and validated in a pilot drilling fluid production unit through continuous online monitoring of the conductivity, density, and apparent viscosity of drilling fluids. Model training was carried out with data collected during assisted normal operation, allowing detection of abnormal conditions with less than 1% of false positives and less than 0.5% of false negatives. Additionally, the proposed methodology also allowed the correct diagnosis of the observed faults. The results indicated that PCA-based approaches can be used for the online monitoring of drilling fluid properties and fault diagnosis in real well drilling operations.

Emerging progress of carbon nanomaterials in wastewater treatment: synthesis and utilization

Abstract In this work, data from experiments concerning carbon components in various forms, including graphite, graphene oxide (GO), active carbon, compilations, and analyses of the substances used as efficient wastewater management agents, are conducted. The initial phase involved characterizing the mentioned carbon compounds and nanoparticles (NPs) using various complementary methods. Raman spectrophotometry, energy-dispersive spectroscopy, scanning electron microscopy, and zeta spectrum research are involved in the approach. The next obstacle was coming up with a very straightforward procedure that would allow us to examine whether carbon NPs behaved in a mixture of effluent water and active sludge regarding hydrodynamic and physiological consequences. Principal component analysis (PCA) (univariate and multivariate) has confirmed the complicated relationships of NPs made of GO with organisms observed in processed wastewater. PCA techniques are used to evaluate experimental outcomes. Utilizing the results from this experiment, several huge-scale studies on how active NPs affect wastewater purification may be designed. Simulations using PCA clearly showed that GO significantly impacts the wastewater technical processes under investigation. It is envisaged that the discoveries will enable the design of clever conservation technologies.

Strategic Thinking and Competitive Advantage: Evidence from the Nigerian Pharmaceutical Industry

The focus of this study is on strategic thinking and competitive advantage in the Nigerian pharmaceutical industry. Competition is now based on knowledge, technology and the ability to serve customers fast and repeatedly. These are functions of strategic thinking in an organisation. We disaggregated strategic thinking into systems thinking, reflective thinking and creative thinking to ascertain their level of impact on the competitive advantage of firms in the pharmaceutical industry. Using data obtained from 91 employees drawn from three pharmaceutical companies in Nigeria, we adopted the Principal Component Analysis (PCA) technique in analysing the data. The analysis found significant effects of systems and creative thinking on the competitive advantage of pharmaceutical firms in Nigeria. In contrast, reflective thinking was not a significant component of the firm’s competitive advantage. The extraction effects of the components revealed that encouragement of individual employees through seeking their ideas on key decisions, leaders being part of the feedback process in the organisation, participative discussions and enhancement of practical skills of employees accounted for high variance in the firm’s strategic thinking and competitive advantage. The study concluded that companies in the Nigerian pharmaceutical industry have more attachment to systems thinking and creative thinking than reflective thinking. Part of the recommendations was adopting the strategic thinking process of plan, act, observe, reflect and plan again to enhance their competitive advantage in the pharmaceutical industry.

Heterogeneous road traffic noise modeling at mid-block sections of mid-sized city in India.

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 (LAeq, 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, forLeq, and a value of 0.86, 0.80, and 0.84 for L10. The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named "Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)" was developed for the benefit of field engineers and policy planners.

Machine learning enhanced characterization of surface defects using ultrasonic Rayleigh waves

Rayleigh waves are successfully employed for the characterization of surface defects through the utilization of both time and frequency domain analysis. However, a significant challenge arises when attempting to cover a broad range of the defect depth to Rayleigh wavelength ratio with a single method. This paper presents an enhanced methodology that integrates optimized machine learning (ML) algorithms to expand the coverage of defect depth assessment. An experimentally validated numerical model is firstly established to simulate the interaction of Rayleigh waves with surface defects and utilized to generate a significant quantity of labeled Rayleigh wave signals. The features of received Rayleigh wave patterns are effectively extracted and serve as indicators of defect parameters. The ML framework encompasses the extraction and selection of optimal features, the construction of model architecture, and the tuning of hyperparameters. Three dimensionality optimization techniques—principal component analysis, correlation coefficient analysis, and expert knowledge integration—are compared. Additionally, six machine learning models (decision tree, support vector machine, k-nearest neighbors, random forest, gradient-boosting machine, and artificial neural network) are optimized using particle swarm optimization and subjected to comparison. The results reveal that: (1) principal component analysis yields the highest performance, suggesting the potential for feature dimensionality reduction without relying on expert knowledge; (2) among the models examined, the random forest model and artificial neural network with hyperparameter tuning demonstrate superior performance in predicting defect depth; and (3) the optimized machine learning methods consistently outperform empirical approaches in accurately predicting defect depths across a wide range. Furthermore, the proposed method shows promise for extension to determine additional parameters associated with surface defects, including their width and angle of inclination.

SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis

Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from a sparse subspace. We introduce SuSiE PCA, a scalable sparse latent factor approach that evaluates uncertainty in contributing variables through posterior inclusion probabilities. We validate our model in extensive simulations and demonstrate that SuSiE PCA outperforms other approaches in signal detection and model robustness. We apply SuSiE PCA to multi-tissue expression quantitative trait loci (eQTLs) data from GTEx v8 and identify tissue-specific factors and their contributing eGenes. We further investigate its performance on the large-scale perturbation data and find that SuSiE PCA identifies modules with a higher enrichment of ribosome-related genes than sparse PCA (false discovery rate [FDR] vs. ), while being 18x faster. Overall, SuSiE PCA provides an efficient tool to identify relevant features in high-dimensional biological data.

Estimation of water quality of Dikhu River of Nagaland through a combination of water quality index and principal component analysis techniques.

Estimation of water quality of Dikhu River of Nagaland through a combination of water quality index and principal component analysis techniques.

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

Seepage Prediction Model for Roller-Compacted Concrete Dam Using Support Vector Regression and Hybrid Parameter Optimization

In this study, a seepage prediction model was established for roller-compacted concrete dams using support vector regression (SVR) with hybrid parameter optimization (HPO). The model includes data processing via HPO and machine learning through SVR. HPO benefits from the correlation extraction capability of grey relational analysis and the dimensionality reduction technique of principal component analysis. The proposed model was trained, validated, and tested using 22 years of monitoring data regarding the Shuidong Dam in China. We compared the performance of HPO with other popular methods, while the SVR method was compared with the traditional time-series prediction method of long short-term memory (LSTM). Our findings reveal that the HPO method proves valuable real-time dam safety monitoring during data processing. Meanwhile, the SVR method demonstrates superior robustness in predicting seepage flowrate post-dam reinforcement, compared with LSTM. Thus, the developed model effectively identifies the factors related to seepage and exhibits high accuracy in predicting fluctuation trends regarding the Shuidong Dam, achieving a determination coefficient R2 > 0.9. Further, the model can provide valuable guidance for dam safety monitoring, including diagnosing the efficacy of monitoring parameters or equipment, evaluating equipment monitoring frequency, identifying locations sensitive to dam seepage, and predicting seepage.

A chance‐constrained tube‐based model predictive control for tracking linear systems using data‐driven uncertainty sets

AbstractThis article presents a chance‐constrained tube‐based model predictive control (MPC) method for tracking linear time‐invariant systems based on data‐driven uncertainty sets. By defining the terminal admissible set to consider all the possible steady‐states and reformulating the stochastic tube‐based MPC framework, the proposed method can systematically hedge against the impact of uncertainties and ensure tracking for all reachable operating setpoints. To reduce the conservatism of control performance while enlarging the feasible region, a data‐driven polyhedral uncertainty set is constructed by using the principal component analysis technique, which can effectively capture correlations among uncertain variables. Since state constraint violations in a certain probability are allowed, a probability uncertainty set is constructed by using statistic limit and cutting plane methods to formulate a stochastic tube to ensure constraint satisfaction. The recursive feasibility and stability can be guaranteed if the uncertainties are bounded. The effectiveness of the proposed method is verified by numerical examples and tracking problems of a thickening process.