Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, whi...Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.展开更多
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 intr...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.展开更多
Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagn...Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagnostic biological markers of BAVM available. Current study demonstrated that micro RNA(mi RNA)showed a feasible marker for vascular disease. To find key correlations between these mi RNAs and the onset of BAVM, we carried out chip analysis of serum mi RNAs by identifying 18 potential markers of BAVM. We then constructed a principle component analysis and logistic regression(PCA-LR) model to analyze the 18 mi RNAs collected from 77 patients. Another 9 independent samples were used to test the resulting model. The results showed that mi RNAs hsa-mir-126-3p and hsa-mir-140 are important protective factors, while hsa-mir-338 is a dominating risk factor, all of which have stronger correlation with BAVM than others. We also compared the testing results using PCA-LR model with those using LR model. The comparison revealed that PCA-LR model is better in predicting the disease.展开更多
NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PC...NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.展开更多
Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quali...Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.展开更多
The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity fr...The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.展开更多
Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component ana...Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis （PCA） was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the W and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st compo- nent, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.展开更多
Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal c...Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.展开更多
How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle co...How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.展开更多
[Objective] The aim was to study on distribution of inorganic elements in kernel of Amygdalus communis L., providing reference for quality evaluation of A. communis L. species. [Method] Totally 26 species of inorganic...[Objective] The aim was to study on distribution of inorganic elements in kernel of Amygdalus communis L., providing reference for quality evaluation of A. communis L. species. [Method] Totally 26 species of inorganic elements in kernel, including Al, B, Be, Ca, Co, Cu, Fe, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sn, Sr, Ti, Zn, Cd, As, Se, V, Hg, Cr and K were measured with inductively coupled plasma emission spectrum （ICP-OES） and principal components analysis （PCA）. [Result] A. communis L. of different species and in different factories showed a similar curve in content of inorganic elements; absolute contents of the elements differed significantly. In addition, the accumulated variance contribution of five principle factors achieved as high as 84.371% and the variance contribution made by the first three factors accounted for 67.546%, proving that Fe, Ti, Pb, Na, Se, Cu, Mo, K, Zn, Ni, Ca and Sr were characteristic elements. [Conclusion] The method, which is brief, rapid and accurate, can be used for determination of inorganic elements in kernel of A. communis L., providing theoretical references for further development and utilization of A. communis L.展开更多
HPLC fingerprinting and quantification of gentiopicroside（GPS） and loganic acid（LA） in Gentianae Macrophyllae Radix（GMR） crude drugs were developed in this study.The samples were separated on Zorbax SB-C_（18） ...HPLC fingerprinting and quantification of gentiopicroside（GPS） and loganic acid（LA） in Gentianae Macrophyllae Radix（GMR） crude drugs were developed in this study.The samples were separated on Zorbax SB-C_（18） column（250 mm×4.6 mm, 5μm） with a linear gradient of acetonitrile and 0.04%phosphoric acid.The HPLC flow rate was 1.0 mL/min and a UV absorption was measured at 230 nm.An orthogonal L9（3^4） test was applied for the optimization of sample extraction conditions,and an aliquot of GMR sample（g） was extracted with 15-fold of 50%ethanol（mL） for 30 min by sonication.Quantitative analysis showed that the content of GPS（14.05 mg/g-74.61 mg/g） in all samples was obviously higher than that of LA（1.13 mg/g-40.46 mg/g）. Based on the content ratio of GPS over LA（1.8-11.4）,samples originated from Gentiana macrophylla（with content ratio of GPS over LA≤4.3） could be distinguished from those from G.dahurica and G.dahurica var.gracilipes（with content ratio of GPS over LA≥4.8）.The principle components analysis of the HPLC fingerprints showed that samples originated from G.macrophylla and G.dahurica（including G.dahurica var.gracilipes） could be divided into two groups.This established HPLC-DAD method could be efficiently used for the species identification and quality control of GMR crude drugs.展开更多
The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are comb...The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are combined and a PC-LDA approach is used to extract the features of traffic signs. After obtaining the binary images of the traffic signs through normalization and binarization, PC-LDA can extract the feature subspace of the traffic sign images with the best description and classification. The extracted features are recognized by using the minimum distance classifier. The approach is verified by using MPEG7 CE Shape-1 Part-B computer shape library and traffic sign image library which includes both standard and natural traffic signs. The results show that under the condition that the traffic sign is in a nature scene, PC-LDA approach applied to binary images in which shape features are extracted can obtain better results.展开更多
The goal of this paper is to propose a fast and secure multi-stage image compression-decompression system by using a wireless network between two Personal Computers (PCs). In this paper, the Principal Component Analys...The goal of this paper is to propose a fast and secure multi-stage image compression-decompression system by using a wireless network between two Personal Computers (PCs). In this paper, the Principal Component Analysis (PCA) technique is used for multi-stage image compression and Inverse Principal Component Analysis (IPCA) for multi-stage image decompression. The first step of the proposed system is to select the input image, the second step is to perform PCA up to 9 times on the input image, this compression, and after multi-stage compression process then the third step begins by transforming across wireless Ad hoc Network (WANET) to the second computing device, forth step start with multi-stage decompression process up 9 times. The proposed system for different images is transferred over the wireless network using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of the MATLAB program. The proposed system implements 25 different images correctly (100%). The main contribution of this paper is that we are dealing with the black image at the end of the compressed process ad start with a black image at the start of the decompressed process of this proposed system. In this work, the compressed and uncompressed images are compared with each other in their size and transmission time. This system can be very useful in networks because they provide a high level of protection to the transmitted data from hackers because they cannot guess how much the image has been compressed or what kind of information the image represents.展开更多
Polycyclic aromatic hydrocarbons （PAHs） are mainly produced by combustion processes and consist of a number of toxic com- pounds. They are always emitted as a mixture and have become a major type of pollutants in ur...Polycyclic aromatic hydrocarbons （PAHs） are mainly produced by combustion processes and consist of a number of toxic com- pounds. They are always emitted as a mixture and have become a major type of pollutants in urban areas. The degree of soil contamination by PAHs is of special concern in areas immediately in proximity to cities with heavy traffic, factories, older buildings, and infrastructure. The accumulation of soil PAHs is also affected by non-anthropogenie factors, such as climate, vegetation, and soil property. This paper reviews three typical source identification techniques, including diagnostic ratios, positive matrix factorization, and principle components analysis. The advantages or disadvantages of these techniques are analyzed. It is recommended that multiple identification techniques be used to determine the sources in order to minimize the weaknesses inherent in each method and thereby to strengthen the conclusions for PAH source identification.展开更多
This study addressed the relationship of river water pollution characteristics to land covers and human activities in the catchments in a complete river system named Cao-E River in eastern China.Based on the hydrogeoc...This study addressed the relationship of river water pollution characteristics to land covers and human activities in the catchments in a complete river system named Cao-E River in eastern China.Based on the hydrogeochemical data collected monthly over a period of 3 years,cluster analysis(CA) and principal component analysis(PCA) were adopted to categorize the river reaches and reveal their pollution characteristics.According to the differences of water quality in the river reaches and land use patterns and average population densities in their catchments,the whole river system could be categorized into three groups of river reaches,i.e.,non-point sources pollution reaches(NPSPR),urban reaches(UR) and mixed sources pollution reaches(MSPR).In UR and MSPR,the water quality was mainly impacted by nutrient and organic pollution,while in NPSPR nutrient pollution was the main cause.The nitrate was the main nitrogen form in NPSPR and particulate phosphorus was the main phosphorus form in MSPR.There were no apparent trends for the variations of pollutant concentrations with increasing river flows in NPSPR and MSPR,while in UR the pollutant concentrations decreased with increasing river flows.Thus dry season was the critical period for water pollution control in UR.Therefore,catchment land covers and human activities had significant impact on river reach water pollution type,nutrient forms and water quality responses to hydrological conditions,which might be crucial for developing strategies to combat water pollution in watershed scale.展开更多
The presence of heavy metals（HMs） in particulate matters（PMs） particularly fine particles such as PM2.5 poses potential risk to the health of human being. The purpose of this study was to analyze the contents of H...The presence of heavy metals（HMs） in particulate matters（PMs） particularly fine particles such as PM2.5 poses potential risk to the health of human being. The purpose of this study was to analyze the contents of HMs in PM2.5 in the atmospheric monitoring stations in Isfahan city,Iran, in different seasons between March 2014 and March 2015 and their source identification using principle component analysis（PCA）. The samples of PM2.5 were taken using a high volume sampler in 7 monitoring stations located throughout the city and industrial zones since March 2014 to March 2015. The HMs content of the samples was measured using ICP-MS.The results showed that the concentrations of As, Cd and Ni were in a range of 23–36, 1–12,and 5–76 ng/m3 at all the stations which exceeded the US-EPA standards. Furthermore,the concentrations of Cr and Cu reached to 153 and 167 ng/m3 in some stations which were also higher than the standard levels. Depending on the potential sources of HMs, their concentration in PM2.5 through the various seasons was different. PCA illustrated that the different potential sources of HMs in the atmosphere, showing that the most important sources of HMs originated from fossil fuel combustion, abrasion of vehicle tires, industrial activities（e.g., iron and steel industries） and dust storms. Management and control of air pollution of industrial plants and vehicles are suggested for decreasing the risk of the HMs in the region.展开更多
To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnos...To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dy-namic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fer-mentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots (t-chart, SPE-chart, and T2-chart). Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.展开更多
Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous ...Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.展开更多
Effects of the heavy metal copper(Cu), the metalloid arsenic(As), and the antibiotic oxytetracycline(OTC) on bacterial community structure and diversity during cow and pig manure composting were investigated. Eight tr...Effects of the heavy metal copper(Cu), the metalloid arsenic(As), and the antibiotic oxytetracycline(OTC) on bacterial community structure and diversity during cow and pig manure composting were investigated. Eight treatments were applied, four to each manure type, namely cow manure with:(1) no additives(control),(2) addition of heavy metal and metalloid,(3) addition of OTC and(4) addition of OTC with heavy metal and metalloid;and pig manure with:(5) no additives(control),(6) addition of heavy metal and metalloid,(7) addition of OTC and(8) addition of OTC with heavy metal and metalloid. After 35 days of composting, according to the alpha diversity indices, the combination treatment(OTC with heavy metal and metalloid) in pig manure was less harmful to microbial diversity than the control or heavy metal and metalloid treatments. In cow manure, the treatment with heavy metal and metalloid was the most harmful to the microbial community, followed by the combination and OTC treatments. The OTC and combination treatments had negative effects on the relative abundance of microbes in cow manure composts. The dominant phyla in both manure composts included Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. The microbial diversity relative abundance transformation was dependent on the composting time. Redundancy analysis(RDA) revealed that environmental parameters had the most influence on the bacterial communities. In conclusion, the composting process is the most sustainable technology for reducing heavy metal and metalloid impacts and antibiotic contamination in cow and pig manure. The physicochemical property variations in the manures had a significant effect on the microbial community during the composting process. This study provides an improved understanding of bacterial community composition and its changes during the composting process.展开更多
Asian monsoon have multiple forms of variations such as seasonal variation, intra-seasonal variation, interannual variation, etc. The interannual variations have not only yearly variations but also variations among se...Asian monsoon have multiple forms of variations such as seasonal variation, intra-seasonal variation, interannual variation, etc. The interannual variations have not only yearly variations but also variations among several years. In general, the yearly variations are described with winter temperature and summer precipitation, and the variations among several years are reflected by circulation of ENSO events. In this study, at first, we analyze the relationship between land cover and interannual monsoon variations represented by precipitation changes using Singular Value Decomposition method based on the time series precipitation data and 8km NOAA AVHRR NDVI data covering 1982 to 1993 in east Asia. Furthermore, after confirmation and reclassification of ENSO events which are recognized as the strong signal of several year monsoon variation, using the same time series NDVI data during 1982 to 1993 in east Asia, we make a Principle Component Analysis and analyzed the correlation of the 7th component eigenvectors and Southern Oscillation Index (SOI) that indicates the characteristic of ENSO events, and summed up the temporal-spatial distribution features of east Asian land cover’s inter-annual variations that are being driven by changes of ENSO events.展开更多
基金the National Natural Science Foundation of China(No.61572033)the Natural Science Foundation of Education Department of Anhui Province of China(No.KJ2015ZD08)the Higher Education Promotion Plan of Anhui Province of China(No.TSKJ2015B14)
文摘Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.
基金Supported by the National Natural Science Foundation of China (No.60434020, No.60374020)International Coop-eration Item of Henan Province (No.0446650006)Henan Province Outstanding Youth Science Fund (No.0312001900).
文摘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.
文摘Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagnostic biological markers of BAVM available. Current study demonstrated that micro RNA(mi RNA)showed a feasible marker for vascular disease. To find key correlations between these mi RNAs and the onset of BAVM, we carried out chip analysis of serum mi RNAs by identifying 18 potential markers of BAVM. We then constructed a principle component analysis and logistic regression(PCA-LR) model to analyze the 18 mi RNAs collected from 77 patients. Another 9 independent samples were used to test the resulting model. The results showed that mi RNAs hsa-mir-126-3p and hsa-mir-140 are important protective factors, while hsa-mir-338 is a dominating risk factor, all of which have stronger correlation with BAVM than others. We also compared the testing results using PCA-LR model with those using LR model. The comparison revealed that PCA-LR model is better in predicting the disease.
基金Supported by the National Natural Science Foundation of China (No. 60776795,60736043,60902031,and 60805012)the Research Fund for the Doctoral Program of Higher Education of China (No. 200807010004,20070701023)the Fundamental Research Funds for the Central Universities of China (No. JY10000902028)
文摘NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.
文摘Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.
基金supported by the National Natural Science Foundation of China under project 41275013the National High-Tech Research and development program of China under project 2013AA09A506-4the National Basic Research Program under project 2009CB723903
文摘The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
基金Supported by the Program for New Century Excellent Talents in University (NCET-05-0573)Fujian Science and Technology Project (No2006I0018)the Science Project of the Education Department of Fujian Province(No 2006F5022)
文摘Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis （PCA） was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the W and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st compo- nent, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.
基金The National Natural Science Foundation of China(No 60504033)
文摘Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.
基金National Key Science & Technology Special Projects(Grant No.2008ZX05000-004)CNPC Projects(Grant No.2008E-0610-10).
文摘How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key,which is usually solved by reducing dimensionality.Principle component analysis（PCA） is the most widely-used linear dimensionality reduction method at present.However,the relationships between seismic attributes and reservoir features are non-linear,so seismic attribute dimensionality reduction based on linear transforms can＇t solve non-linear problems well,reducing reservoir prediction precision.As a new non-linear learning method,manifold learning supplies a new method for seismic attribute analysis.It can discover the intrinsic features and rules hidden in the data by computing low-dimensional,neighborhood-preserving embeddings of high-dimensional inputs.In this paper,we try to extract seismic attributes using locally linear embedding（LLE）,realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters.Combining model analysis and case studies,we compare the dimensionality reduction and clustering effects of LLE and PCA,both of which indicate that LLE can retain the intrinsic structure of the inputs.The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies,reservoir,and even reservoir fluids.
基金Supported by the Pillar Program of Ministry of Science and Technology of the People's Republic of China (2012BAI27B07)the Fundamental Research Funds for the Central Universities (11NZYTH02)+1 种基金Sichuan Key Technology Research and Development Program (2011SZ0233)Academic Technology for Excellent Youth Follow-up Plan in Sichuan (2011JQ0051)~~
文摘[Objective] The aim was to study on distribution of inorganic elements in kernel of Amygdalus communis L., providing reference for quality evaluation of A. communis L. species. [Method] Totally 26 species of inorganic elements in kernel, including Al, B, Be, Ca, Co, Cu, Fe, Mg, Mn, Mo, Na, Ni, P, Pb, Si, Sn, Sr, Ti, Zn, Cd, As, Se, V, Hg, Cr and K were measured with inductively coupled plasma emission spectrum （ICP-OES） and principal components analysis （PCA）. [Result] A. communis L. of different species and in different factories showed a similar curve in content of inorganic elements; absolute contents of the elements differed significantly. In addition, the accumulated variance contribution of five principle factors achieved as high as 84.371% and the variance contribution made by the first three factors accounted for 67.546%, proving that Fe, Ti, Pb, Na, Se, Cu, Mo, K, Zn, Ni, Ca and Sr were characteristic elements. [Conclusion] The method, which is brief, rapid and accurate, can be used for determination of inorganic elements in kernel of A. communis L., providing theoretical references for further development and utilization of A. communis L.
基金Research Program of Science Technology of Traditional Chinese Medicine(TCM) Sponsored by the State Administration of TCM of China(Grant No.04-05ZL01)
文摘HPLC fingerprinting and quantification of gentiopicroside（GPS） and loganic acid（LA） in Gentianae Macrophyllae Radix（GMR） crude drugs were developed in this study.The samples were separated on Zorbax SB-C_（18） column（250 mm×4.6 mm, 5μm） with a linear gradient of acetonitrile and 0.04%phosphoric acid.The HPLC flow rate was 1.0 mL/min and a UV absorption was measured at 230 nm.An orthogonal L9（3^4） test was applied for the optimization of sample extraction conditions,and an aliquot of GMR sample（g） was extracted with 15-fold of 50%ethanol（mL） for 30 min by sonication.Quantitative analysis showed that the content of GPS（14.05 mg/g-74.61 mg/g） in all samples was obviously higher than that of LA（1.13 mg/g-40.46 mg/g）. Based on the content ratio of GPS over LA（1.8-11.4）,samples originated from Gentiana macrophylla（with content ratio of GPS over LA≤4.3） could be distinguished from those from G.dahurica and G.dahurica var.gracilipes（with content ratio of GPS over LA≥4.8）.The principle components analysis of the HPLC fingerprints showed that samples originated from G.macrophylla and G.dahurica（including G.dahurica var.gracilipes） could be divided into two groups.This established HPLC-DAD method could be efficiently used for the species identification and quality control of GMR crude drugs.
基金Supported by National Natural Science Foundation of China(No.61540069)
文摘The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are combined and a PC-LDA approach is used to extract the features of traffic signs. After obtaining the binary images of the traffic signs through normalization and binarization, PC-LDA can extract the feature subspace of the traffic sign images with the best description and classification. The extracted features are recognized by using the minimum distance classifier. The approach is verified by using MPEG7 CE Shape-1 Part-B computer shape library and traffic sign image library which includes both standard and natural traffic signs. The results show that under the condition that the traffic sign is in a nature scene, PC-LDA approach applied to binary images in which shape features are extracted can obtain better results.
文摘The goal of this paper is to propose a fast and secure multi-stage image compression-decompression system by using a wireless network between two Personal Computers (PCs). In this paper, the Principal Component Analysis (PCA) technique is used for multi-stage image compression and Inverse Principal Component Analysis (IPCA) for multi-stage image decompression. The first step of the proposed system is to select the input image, the second step is to perform PCA up to 9 times on the input image, this compression, and after multi-stage compression process then the third step begins by transforming across wireless Ad hoc Network (WANET) to the second computing device, forth step start with multi-stage decompression process up 9 times. The proposed system for different images is transferred over the wireless network using Transmission Control Protocol/Internet Protocol (TCP/IP), which is programmed using the network role property of the MATLAB program. The proposed system implements 25 different images correctly (100%). The main contribution of this paper is that we are dealing with the black image at the end of the compressed process ad start with a black image at the start of the decompressed process of this proposed system. In this work, the compressed and uncompressed images are compared with each other in their size and transmission time. This system can be very useful in networks because they provide a high level of protection to the transmitted data from hackers because they cannot guess how much the image has been compressed or what kind of information the image represents.
基金the financial support from the National Natural Science Foundation of China(No.41671085)
文摘Polycyclic aromatic hydrocarbons （PAHs） are mainly produced by combustion processes and consist of a number of toxic com- pounds. They are always emitted as a mixture and have become a major type of pollutants in urban areas. The degree of soil contamination by PAHs is of special concern in areas immediately in proximity to cities with heavy traffic, factories, older buildings, and infrastructure. The accumulation of soil PAHs is also affected by non-anthropogenie factors, such as climate, vegetation, and soil property. This paper reviews three typical source identification techniques, including diagnostic ratios, positive matrix factorization, and principle components analysis. The advantages or disadvantages of these techniques are analyzed. It is recommended that multiple identification techniques be used to determine the sources in order to minimize the weaknesses inherent in each method and thereby to strengthen the conclusions for PAH source identification.
基金Supported by the National Natural Science Foundation of China (No. 40871104)the National High Technology Research andDevelopment Program (863 Program) of China (No. 2007AA10Z218)
文摘This study addressed the relationship of river water pollution characteristics to land covers and human activities in the catchments in a complete river system named Cao-E River in eastern China.Based on the hydrogeochemical data collected monthly over a period of 3 years,cluster analysis(CA) and principal component analysis(PCA) were adopted to categorize the river reaches and reveal their pollution characteristics.According to the differences of water quality in the river reaches and land use patterns and average population densities in their catchments,the whole river system could be categorized into three groups of river reaches,i.e.,non-point sources pollution reaches(NPSPR),urban reaches(UR) and mixed sources pollution reaches(MSPR).In UR and MSPR,the water quality was mainly impacted by nutrient and organic pollution,while in NPSPR nutrient pollution was the main cause.The nitrate was the main nitrogen form in NPSPR and particulate phosphorus was the main phosphorus form in MSPR.There were no apparent trends for the variations of pollutant concentrations with increasing river flows in NPSPR and MSPR,while in UR the pollutant concentrations decreased with increasing river flows.Thus dry season was the critical period for water pollution control in UR.Therefore,catchment land covers and human activities had significant impact on river reach water pollution type,nutrient forms and water quality responses to hydrological conditions,which might be crucial for developing strategies to combat water pollution in watershed scale.
基金financially supported by the Fundamental Research Funds for the central University,China University of Geosciences(Wuhan)(No.CUG150602)the International collaboration project funded by the China University of Geosciences(Wuhan)
文摘The presence of heavy metals（HMs） in particulate matters（PMs） particularly fine particles such as PM2.5 poses potential risk to the health of human being. The purpose of this study was to analyze the contents of HMs in PM2.5 in the atmospheric monitoring stations in Isfahan city,Iran, in different seasons between March 2014 and March 2015 and their source identification using principle component analysis（PCA）. The samples of PM2.5 were taken using a high volume sampler in 7 monitoring stations located throughout the city and industrial zones since March 2014 to March 2015. The HMs content of the samples was measured using ICP-MS.The results showed that the concentrations of As, Cd and Ni were in a range of 23–36, 1–12,and 5–76 ng/m3 at all the stations which exceeded the US-EPA standards. Furthermore,the concentrations of Cr and Cu reached to 153 and 167 ng/m3 in some stations which were also higher than the standard levels. Depending on the potential sources of HMs, their concentration in PM2.5 through the various seasons was different. PCA illustrated that the different potential sources of HMs in the atmosphere, showing that the most important sources of HMs originated from fossil fuel combustion, abrasion of vehicle tires, industrial activities（e.g., iron and steel industries） and dust storms. Management and control of air pollution of industrial plants and vehicles are suggested for decreasing the risk of the HMs in the region.
基金Supported by the National Natural Science Foundation of China (No.60574038).
文摘To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dy-namic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fer-mentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots (t-chart, SPE-chart, and T2-chart). Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.
基金funded by the German Federal Ministry for Economic Affairs and Energy(BMWi)(01MD15009F).
文摘Liquid leakage from pipelines is a critical issue in large-scale process plants.Damage in pipelines affects the normal operation of the plant and increases maintenance costs.Furthermore,it causes unsafe and hazardous situations for operators.Therefore,the detection and localization of leakages is a crucial task for maintenance and condition monitoring.Recently,the use of infrared(IR)cameras was found to be a promising approach for leakage detection in large-scale plants.IR cameras can capture leaking liquid if it has a higher(or lower)temperature than its surroundings.In this paper,a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant.Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid,it is applicable for any type of liquid leakage(i.e.,water,oil,etc.).In this method,subsequent frames are subtracted and divided into blocks.Then,principle component analysis is performed in each block to extract features from the blocks.All subtracted frames within the blocks are individually transferred to feature vectors,which are used as a basis for classifying the blocks.The k-nearest neighbor algorithm is used to classify the blocks as normal(without leakage)or anomalous(with leakage).Finally,the positions of the leakages are determined in each anomalous block.In order to evaluate the approach,two datasets with two different formats,consisting of video footage of a laboratory demonstrator plant captured by an IR camera,are considered.The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos.The proposed method has high accuracy and a reasonable detection time for leakage detection.The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.
基金the National Key Technology R&D Program of China(2018YFD0500206)the National Natural Science Foundation of China(31572209,31772395 and 31972943)the Foundation for Safety of Agricultural Products by Ministry of Agriculture and Rural Affairs,China(GJFP2019033)。
文摘Effects of the heavy metal copper(Cu), the metalloid arsenic(As), and the antibiotic oxytetracycline(OTC) on bacterial community structure and diversity during cow and pig manure composting were investigated. Eight treatments were applied, four to each manure type, namely cow manure with:(1) no additives(control),(2) addition of heavy metal and metalloid,(3) addition of OTC and(4) addition of OTC with heavy metal and metalloid;and pig manure with:(5) no additives(control),(6) addition of heavy metal and metalloid,(7) addition of OTC and(8) addition of OTC with heavy metal and metalloid. After 35 days of composting, according to the alpha diversity indices, the combination treatment(OTC with heavy metal and metalloid) in pig manure was less harmful to microbial diversity than the control or heavy metal and metalloid treatments. In cow manure, the treatment with heavy metal and metalloid was the most harmful to the microbial community, followed by the combination and OTC treatments. The OTC and combination treatments had negative effects on the relative abundance of microbes in cow manure composts. The dominant phyla in both manure composts included Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. The microbial diversity relative abundance transformation was dependent on the composting time. Redundancy analysis(RDA) revealed that environmental parameters had the most influence on the bacterial communities. In conclusion, the composting process is the most sustainable technology for reducing heavy metal and metalloid impacts and antibiotic contamination in cow and pig manure. The physicochemical property variations in the manures had a significant effect on the microbial community during the composting process. This study provides an improved understanding of bacterial community composition and its changes during the composting process.
基金Knowledge Innovation Project of CAS, No.KZCX2-308Natural Science Foundation of Inner Mongolia Autonomous Region, No. 20010905-14
文摘Asian monsoon have multiple forms of variations such as seasonal variation, intra-seasonal variation, interannual variation, etc. The interannual variations have not only yearly variations but also variations among several years. In general, the yearly variations are described with winter temperature and summer precipitation, and the variations among several years are reflected by circulation of ENSO events. In this study, at first, we analyze the relationship between land cover and interannual monsoon variations represented by precipitation changes using Singular Value Decomposition method based on the time series precipitation data and 8km NOAA AVHRR NDVI data covering 1982 to 1993 in east Asia. Furthermore, after confirmation and reclassification of ENSO events which are recognized as the strong signal of several year monsoon variation, using the same time series NDVI data during 1982 to 1993 in east Asia, we make a Principle Component Analysis and analyzed the correlation of the 7th component eigenvectors and Southern Oscillation Index (SOI) that indicates the characteristic of ENSO events, and summed up the temporal-spatial distribution features of east Asian land cover’s inter-annual variations that are being driven by changes of ENSO events.
