Estimation of the Number of Collapsed Houses Damaged by Typhoon Based on Principal Components Analysis and Support Vector Machine

The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.

Determination of bioactive components in the fruits of Cercis chinensis Bunge by HPLC-MS/MS and quality evaluation by principal components and hierarchical cluster analyses

The fruits of leguminous plants Cercis Chinensis Bunge are still overlooked although they have been reported to be antioxidative because of the limited information on the phytochemicals of C.chinensis fruits.A simple,rapid and sensitive HPLC-MS/MS method was developed for the identification and quantitation of the major bioactive components in C.chinensis fruits.Eighteen polyphenols were identified,which are first reported in C.chinensis fruits.Moreover,ten components were simultaneously quantified.The validated quantitative method was proved to be sensitive,reproducible and accurate.Then,it was applied to analyze batches of C.chinensis fruits from different phytomorph and areas.The principal components analysis(PCA)realized visualization and reduction of data set dimension while the hierarchical cluster analysis(HCA)indicated that the content of phenolic acids or all ten components might be used to differentiate C.chinensis fruits of different phytomorph.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

QUALITY CONTROL OF SEMICONDUCTOR PACKAGING BASED ON PRINCIPAL COMPONENTS ANALYSIS

5 critical quality characteristics must be controlled in the surface mount and wire-bond process in semiconductor packaging. And these characteristics are correlated with each other. So the principal components analysis（PCA） is used in the analysis of the sample data firstly. And then the process is controlled with hotelling T^2 control chart for the first several principal components which contain sufficient information. Furthermore, a software tool is developed for this kind of problems. And with sample data from a surface mounting device（SMD） process, it is demonstrated that the T^2 control chart with PCA gets the same conclusion as without PCA, but the problem is transformed from high-dimensional one to a lower dimensional one, i.e., from 5 to 2 in this demonstration.

Determination of magnesium and aluminum content as principal components in pyrotechnic compositions used for fireworks and firecrackers based on inductively coupled plasma optical emission spectrometric approach （ICP-OES）

Inductively coupled plasma optical emission spectrometric approach（ICP-OES）is used to determine the magnesium and aluminum content as principal components in pyrotechnic compositions used for fireworks and firecrackers. Elements of magnesium and aluminum are commonly found in aluminum powder or magnesium-aluminum alloy powder in pyrotechnic compositions in fireworks and firecrackers. Statistical analysis shows that the magnesium content in pyrotechnics is between 8% to 30% and the aluminum content is between 8% to 35%（roughly）.Concept of this method： suppose the weight of the sample is 400rag,constant volume is IL and the concentlation of magnesium and aluminum is between 12mg/L to 160mg/L in sample solution, the determination scope of the method for magnesium and aluminum content would be between 3% to 40%.Further experiments proved that the fitting correlation coefficient of the magnesium calibration curve is 0.9999 or higher, recovery is 101.01% -101.96%.The fitting correlation coefficient of the aluminum calibration curve is 0.9999 or higher, recovery is 99.36%-103.07%. The allowable differential value is 0.4% between two single tests under repeatable conditions. This method can completely satisfy the requirements of the fireworks and firecrackers industry with high accuracy and good precision.

＂Principal Components＂ for the Calculation of the Best Estimate Liability of Future Discretionary Benefits under Solvency II

The core of the presentation that I propose treats about the application of principal component analysis for the estimation of the future earnings of the Segregated Funds. The Segregated Funds are the pools of assets which have been using in the Italian Insurance Market since mid 80＇s for managing the ＂with profit＂ business, also known as business with ＂discretionary participation feature＂ according to IFRS4 since the income attributable to the policyholders is composed by the net realised gains less a fee and by a minimum guarantee, if any. The discretion of the Management of the Entity lays on the decision on whether and when to realise both the financial gains and the financial losses of the underlying investments. This strategy is tied by the need to manage an appropriate ALM, from the rules included in the local legislation as well as by the policy about the solvency margin （for example a minimum solvency ratio to fulfil that is conditional upon the value of income attributed that year）. The fee can not be changed, i.e., is not discretionary, the minimum granted could be fixed on annual basis-sometimes paid on cash recurrently-or could be promised only at expiration or sometimes promised at expiration or in advance only for death and disability. In case of years so adverse that the Entity is not able to avoid earnings too low, sometimes the entity delivers an additional bonus, in order to compensate their policyholders, which is based above a full discretion. Principal components are used in finance as well as in other fields such as genetic. You can see some of these applications in two of the references advised below. The purposes of the technique of principal components are （1） to increase objectivity of results and their verifiability by third parties such as auditors, （2） make speeder the time spent for the analytical calculations （i.e., runs of the actuarial models） and （3） to render the relationship between the asset composition and the their earnings effectively used for the best estimate liabilities through their contribution in forecasting future cash flows.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Improved performance of process monitoring based on selection of key principal components

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Determination of potassium content as principal components in pyrotechnic compositions used for fireworks and firecrackers based on inductively coupled plasma optical emission spectrometric approach （ICP-OES）

Inductively coupled plasma optical emission spectrometric approach（1CP-OES） is used to determine the potassium content as principal component in pyrotechnic compositions used for fireworks and firecrackers. Element of potassium is conunonly found in potassium nitrate and potassium perchlorate in pyrotechnic compositions in fireworks and firecrackers. Statistical analysis shows that potassium nitrate content in pyrotechnics is between 10% to 60% and the potassium perchlorate content is between 20% to 70%,which counted in the content of potassium element is between 4% to 23%. Concept of this method： considering the weight of the sample is 400rag,constant volume is 1L and the concentration of potassium is between 10 mg/L to 90 mg/L in sample solution, the determination scope of the method for the potassium content would be between 1% to 23%.Further experiments proved that the fitting correlation coefficient of potassium calibration curve is 0.9997 or higher, recovery is 89.15%-100.23%.The allowable differential value is 0.4% between two single tests under repeatable conditions. This method can completely satisfy the requirements of the fireworks and firecrackers industry with high accuracy and good precision.

Optimized ANN for LiFePO_(4) battery charge estimation using principal components based feature generation

Electric vehicles(EVs)have gained prominence in the present energy transition scenario.Widespread adoption of EVs necessitates an accurate State of Charge estimation(SoC)algorithm.Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle technology.In this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation algorithms.The investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup.The voltage,current and open-circuit voltage of the battery are monitored with computerized battery analyzer.The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi.Principal components are derived for the collected battery data set and analyzed for feature engineering.Three principal components were generated as input parameters for the developed ANN.Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN.While considering eleven combinations for ten different optimizers loss function is minimized.Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998.The proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level dataset.The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

Principal components of nuclear mass models

Principal component analysis(PCA)is employed to extract the principal components(PCs)present in nuclear mass models for the first time.The effects from different nuclear mass models are reintegrated and reorganized in the extracted PCs.These PCs are recombined to build new mass models,which achieve better accuracy than the original theoretical mass models.This comparison indicates that using the PCA approach,the effects contained in different mass models can be collaborated to improve nuclear mass predictions.

Power analysis of principal components regression in genetic association studies

Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression （PCR）-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components （PCs） selected from single nucleotide polyrnorphisms （SNPs） may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom （DF）. Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.

Principal components methodology-A novel approach to forecasting production from liquid-rich shale(LRS)reservoirs

With increasing global demand for energy,the importance of unconventional shale oil and gas research cannot be over-emphasized.The oil and gas industry requires rapid and reliable means of forecasting production.Existing traditional decline curve analysis(DCA)methods have been limited in their ability to satisfactorily forecast production from unconventional liquid-rich shale(LRS)reservoirs.This is due to several causes ranging from the complicated production mechanisms to the ultra-low permeability in shales.The use of hybrid(combination)DCA models can improve results.However,complexities associated with these techniques can still make their application quite tedious without proper diagnostic plots,correct use of model parameters and some knowledge of the production mechanisms involved.This work,therefore,presents a new statistical data-driven approach of forecasting production from LRS reservoirs called the Principal Components Methodology(PCM).PCM is a technique that bypasses a lot of the difficulties associated with existing methods of forecasting and forecasts production with reasonable certainty.PCM is a data-driven method of forecasting based on the statistical technique of principal components analysis(PCA).In our study,we simulated production of fluids with different compositions for 30 years with the aid of a commercial compositional simulator.We then applied the Principal Components Methodology(PCM)to the production data from several representative wells by using Singular Value Decomposition(SVD)to calculate the principal components.These principal components were then used to forecast oil production from wells with production histories ranging from 0.5 to 3 years,and the results were compared to simulated data.Application of the PCM to field data is also included in this work.

Comparison of Principal Components Analysis,Independent Components Analysis and Common Components Analysis

The aim of this work is to describe and compare three exploratory chemometrical tools,principal components analysis,independent components analysis and common components analysis,the last one being a modification of the multi-block statistical method known as common components and specific weights analysis.The three methods were applied to a set of data to show the differences and similarities of the results obtained,highlighting their complementarity.

The Pre－test Principal Components Estimator in the Two Seemingly Unrelated Regression System

For the two seemingly unrelated regression system, this paper proposed a new type of estimator called pre-test principal components estimator (PTPCE) and discussed some properties of PTPCE.

Molecular dynamics and principal components of potassium binding with human telomeric intra-molecular G-quadruplex

Telomere assumes intra-molecular G-quadruplex that is a significant drug target for inhibiting telomerase main- tenance of telomeres in cancer. Metal cations have been recognized as playing important roles in stabilizing G-quadruplex, but their binding processes to human telomeric G-quadruplex remain uncharacterized. To in- vestigate the detailed binding procedures, molecular dynamics simulations were conducted on the hybrid [3 ＋ 1] form-one human telomeric intra-molecular G-quadruplex. We show here that the binding of a potas- sium ion to a G-tetrad core is mediated by two alternative pathways. Principal component analysis illustrated the dominant concerted motions of G-quadruplex occurred at the loop domains. MM-PBSA calculations revealed that binding was energetically favorable and driven by the electrostatic interactions. The lower binding site was found more constructive favorable for binding. Our data provide useful information on a potassium-mediated stable structure of human telomeric intra-molecular G-quadruplex, implicating in ion disorder associated conformationa｜ changes and targeted drug design.

Improve Survival Prediction Using Principal Components of Gene Expression Data

The purpose of many microarray studies is to find the association between gene expression and sample characteristics such as treatment type or sample phenotype. There has been a surge of efforts developing different methods for delineating the association. Aside from the high dimensionality of microarray data, one well recognized challenge is the fact that genes could be complicatedly inter-related, thus making many statistical methods inappropriate to use directly on the expression data. Multivariate methods such as principal component analysis （PCA） and clustering are often used as a part of the effort to capture the gene correlation, and the derived components or clusters are used to describe the association between gene expression and sample phenotype. We propose a method for patient population dichotomization using maximally selected test statistics in combination with the PCA method, which shows favorable results. The proposed method is compared with a currently well-recognized method.

Learning robust principal components from L1-norm maximization

Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA-REM) since conventional L2-norm based PCA(L2-PCA) is sensitive to outliers.Recently,the variance maximization formulation of PCA with L1-norm(L1-PCA-VM) has been proposed,where new greedy and nongreedy solutions are developed.Armed with the gradient ascent perspective for optimization,we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation,which are verified by experiments on synthetic and real-world datasets.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.