Weighted Principal Component Analysis Research Articles

Modeling and health feature extraction method for lithium-ion batteries state of health estimation by distribution of relaxation times

Accurate state of health (SOH) estimation of lithium batteries (LIBs) depends on the accuracy of LIBs modeling and the correlation of extracted health features (HFs). Aiming at the problems of a priori assumptions for existing battery modeling and poor correlation of HFs, this paper proposes a SOH estimation method for LIBs based on distribution of relaxation times (DRT). Firstly, we deduce and establish the developed DRT-based impedance model from battery electrochemical impedance spectroscopy (EIS), and the model parameters are calculated by EIS split-frequency domain method based on the principle of parsimony. Then, we analyze the variation rules of the peak and valley information in the distribution function of relaxation times curve with the capacity decay, and we extract multiple features strongly correlated with SOH as HFs. The extracted HFs are subjected to Weighted Principal Component Analysis (WPCA) to obtain indirect health features (IHFs), which achieve the estimation of SOH for LIBs. Finally, the effectiveness of the proposed modeling and health feature extraction method is validated by SOH estimation using the public dataset from Nature subseries. The results indicated that the EIS fitting accuracy R2 of the developed model is the best and the RMSE of SOH estimation is within 0.873 %.

A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes

Developing advanced engineering materials with enhanced mechanical and metallurgical properties has caused non-traditional machining (NTM) processes to emerge as cost-effective and energy-efficient options. Improper settings of the input parameters of those processes may often lead to inferior quality and accuracy of the finished products. Multi-criteria decision-making (MCDM) methods appear as potent tools for multi-objective optimization of those processes. Determining criteria weights is a crucial step in any MCDM problem; subjective approaches for evaluating criteria weights may result in inconsistent judgments. In this paper, the effects of ten different objective weighting methods, i.e., equal weight, entropy weight, Criteria Importance Through Inter-criteria Correlation (CRITIC), Method based on Removal Effects of Criteria (MEREC), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Symmetry Point of Criterion (SPC), Criteria Impact Loss (CILOS), Integrated Determination of Objective Criteria Weights (IDOCRIW), Principal Component Analysis (PCA) and Weighted Principal Component Analysis (WPCA) are studied on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-based optimization of two NTM processes. In the first example, the optimal parametric combination computed using equal weight, entropy, MEREC, LOPCOW, PCA, and WPCA is noticed to be the same. In contrast, a different set of optimal parameters is derived by applying CRITIC, SPC, CILOS, and IDOCRIW. In the second example, seven out of ten approaches obtain a unique set of parametric combinations, while CILOS, IDOCRIW, and PCA lead to a different set of intermixes. It can be concluded that the ranks of the experiment trials for both the examples are almost similar irrespective of the objective criteria weighting technique employed.

Measurement error prediction-based reliability assessment framework for electric metering devices under harsh natural environments

Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis

The efficient diagnosis of bearing faults requires the extraction of informative features. This paper presents a novel approach that combines Weighted Principal Component Analysis (WPCA) with the Gaussian Mixture Model (GMM) for bearing fault diagnosis. The method employs GMM as a fault classifier, aiming to enhance both efficiency and diagnostic accuracy. The proposed algorithm, Expectation Selection Maximization (ESM), introduces a feature selection step to identify the most relevant features for effective bearing fault detection. Specifically, the suggested algorithm utilizes the conditional entropy divergence indicator, a statistical metric, to quantify the significance of features in detecting bearing faults. To validate the effectiveness of this approach, two distinct case studies are conducted using datasets obtained from the University of Ottawa and Case Western Reserve University (CWRU). These datasets encompass a wide range of bearing working conditions, providing a comprehensive evaluation. Experimental results underscore the merits of the approach, achieving an average accuracy rate of 93% for the University of Ottawa dataset and 80% for the CWRU dataset. Furthermore, the findings highlight the superior performance of the proposed method compared to alternative techniques, as evidenced by the receiver operating characteristic (ROC) curve metric.

Multi-performance characteristics optimization in near-dry rotary EDM of AlSiC by weighted principal component analysis

In the current study, electrical discharge machining of particle reinforced aluminium matrix composite (AlSiC) using near-dry conditions is investigated. The investigation has been carried out by using de-ionized water as liquid phase and air as gaseous phase of a two-phase dielectric fluid. The gap between the work material and the tool is supplied with a little amount of de-ionized water droplets at a slow, steady pace using a minimum quantity lubrication (MQL) device. The process parameters included in the study are peak current, duty cycle, pulse-on time, tool rotation and air pressure. MRR (Material removal rate), TWR (Tool wear rate) and HT (Hole Taper) were selected as evaluation criteria. L27 (313) orthogonal array was used to conduct the experiments. The experimental results obtained were used in principal component analysis (PCA). The MPI (multi-response performance index), which combines these principal components and weighted principal component analysis (WPCA), is calculated as overall performance index. The approach is capable of forecasting optimal levels of machining factors. Significant factors and their contribution is determined by ANOVA performed for MPI. To validate the optimum parametric settings confirmatory experiment was conducted. Experimental results show that the responses in near dry rotary EDM process can be successfully improved using the suggested method.

Impacts of climate change on suitability zonation for potato cultivation in Jilin Province, Northeast China

Global climate change is causing notable shifts in the environmental suitability of the main regions involved in potato cultivation and has, thus, changed the production potential of potatoes. These shifts can be mapped at fine scales to better understand climate change within areas of potato cultivation and to find infrastructural- and breeding-based solutions. As a case study, we have identified and mapped the structural and spatial shifts that occurred in areas suitable for potato cultivation in Jilin Province, China. We identified a discontinuity in climate change trends between 1961 and 2018 based on data for Jilin Province, and analyzed the averages and linear trends for six important climatic parameters. We used the averages of these climatic parameters to establish climate models for the province and determined cultivation using a multi-criteria, decision-based model that integrates Analytical Hierarchy Process Weighted Principal Component Analysis (AHP-PCA) and Geographic Information System (GIS). We mapped the environmentally suitable areas for potato cultivation at a 3-km resolution based on the geo-climate model for each time period and analyzed differences between them. We found that "Most suitable” areas for potato cultivation were mainly distributed in the central area of Jilin Province, “Suitable” areas were located in the northwestern plains, and “Sub-suitable” areas were located in the eastern mountainous areas. In contrast, “Not suitable” areas occur mainly in the high-altitude areas in the east. The areas of “Most suitable” and “Suitable” areas for potato cultivation in Jilin Province were increasing, with increasing rates of 0.37 × 1,000 km2 decade−1 (R2 = 0.58, P < 0.01) and 0.20 × 1,000 km2 decade−1 (R2 = 0.28, P < 0.01), respectively, while the extent of “Sub-suitable” areas is decreasing, with a decreasing rate of 0.58 × 1,000 km2 decade−1 (R2 = 0.53, P < 0.05). The area of “Not suitable” areas had undergone little change. “Most suitable” and “Suitable” areas for potato cultivation showed a trend towards northward expansion. Overall, our results suggest that global climate change has had a positive impact on potato cultivation in Jilin Province over the past 58 years.

An OSM Contributors Classification Method Based on WPCA and GMM

Contributors have a significant impact on data quality of OpenStreetMap (OSM) because most of them are the non-professional, so clustering analysis of contributors based on different experiences has practical significance. Firstly, this paper obtained 31 behavioural characteristics of contributors from OSM historical data. Then, a weighted principal component analysis (WPCA) method was used to reduce the dimensions of the contributors’ behaviour in the selected region. By using an unsupervised prototype-based Gaussian mixture model (GMM) clustering algorithm, contributors with similar contribution attributes in the London area were clustered into four groups. Finally, the characteristics of four different types of contributors are analysed, and two types of experienced and professional contributors are found, who contribute a large amount of high-quality data.

Optimized Volterra filter equalizer based on weighted principal component analysis for IM-DD transmission.

Volterra filter equalizer (VFE) is a commonly-used nonlinear equalizer (NLE) to effectively mitigate both linear and nonlinear impairments arising in intensity-modulation direct-detection (IM-DD) transmission. However, the high calculation complexity of VFE becomes the bottleneck for the high-speed long-reach IM-DD transceiver. To achieve the hardware-efficient NLE, we propose an optimized VFE based on the weighted principal component analysis (WPCA), which uses both supervised weights and unsupervised PCA to extract principal components, so that the number of VFE taps can be significantly reduced without transmission penalty. It is experimentally verified that the proposed method can reduce 40% and 60% taps of traditional VFE, respectively, for C-band 80 Gb/s four-level pulse amplitude modulation (PAM-4) transmission over 20 km chromatic dispersion (CD)-uncompensated standard single-mode fiber (SSMF) and 56 Gb/s PAM-4 transmission over 100 km CD-managed SSMF.

Prediction of Oxygen Content Using Weighted PCA and Improved LSTM Network in MSWI Process

The accurate and real-time measurement of oxygen content in flue gas is the cornerstone of high incineration efficiency and economic benefits for municipal solid waste incineration (MSWI) plants. However, conventional hardware oxygen analyzers are difficult to obtain the oxygen content in flue gas timely and precisely. In this article, a weighted principal component analysis (WPCA) algorithm combined with improved long short-term memory (ILSTM) network is proposed for oxygen content prediction. First, to reduce the model complexity, a WPCA is developed to calculate mutual information correlation coefficients between principal components and the quality variable. Second, the LSTM network is exploited to establish a prediction model, and its hyperparameters are obtained with the particle swarm optimization (PSO) algorithm to improve the generalization ability of the prediction model. Finally, the effectiveness of the proposed prediction method is validated by a benchmark simulation and the real industrial data. And the comparison results with other methodologies demonstrate the considerable prediction performance of the proposed WPCA-ILSTM model.

Spatiotemporal Statistical Shape Model for Temporal Shape Change Analysis of Adult Brain.

This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject's age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer's disease (AD) identification utilizing support vector machine. In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.

Optimization Design of Electrochemical Machining Process of SKD11 Tool Steel Using Weighted Principal Component Analysis (WPCA)

This research was carried out on electrochemical machining (ECM) process using a workpiece material of SKD 11 tool steel and electrode of brass. Three process variables, i.e., voltage, electrolyte concentration and gap width with three levels for each process variables investigated. Based on the number of process variables and its level, an orthogonal array of L9 and two times replications employed in the design of the experiment. Setting a combination of significant machining parameters to maximize the material removal rate and minimize the surface roughness of the workpiece based on the results of optimization using the Taguchi method and weighted principal component analysis is a combination of voltage factors at level 3 of 48 V, electrolyte concentration at level 2 of 150 g/l, and gap width at level 1 of 1 mm. Machining parameter that has the greatest contribution is an electrolyte concentration which is 41.98%, then the contribution of voltage of 32.33%, and the gap width is 5.63%. Based on the results of confirmation experiments reveal that principal component analysis can effectively acquire the optimal combination of cutting parameters.

INTEGRASI METODE SAMPLE BOOTSTRAPPING DAN WEIGHTED PRINCIPAL COMPONENT ANALISYS (PCA) UNTUK MENINGKATKAN PERFORMA NAÏVE BAYES PADA CITRA TUNGGAL PAP SMEAR

Research on cervical cancer with the Pap Smear method is useful for finding pre-cancer diagnoses. Associated with previous research that the accuracy of the Naïve Bayes algorithm to the classification of a single Pap smear image still has an unsatisfactory accuracy. Whereas determining the class of single Pap cell smears is very important in determining whether these cells are normal or not. This study aims to determine whether integration using the Sample Bootstrapping (SB) method with the Weighted Principal Component Analysis (W-PCA) algorithm can improve the performance of the Naïve Bayes algorithm for seven different cell types. This model is the best solution used in the classification of datasets that are classified as having large dimensions. So that the integration of the two algorithms can increase the accuracy value to 87.24% for the seven classes and 97.30% for the two classes, and it can be concluded that with this integration model can improve the best accuracy value.

Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.

Investigation on hard turning temperature under a novel pulsating MQL environment: An experimental and modelling approach

Generation of total heat in hard turning largely influenced the cutting tool wear, tool life and finishing quality of work-surface. Thus, the measurement of this heat in terms of temperature becomes a necessity for achieving favourable machining performances. Therefore, this work presents a novel study on temperature measurement in three different zones during hard turning operation of 4340 grade steel under pulsating MQL environment. Temperatures are measured at three different locations namely chip-tool interface, flank face, and machined work surface (near to tool-work contact) and the location wise temperature is termed as chip tool interface temperature (T), flank face temperature (Tf) and machined work surface temperature (Tw) correspondingly. The temperature T and Tf are measured with help of K-type thermocouple while Tw is measured by Fluke make infra-red thermal camera. Pulsating MQL significantly reduced the temperature as the maximum temperature is noticed 110 °C which corresponds to chip-tool interface temperature (T) at highest speed (200 m/min) condition. In each test, the order of temperature follow the trend as: T &gt; Tf &gt; Tw. Considering average of all 16 temperatures, T is 14.42% greater than Tf and 39.36% larger than Tw while Tf is 21.79% greater than Tw. Experimental results concludes that the cutting speed is the most influencing factor followed by depth of cut for both T and Tf, whereas depth of cut is the most influencing factor for Tw. Further, these temperatures are predicted using linear regression, and absolute mean error (MAE) for responses T, Tf, and Tw is noticed as 1.848%, 0.542%, and 3.766% individually. Additionally, the optimum setting of input terms are estimated using WPCA (weighted principal component analysis) and found to bedc1(0.1 mm) − fr2(0.08 mm/rev) −vc2(100 m/min) − Pt2(2 s).

Pristup kombinaciji prirodom inspirisanog algoritma svitaca i ponderisane analize glavnih komponenata kod obrade legure Inconel X-750

Pristup kombinaciji prirodom inspirisanog algoritma svitaca i ponderisane analize glavnih komponenata kod obrade legure Inconel X-750

Multi-criteria Decision-Making Approaches to Agricultural Land Suitability Classification of Malda District, Eastern India

Land suitability classification (LSC) is an approach of land evaluation, which measures the degree of appropriateness of land for a specific land use. LSC is governed by a myriad of factors at the local and regional level including physiographic, pedologic and a host of socioeconomic and infrastructural determinants. This has called for the application of different multi-criteria decision-making (MCDM) techniques in agricultural LSC. The present study has attempted and compared various MCDM-based agricultural LSCs for Malda District in Eastern India. The study is based on multiple parameters governing agriculture, considering not only the physiographic and pedological attributes (e.g., relief, slope, soil fertility, soil organic carbon, etc.) but also the socioeconomic ones (e.g., the percentage of people engaged in agriculture, cultivator–labor ratio, degree of electrification, etc.). Four major MCDM algorithms have been applied, i.e., composite ranks, composite Z-scores, analytical hierarchy process (AHP) and weighted principal component analysis (WPCA). The results were also compared with the crop productivity-based agricultural efficiency. It was observed that about 15.44% of the area of Malda District is highly suitable for agriculture, whereas limited suitability is displayed by about 12.68% of area. The remaining part falls under moderate and marginal suitability classes. Furthermore, WPCA and AHP are superior to the nonparametric techniques of MCDM, namely composite ranks and composite Z-score. Moreover, the results of WPCA are superior to those of AHP. Due to the inherent limitations of the AHP approach, this study proposes the use of WPCA in the domain of agricultural LSC.

Hybrid descriptors and Weighted PCA-EFMNet for Face Verification in the Wild

Unconstrained face recognition is a complex task because of the numerous intra-class changes caused by lighting, occlusion, facial expressions and poses changes. Such variations affect considerably the performance of facial recognition systems, particularly those based on 2D information. In this research work, an efficient feature extraction method, called Gabor Local Binarized Statistical Image Features, is introduced. A new deep learning network is also developed to extract features relying on data processing components: (1) Cascaded Weighted Principal Component Analysis (WPCA) with Enhanced Fisher Model (WPCA-EFM); (2) binary hashing; and (3) Block-wise Histograms. The suggested architecture (Weighted PCA-EFM) is employed to learn multistage filter banks. Simple block histogram and binary hashing are applied for indexing and pooling, which facilitate the design and learning of our architecture for Face Verification in the Wild. Classification is finally performed using distance measure Cosine and support vector machine. Our experiments are carried out in real-world dataset: Labeled Faces in the Wild. Experimental findings demonstrate that the developed technique achieved high accuracy of 95%.

ACCURACY AND SURFACE INTEGRITY ANALYSIS OF MICRO HOLES DRILLED ON HASTELLOY-X ALLOY USING A LASER SOURCE WITH OPTIMIZATION

Hastelloy-X is a nickel-based superalloy used for making parts of extreme temperature applications. This paper is aimed at studying the effects of laser drilling parameters on accuracy and surface characteristics of the micro-holes drilled on Hastelloy-X alloy. The independent factors like power, pulse width, pulse repetition rate and gas pressure were selected as process variables. The experiments were designed using response surface methodology (RSM) with four factors at five levels. The process was evaluated in terms of exit diameter, entrance diameter, taper and entrance circularity error along with the surface topography analysis of the hole. Weighted principal component analysis (WPCA) is performed to calculate the accuracy of a hole through the geometric index (GI). It also determines the optimal combination among the alternatives. Relationship of GI with input parameters was established with RSM and analysis of the variance (ANOVA) was done to enact the significance of process parameters on the outputs. The obtained results proved that power, gas pressure and pulse width are the most influencing factors of accuracy. The desirability analysis also confirmed the optimal input sequence that has been obtained from the RSM–WPCA approach.

A weighted principal component analysis approach for online quality determination of biodiesel using spectrophotometry data

ABSTRACT Biodiesel fuels deteriorate when exposed to air or high temperatures. In this study, biodiesel was produced through transesterification of sunflower oil and kept at 25, 45, and 70°C for 28 days. The spectrophotometry graphs of the samples were prepared and used as inputs to three classification schemes of linear discrimination analysis, principal component analysis (PCA), and weighted PCA (WPCA) to categorize the fuels into three groups, based on their acid values. The resulting average recognition accuracies were 82%, 84%, and 96% for the three classifiers, respectively. The analysis of the classifiers outputs indicated that the wavelengths between 200 and 240 nm had the higher significant role in determining the quality of the fuels. The model assessment and error analysis of the results were performed using Hotelling T 2 statistic and Q-residual index. The analysis indicated the WPCA model, at a 5% significance level, had higher power for accurately determining the quality of biodiesel samples using their spectrophotometry data.