Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

RELATIVE PRINCIPLE COMPONENT AND RELATIVE PRINCIPLE COMPONENT ANALYSIS ALGORITHM

In this letter,the new concept of Relative Principle Component (RPC) and method of RPC Analysis (RPCA) are put forward. Meanwhile,the concepts such as Relative Transform (RT),Ro-tundity Scatter (RS) and so on are introduced. This new method can overcome some disadvantages of the classical Principle Component Analysis (PCA) when data are rotundity scatter. The RPC selected by RPCA are more representative,and their significance of geometry is more notable,so that the application of the new algorithm will be very extensive. The performance and effectiveness are simply demonstrated by the geometrical interpretation proposed.

A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression

There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.

Establishment of HPLC Fingerprint, Cluster Analysis and Principle Component Analysis of Citri Reticulatae Pericarpium Viride

[Objectives] This study aimed to establish HPLC fingerprint and conduct cluster analysis and principle component analysis for Citri Reticulatae Pericarpium Viride. [Methods] Using the HPLC method, the determination was performed on XSelect~® HSS T3-C_(18) column with mobile phase of acetonitrile-0.5% acetic acid solution(gradient elution) at the flow rate of 1.0 mL/min. The detection wavelength was 360 nm. The column temperature was 25℃. The sample size was 10 μL. With peak of hesperidin as the reference, HPLC fingerprints of 10 batches of Citri Reticulatae Pericarpium Viride were determined. The similarity of the 10 batches of samples was evaluated by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012 edition) to determine the common peaks. Cluster analysis and principal component analysis were performed by using SPSS 17.0 statistical software. [Results] The HPLC fingerprints of the 10 batches of medicinal materials had total 11 common peaks, and the similarity was 0.919-1.000, indicating that the chemical composition of the 10 batches of medicinal materials was consistent. There were 11 common components in the 10 batches of medicinal materials, but their contents were different. When the Euclidean distance was 20, the 10 batches of samples were divided into two categories, S4 in the first category, and the others in the second one. When the Euclidean distance was 5, the second category could be further divided into two sub-categories, S1 and S10 in one sub-category, and S2, S3, S5, S6, S7, S8 and S9 in the other one. The principle component analysis showed that cumulative contribution rate of the two main component factors was 92.797%, and the comprehensive score of S7 was the highest with the best quality. [Conclusions] The results of HPLC fingerprinting, cluster analysis and principle component analysis can provide reference for the quality control of Citri Reticulatae Pericarpium Viride.

Celiac Disease Seen with the Eyes of the Principle Component Analysis and Analyse Des Données

This paper aims to deepen the quality of life of people with celiac disease with a focus on compliance to the diet through Principle Component Analysis and Analyse des Données. In particular, we will try to understand whether these analyzes are also applicable in the context of research web2.0 carried out with web-survey.

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.

Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

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.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Correlation and Principal Component Analysis on Main Agronomic Traits of New Waxy Corn Varieties

[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Modified algorithm of principal component analysis for face recognition

In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.

Comparative assessment of the frying efficiency of standard and low linolenic rapeseed oils: Principal Component Analysis (PCA)

In this research,the performance of regular rapeseed oil(RSO)and modified low-linolenic rapeseed oil(LLRO)during frying was assessed using a frying procedure that commonly found in fast-food restaurants.Key physicochemical attributes of these oils were investigated.RSO and LLRO differed for initial linolenic acid(12.21%vs.2.59%),linoleic acid(19.15%vs.24.73%).After 6 successive days frying period of French fries,the ratio of linoleic acid to palmitic acid dropped by 54.49%in RSO,higher than that in LLRO(51.54%).The increment in total oxidation value for LLRO(40.46 unit)was observed to be significantly lower than those of RSO(42.58 unit).The changes in carbonyl group value and iodine value throughout the frying trial were also lower in LLRO compared to RSO.The formation rate in total polar compounds for LLRO was 1.08%per frying day,lower than that of RSO(1.31%).In addition,the formation in color component and degradation in tocopherols were proportional to the frying time for two frying oils.Besides,a longer induction period was also observed in LLRO(8.87 h)compared to RSO(7.68 h)after frying period.Overall,LLRO exhibited the better frying stability,which was confirmed by principal component analysis(PCA).

Correlation Analysis Between Yield Components and Yield Per Plant of Double-low Hybrid Rapeseed

[Objective] The experiment aimed to study the relation between yield components and yield per plant of double-low hybrid rapeseed and provide reference for breaking yield limitation of rapeseed and culturing new double-low hybrid rapeseed variety. [Method]The yield components and yield per plant of two cross combination of double-low hybrid rapeseed (B02, D04) and Shuza 6 were correlatively analyzed and compared, besides, the path analysis was also carried on to them. [Result] Among B02, D04 and Shuza No.6, effective pod number per plant and seeds per silique, seeds per pod and 1 000-grain weight were all negative correlation. In high yield hybrid, pod number per plant, seeds per pod had more impaction on yield per plant than 1 000-grain weight and the difference was at 0.01 significant level. In the control variety Shuza No.6, the impactions of pod number per plant and seeds per pod on yield per plant were bigger than that of 1 000-grain weight on yield per plant, however, the difference was not significant. [Conclusion] The improvement of effective pod number per plant was an important aim of breeding work of double low rapeseed breeding in Sichuan ecological region.

Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time applications.This paper presents an approach based on state-of-the-art machine-learning methods.In this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

Correlation, Principal Component and Grey Relation Analysis of Sweetpotato Root Biological Traits

[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 sweetpotato cultivars were measured through hydroponic culture in a greenhouse and field survey, and then their correlations were analyzed by statistical methods. [Result] The root morphological traits of sweetpotato at seedling stage such as projected area, surface area, average diameter and volume processed the highest contribution rate （80.56%） 10 d after transplanting, and the contribution rate of root average diameter reached 27.79% 20 d after transplanting. Storage root fresh weight per plant shared extremely significant positive correlations with storage root fresh weight of penultimate node and storage root fresh weight of antepenultimate node, and a significant positive corre- lation with commercial storage root number, and a significant negative correlation with storage root number of penultimate node. Among them, the correlation coeffi- cient of storage root fresh weight per plant with storage root fresh weight of antepenultimate node was the highest （0.659 5）. Fifteen days after transplanting, storage root fresh weight per plant had significant negative correlations with root projected area, surface area and volume. There was a significant positive correlation between root dry weight and storage root fresh weight per plant 25 d after transplanting. Root dry weight, volume, length, average diameter of sweetpotato seedlings had higher relational degrees with storage root fresh weight per plant. Ten and twenty days after transplanting were important time for the growth and differentiation of sweetpotato roots. In addition, node length and planting depth had certain influence on sweetpotato yield, and direct relationship existed between the seedling root biological traits and storage root yield of sweetpotato. [Conclusion] The results provide theoretical support for standard cultivation and new variety breeding of sweetpotato.

Study on Trace Elements in Rehmannia glutinosa Libosch. by Principal Component Analysis and Clustering Analysis

[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.

Kernel principal component analysis network for image classification

In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.