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.

Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process

Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

DYNAMIC VARIATIONS OF WATER QUALITY IN TAIHU LAKE AND MULTIVARIATE ANALYSIS OF ITS INFLUENTIAL FACTORS

Dynamic variation of water quality in Meiliang Bay and part of West Taihu Lake has been analysed based on data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various factors. It is shown that there existis an obvious spatial and temporal variation in the main factors of water quality. Annual values of TP, CON, TN, Chl-a and conductivity decrease evidently from inner Meiliang Bay to the outer from north to south. TP and TN fluctuate seasonally with much higher value in winter. This is particularly true for the mouth of Liangxi River. In addition, the Chl-1 has a synchronous variation with water temperature, although being lagged a little, and closely relates to TP and TN. Finally, the results from Principal Component Analysis show that TP, TN, SS (or SD), water temperature and Chl-a are the most influential factors to water qualuty in this area, and both suspensions and algae can contribute to transparency to Taihu Lake.

DYNAMIC VARIATIONS OF WATER QUALITY IN TAIHU LAKE AND MULTIVARIATE ANALYSIS OF ITS INFLUENTIAL FACTORS

ＤＹＮＡＭＩＣ ＶＡＲＩＡＴＩＯＮＳ ＯＦ ＷＡＴＥＲ ＱＵＡＬＩＴＹ ＩＮ ＴＡＩＨＵ ＬＡＫＥ ＡＮＤ ＭＵＬＴＩＶＡＲＩＡＴＥ ＡＮＡＬＹＳＩＳ ＯＦ ＩＴＳ ＩＮＦＬＵＥＮＴＩＡＬ ＦＡＣＴＯＲＳＤＹＮＡＭＩＣＶＡＲＩＡＴＩＯＮＳＯＦＷＡＴＥ...

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

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.

Crustal structure study based on principal component analysis of receiver functions

The receiver function(RF) technique is an effective method for studying crustal structure. For a single station, the average 1-D crustal structure is usually derived by stacking the radial RFs from all back-azimuths, whereas structural variations(such as dipping discontinuities or anisotropy) can be constrained through analysis of waveform dependence on the backazimuth of both the radial and tangential RFs. However, it is often difficult to directly extract information about structural variations from the waveform of RF, due to the common presence of noise in real data. In this study, we proposed a new method to derive structural variation information for individual stations by applying principal component analysis(PCA) to RFs sorted by back-azimuth. In this method(termed as RF-PCA), a set of principal components(PCs), which are uncorrelated with each other and reflect different characteristics of the RF data, were extracted and utilized separately to reconstruct new RFs. Synthetic tests show that the first PC of the radial RFs contains the average structural information of the crust beneath the corresponding station, and the second PC of the radial RFs and the first PC of the tangential RFs both reflect the variations of the crustal structure. Our synthetic modeling results indicate that the new RF-PCA method is valid for a variety of synthetic models with intra-crustal dipping discontinuities and/or anisotropy. We applied this method to the real data from a broadband temporary seismic station(s233) in the central part of the Sichuan Basin. The results suggest that the RF data can be best explained by the presence of two nearly parallel dipping discontinuities within the crust. Combining with previous logging data, seismic exploration and deep sounding observations, we interpret the shallow dipping discontinuity as the top boundary of the Precambrian crystalline basement of the Sichuan Basin and the deep one corresponding to the Conrad interface between the upper and lower crust, consistent with the geological feature of the study area. In this work, both synthetic tests and real data application results demonstrate the effectiveness of the RF-PCA method for studying crustal structures.

A Multi-level Approach for Complex Fault Isolation Based on Structured Residuals

In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.

Genetic diversity and population structure of Commelina communis in China based on simple sequence repeat markers

Commelina communis（Asiatic dayflower） is a troublesome weed in China. Genetic variation of 46 C. communis populations from different collection sites in our country was investigated using 12 simple sequence repeat（SSR） primer pairs. Polymorphism analysis results showed high level of genetic diversity among these populations. The alleles（bands） were amplified by these primer pairs. The polymorphic proportion was 18.25%, and the average polymorphism information content was 0.1330. The highest effective number of alleles was 1.9915 at locus YP33, and the lowest value was 1.0000 at both loci YP25 and YP31. C. communis showed major average observed heterozygosity value（0.8655） than that of average expected heterozygosity（0.1330）. C. communis populations were divided into three groups on the basis of unweighted pair-group method with arithmetic mean cluster analysis（Dice genetic similarity coefficient=0.772） and genetic structure analysis（K=3）, and a principal coordinate analysis. The results of this study further illustrated that C. communis populations contained abundant genetic information, and the 12 SSR markers could detect the microsatellite loci of C. communis genomic DNA. These results might indicate that C. communis maintains high genetic diversity among different populations.

Structured illumination microscopy based on principal component analysis

Structured illumination microscopy(SIM)is one of the powerful super-resolution modalities in bioscience with the advantages of full-field imaging and high photon efficiency.However,artifact-free super-resolution image reconstruction requires precise knowledge about the illumination parameters.The sample-and environment-dependent on-the-fly experimental parameters need to be retrieved a posteriori from the acquired data,posing a major challenge for real-time,long-term live-cell imaging,where low photobleaching,phototoxicity,and light dose are a must.In this work,we present an efficient and robust SIM algorithm based on principal component analysis(PCA-SIM).PCA-SIM is based on the observation that the ideal phasor matrix of a SIM pattern is of rank one,leading to the low complexity,precise identification of noninteger pixel wave vector and pattern phase while rejecting components that are unrelated to the parameter estimation.We demonstrate that PCA-SIM achieves non-iteratively fast,accurate(below 0.01-pixel wave vector and 0.1%of 2relative phase under typical noise level),and robust parameter estimation at low SNRs,which allows real-time super-resolution imaging of live cells in complicated experimental scenarios where other state-of-the-art methods inevitably fail.In particular,we provide the open-source MATLAB toolbox of our PCA-SIM algorithm and associated datasets.The combination of iteration-free reconstruction,robustness to noise,and limited computational complexity makes PCA-SIM a promising method for high-speed,long-term,artifact-free super-resolution imaging of live cells.

Dynamic model for predicting nitrogen oxide concentration at outlet of selective catalytic reduction denitrification system based on kernel extreme learning machine

To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.

Genetic Diversity and Genetic Structure of Maize Inbred Lines from Yunnan Revealed by SNP Chips

[Objectives]The genetic diversity and population genetic structure of 107 inbred lines of maize in Yunnan were analyzed,in order to provide technical support for maize germplasm innovation,genetic improvement of germplasm resources,variety management,and lay a solid foundation for exploring genes related to fine traits in the future.[Methods]The 107 maize inbred lines generalized in Yunnan were selected,and 45 backbone inbred lines commonly used in China were used as reference for heterotic group classification.On Axiom Maize 56K SNP Array platform,maize SNP chips(56K)were used to scan the whole maize genome,and the NJ-tree model of Treebest was used to construct a phylogenetic tree.Principal component analysis(PCA)was conducted by GCTA(genome-wide complex trait analysis)to reveal the genetic diversity and population genetic structure.[Results]In the 107 Yunnan local inbred lines,5533 uniformly distributed high-quality SNP marker sites were finally detected.Based on the analysis of these SNP marker sites,Nei s gene diversity index(H)of 107 maize germplasm genes was 0.2981-0.5000 with an average value being 0.4832,and polymorphism information content(PIC)values were 0.2536-0.3750 with an average value being 0.3662.The minimum allele frequency value was 0.5000-0.8178 with an average value being 0.5744.The analysis of population genetic structure showed that when K=6,the maximum value of△K was the maximum,which meant that the inbred lines used in this study could be divided into six groups.They were Tangsi Pingtou blood relationship group,PB blood relationship group,335 female blood relationship group,Zi 330 and the Lüda Honggu blood relationship group,unknown group 1 and unknown group 2.No inbred lines were divided into other heterotic groups.Among them,37 inbred lines from the 2 unknown groups could not be classified into the same group as the 10 known heterotic groups in China.The results of principal component analysis showed that the 107 maize inbred lines generalized in Yunnan could be clearly distinguished from the backbone maize inbred lines commonly used in China.Most of the maize inbred lines in Yunnan were concentrated near the reference backbone inbred lines.But some Yunnan inbred lines were far away from the reference inbred lines commonly used in China.[Conclusions]The maize germplasm resources in Yunnan area were rich in genetic diversity,including multiple heterotic groups,and there was a rich genetic basis of breeding parents.They could be clearly distinguished from the backbone inbred lines commonly used in China,and some of them had a long genetic distance from the backbone inbred lines.The resources which have good application potential can be used to create new heterotic groups.

New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors, HSEHPCSV （component score vector of hydrophobic, steric, and electronic properties together with hydrogen bonding contributions）, were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described, namely, hydrophobic, steric, and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors, bitter tasting thresholds and bactericidal 18 peptide, and to construct QSAR models based on partial least square （PLS）. The results obtained are as follows： the multiple correlation coefficient （R2cum） of 0.846, 0.917 and 0.993, leave-one-out cross validated Q2cm of 0.835, 0.865 and 0.899, and root-mean-square error for estimated error （RMSEE） of 0.396, 0.187and 0.22, respectively. Satisfactory results showed that, as new amino acid scales, data of HSEHPCSV may be a useful structural expression methodology＇for the studies on peptide QSAR （quantitative structure-activity relationship） due to many advantages such as plentiful structural information, definite physical and chemical meaning and easy interpretation.

DYNAMICS OF REGIONAL URBANIZATION IN FUJIAN COASTAL AREA

Since the economic reform in 1978, urban development in China has become much more rapid and the dynamic mechanisms of urbanization more diversified. The 'Bottom Up' strategy becomes as important as, or even more important than, the 'Top Down' strategy as the dynamic mechanisms of regional urbanization. On the basis of major theories of development economics and regional economics, this paper analyzes the major dynamic mechanisms of regional urbanization in coastal area of Fujian Province from 1978 to 1989, and describes quantitatively the territorial differentiation of regional urbanization process under two major dynamic mechanisms using principal componet analysis.

Insight into the Urea Binding and K166R Mutation Stabilizing Mechanism of TIpB： Molecular Dynamics and Principal Component Analysis Study

Chemoreceptor TlpB（Tlp=transducer-like protein）, which has been demonstrated to respond to pH sensing function, is crucial for the survival ofHelicobacterpylori（H, pylori） in host stomach. Urea was proposed to be essen- tial for TlpB＇s pH sensing function via binding with the Per-ARNT-Sim（PAS） domain of TlpB. Additionally, KI66R mutation of the TlpB protein has also been proven to have a similar effect on TlpB pH sensing as urea binding. Al- though X-ray crystallographic studies have been carried out for urea-bound Tlpl3, the molecular mechanism for the stabilization of TIpB induced by urea binding and K166R mutation remains to be elucidated. In this study, molecular dynamics simulations combined with principal component analysis（PCA） for the simulation results were used to gain an insight into the molecular mechanism of the stabilization of urea on TlpB protein. The formed H-bonds and salt-bridges surrounding Aspll4, which were induced by both urea binding and K166R mutation of TIpB, were im- portant to the stabilization of TlpB by urea. The similarity between the urea binding and K166R mutation as well as their differences in effect has been explicitly demonstrated with computer simulations at atomic-level. The findings may Dave the wav for the further researches of TlpB.

Ecological distribution and population structure of Litsea glaucescens(Lauraceae)in the ravines of Sierra Fría and Sierra Laurel,Central Mexico

Litsea glaucescens Kunth(Mexican bay leaf,laurel)has a wide distribution in Mexico,growing at both riparian and rupicolous environments in the mountainous region of the Central Highlands of the country.Sierra Fría-Sierra Laurel is a protected natural area covered by a dry forest.The Mexican bay leaf is associated with the oak forest,especially on ravines.The species has been considered at risk in recent years.This research is focused on analyzing the elements of the environment of the ravines,which are influencing the distribution and establishment of laurel populations in the region.Two mountainous regions of Aguascalientes were selected,Sierra Fría and Sierra Laurel.Three ravines of the basin were selected to obtain environmental data.Variables registered were topographic,edaphic,and biotic.Principal component analysis was used to identify ecological factors associated with the presence of L.glaucescens.Mexican bay leaf populations were registered in 10 ravines.At the structural level in the community,29 woody species were registered,Mexican bay leaf had an Importance Value Index of 15.8,ranking 10th among all species.Individuals of laurel were classified by size classes(S,individuals with heights ranging from 0 to 20 cm;S,heights ranging from 0.2 to 1.0 m;S,heights between 1 and 2 m with light trace of flowering;S,heights greater than 2 m with flowering greater than 30% of the canopy;and S,individuals with heights greater than 5 m,curved trunk and basal regrowth)to obtain the population structure.The importance index value for all the species in the riparian community was calculated to the community level.Edaphic factors that characterized the presence of Mexican bay leaf were a high percentage of rock coverage(90%),less mulch depth,and sandy loam shallow soils.Sites that showed higher cation exchange capacity had a higher presence of individuals of the S,S,and Ssize classes.Class Sindividuals were found in shady places with 97% of intercepted light.Individuals of classes Sand Sendure less shady places(75%–85% of intercepted light),and individuals class Sand Sare more frequent in open canopies and crag conditions.Regarding the ecological site factors,such as riverside stream,and rocks on mountain slopes,L.glaucescens life form is riparian and rupicolous.Cation exchange capacity,sodium and calcium levels play an important role in the presence of Mexican bay leaf.Distribution on the ravine and recruitment of the Mexican bay leaf populations are associated with shaded sites,mainly for individuals of size classes Sand S,versus sunny places for individuals of size classes Sand S.The overall population structure had a positive kurtosis with all plant size categories well represented;statistically,the population structure of L.glaucescens is very close to the normal distribution.The information obtained allows us to affirm that the laurel populations in the mountainous areas of Sierra Fría and Sierra Laurel from central Mexico are in good demographic condition.

Rapid bacteria identification using structured illumination microscopy and machine learning

Traditionally,optical microscopy is used to visualize the morphological features of pathogenic bacteria,of which the features are further used for the detection and ident ification of the bacteria.However,due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification,the ffectiveness of this optical microscopy-based method is limited.Here,we reported a pilot study on a combined use of Structured Illumination Microscopy(SIM)with machine learning for rapid bacteria identification.After applying machine learning to the SIM image datasets from three model bacteria(including Escherichia coli,Mycobacterium smegmatis,and Pseudomonas aeruginosa),we obtained a classifcation accuracy of up to 98%.This study points out a promising possibility for rapid bacterial identification by morphological features.

A Low-Memory-Requiring and Fast Approach to Cluster Large-Scale Decoy Protein Structures

This work demonstrates the so-called PCAC (Protein principal Component Analysis Clustering) method, which clusters large-scale decoy protein structures in protein structure prediction based on principal component analysis (PCA), is an ultra-fast and low-memory-requiring clustering method. It can be two orders of magnitude faster than the commonlyused pairwise rmsd-clustering (pRMSD) when enormous of decoys are involved. Instead of N(N – 1)/2 least-square fitting of rmsd calculations and N2 memory units to store the pairwise rmsd values in pRMSD, PCAC only requires N rmsd calculations and N × P memory storage, where N is the number of structures to be clustered and P is the number of preserved eigenvectors. Furthermore, PCAC based on the covariance Cartesian matrix generates essentially the identical result as that from the reference rmsd-clustering (rRMSD). From a test of 41 protein decoy sets, when the eigenvectors that contribute a total of 90% eigenvalues are preserved, PCAC method reproduces the results of near-native selections from rRMSD.