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
Traditionally, Chinese indigenous cattle is geographically widespread. The present study analyzed based on genome-wide variants to evaluate the genetic background among 157 individuals from four representative indigen...Traditionally, Chinese indigenous cattle is geographically widespread. The present study analyzed based on genome-wide variants to evaluate the genetic background among 157 individuals from four representative indigenous cattle breeds of Hubei Province of China： Yiling yellow cattle （YL）, Bashan cattle （BS）, Wuling cattle （WL）, Zaobei cattle （ZB）, and 21 indi- viduals of Qinchuan cattle （QC） from the nearby Shanxi Province of China. Linkage disequilibrium （LD） analysis showed the LD of YL was the lowest （~=0.32） when the distance between markers was approximately 2 kb. Principle component analysis （PCA）, and neighbor-joining （NJ）-tree revealed a separation of Yiling yellow cattle from other geographic nearby local cattle breeds. In PCA plot, the YL and QC groups were segregated as expected; moreover, YL individuals clustered together more obviously. In the N J-tree, the YL group formed an independent branch and BS, WL, ZB groups were mixed. We then used the FST statistic approach to reveal long-term selection sweep of YL and other 4 cattle breeds. According to the selective sweep, we identified the unique pathways of YL, associated with production traits. Based on the results, it can be proposed that YL has its unique genetic characteristics of excellence resource, and it is an indispensable cattle breed in China.展开更多
In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)...In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)O_(2)-assisted Na_(2)CO_(3)(HSC)pretreatment of corn stover.A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables.The results showed that the Na_(2)CO_(3) dosage and pretreatment temperature had a strong influence on lignin removal,whereas pulp refining instrument(PFI)refining and Na_(2)CO_(3) dosage played positive roles in the final total sugar yield.Furthermore,it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover.In addition,a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time,and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance(particularly for the final total sugar yield),indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment.Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.展开更多
Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines ...Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines for resistance to lodging in Gossypium hirsutum L.Cotton breeders are interested in using diverse genotypes to enhance fiber quality and high-yield.Few pieces of research for hardness and its relationship with fiber quality and yield were found.This study was designed to find the relationship of stem hardness traits with fiber quality and yield contributing traits of upland cotton.Results:Experiments were carried out to measure the bending,acupuncture,and compression properties of the stem from a collection of upland cotton genotypes,comprising 237 accessions.The results showed that the genotypic difference in stem hardness was highly significant among the genotypes,and the stem hardness traits(BL,BU,AL,AU,CL,and CU)have a positive association with fiber quality traits and yield-related traits.Statistical analyses of the results showed that in descriptive statistics result bending(BL,BU)has a maximum coefficient of variance,but fiber length and fiber strength have less coefficient of variance among the genotypes.Principal component analysis(PCA)trimmed quantitative characters into nine principal components.The first nine principal components(PC)with Eigenvalues>1 explained 86%of the variation among 237 accessions of cotton.Both 2017 and 2018,PCA results indicated that BL,BU,FL,FE,and LI contributed to their variability in PC1,and BU,AU,CU,FD,LP,and FWPB have shown their variability in PC2.Conclusion:We describe here the systematic study of the mechanism involved in the regulation of enhancing fiber quality and yield by stem bending strength,acupuncture,and compression properties of G.hirsutum.展开更多
Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feat...Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.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
Today,securing devices connected to the internet is challenging as security threats are generated through various sources.The protection of cyber-physical systems from external attacks is a primary task.The presented ...Today,securing devices connected to the internet is challenging as security threats are generated through various sources.The protection of cyber-physical systems from external attacks is a primary task.The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters.The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis(MEDA)through Principle Component Analysis(PCA)and Singular Value Decompo-sition(SVD)for the extraction of unique parameters.The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network(R2CNN)and Gradient Boost Regression(GBR)to identify the maximum correlation.Novel Late Fusion Aggregation enabled with Cyber-Net(LFAEC)is the robust derived algorithm.The quantitative analysis uses predicted threat points with actual threat variables from which mean and difference vectors areevaluated.The performance of the presented system is assessed against the parameters such as Accuracy,Precision,Recall,and F1 Score.The proposed method outperformed by 98% to 100% in all quality measures compared to existing methods.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
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
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
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
Lumber moisture content(LMC) is the important parameter to judge the dryness of lumber and the quality of wooden products.Nevertheless the data acquired are mostly redundant and incomplete because of the complexity of...Lumber moisture content(LMC) is the important parameter to judge the dryness of lumber and the quality of wooden products.Nevertheless the data acquired are mostly redundant and incomplete because of the complexity of the course of drying,by interference factors that exist in the dryness environment and by the physical characteristics of the lumber itself.To improve the measuring accuracy and reliability of LMC,the optimal support vector machine(SVM) algorithm was put forward for regression analysis LMC.Environmental factors such as air temperature and relative humidity were considered,the data of which were extracted with the principle component analysis method.The regression and prediction of SVM was optimized based on the grid search(GS) technique.Groups of data were sampled and analyzed,and simulation comparison of forecasting performance shows that the main component data were extracted to speed up the convergence rate of the optimum algorithm.The GS-SVM shows a better performance in solving the LMC measuring and forecasting problem.展开更多
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
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dim...The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.展开更多
Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three...Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include： principle component analysis （PCA） based filters, linear discriminant analysis （LDA） based filters and minimum classification error （MCE） based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.展开更多
This study aimed to provide relevant knowledge about the dynamics of the hydrological parameters in the river-estuary continuum of the Wouri-Nkam river estuary for a sustainable management program. The hydrological pa...This study aimed to provide relevant knowledge about the dynamics of the hydrological parameters in the river-estuary continuum of the Wouri-Nkam river estuary for a sustainable management program. The hydrological parameters were recorded in eleven stations spanned out on the bas<span style="white-space:normal;"><span style="font-family:;" "="">is</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> of population density and human activities. Water quality parameters (Temperature, salinity, dissolved oxygen, pH, Total dissolved solutes, Redox potential and conductivity) were collected in subsurface water using a multiple parameter. Surface currents and morphometric (depth and width) parameters were recorded using a drifter, sonar depth and GPS. The field measurements took placed between 18/05/2019 to 08/09/2020 and were divided into six (06) cruises. The data were later subjected to an analysis of variance (ANOVA) and Principle Component Analysis using XLSTAT 2017 (2.7 version) software. Results obtained revealed that, the water quality parameters were spatially more stable no</span></span><span style="white-space:normal;"><span style="font-family:;" "="">t</span></span><span style="white-space:normal;"><span style="font-family:;" "="">signficant at (df = 9, p <</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> </span></span><span style="white-space:normal;"><span style="font-family:;" "="">0.05) with a relatively low temperature (25.5</span></span><span style="white-space:normal;"><span style="font-family:;" "=""><span style="white-space:nowrap;">°</span>C - 27<span style="white-space:nowrap;">°</span>C) during the wet period. The limit of the frontal zone extended to S5 (Bonalokan, 8.25</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> </span></span><span style="white-space:normal;"><span style="font-family:;" "="">km from S1) during the snapshot of the dry period, spring phase and flood tide conditions. Inversely, during wet period, this extension reduced to S1 (Bridge) and relatively increases slightly to S3 (Bonangang) during the neap phase and ebb tides of this season. This result revealed a change in the axial gradient of about eight (08) and four (04) kilometers during the seasonal and tidal scales (lunar and diurnal periods) respectively. These changes were also accompanied by changes in the water quality signatures, that may affect the fishery distribution and compositions. However, futures studies to buttress the results of this investigation should focus on longer time series sampling methods and model developments.</span></span>展开更多
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.
基金funded in part by the National Natural Science Foundation of China (31402039,31472079,31372294)the Beijing Natural Science Foundation (6154032)+2 种基金the Species and Breed Resources Conservation of the Ministry of Agriculture of China (2017-2019)the Cattle Breeding Innovative Research Team of Chinese Academy of Agricultural Sciences (cxgc-ias-03)the National Beef Cattle Industrial Technology System (CARS-37)
文摘Traditionally, Chinese indigenous cattle is geographically widespread. The present study analyzed based on genome-wide variants to evaluate the genetic background among 157 individuals from four representative indigenous cattle breeds of Hubei Province of China： Yiling yellow cattle （YL）, Bashan cattle （BS）, Wuling cattle （WL）, Zaobei cattle （ZB）, and 21 indi- viduals of Qinchuan cattle （QC） from the nearby Shanxi Province of China. Linkage disequilibrium （LD） analysis showed the LD of YL was the lowest （~=0.32） when the distance between markers was approximately 2 kb. Principle component analysis （PCA）, and neighbor-joining （NJ）-tree revealed a separation of Yiling yellow cattle from other geographic nearby local cattle breeds. In PCA plot, the YL and QC groups were segregated as expected; moreover, YL individuals clustered together more obviously. In the N J-tree, the YL group formed an independent branch and BS, WL, ZB groups were mixed. We then used the FST statistic approach to reveal long-term selection sweep of YL and other 4 cattle breeds. According to the selective sweep, we identified the unique pathways of YL, associated with production traits. Based on the results, it can be proposed that YL has its unique genetic characteristics of excellence resource, and it is an indispensable cattle breed in China.
基金This work was financially supported by the National Natural Science Foundation of China(No.31870568)Shandong Provincial Natural Science Foundation for Distinguished Young Scholars(China)(No.ZR2019JQ10)+1 种基金the Major Program of the Shandong Province Natural Science Foundation(No.ZR2018ZB0208)the"Transformational Technologies for Clean Energy and Demonstration"Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA21060201).
文摘In this study,multivariate analysis methods,including a principal component analysis(PCA)and partial least square(PLS)analysis,were applied to reveal the inner relationship of the key variables in the process of H_(2)O_(2)-assisted Na_(2)CO_(3)(HSC)pretreatment of corn stover.A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables.The results showed that the Na_(2)CO_(3) dosage and pretreatment temperature had a strong influence on lignin removal,whereas pulp refining instrument(PFI)refining and Na_(2)CO_(3) dosage played positive roles in the final total sugar yield.Furthermore,it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover.In addition,a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time,and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance(particularly for the final total sugar yield),indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment.Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.
基金National Key Technology R&D Program,Ministry of Science and Technology(2016YFD0100306,2016YFD0100203)National Natural Science Foundation of China(grants 31671746).
文摘Background:Stem hardness is one of the major influencing factors for plant architecture in upland cotton(Gossypium hirsutum L.).Evaluating hardness phenotypic traits is very important for the selection of elite lines for resistance to lodging in Gossypium hirsutum L.Cotton breeders are interested in using diverse genotypes to enhance fiber quality and high-yield.Few pieces of research for hardness and its relationship with fiber quality and yield were found.This study was designed to find the relationship of stem hardness traits with fiber quality and yield contributing traits of upland cotton.Results:Experiments were carried out to measure the bending,acupuncture,and compression properties of the stem from a collection of upland cotton genotypes,comprising 237 accessions.The results showed that the genotypic difference in stem hardness was highly significant among the genotypes,and the stem hardness traits(BL,BU,AL,AU,CL,and CU)have a positive association with fiber quality traits and yield-related traits.Statistical analyses of the results showed that in descriptive statistics result bending(BL,BU)has a maximum coefficient of variance,but fiber length and fiber strength have less coefficient of variance among the genotypes.Principal component analysis(PCA)trimmed quantitative characters into nine principal components.The first nine principal components(PC)with Eigenvalues>1 explained 86%of the variation among 237 accessions of cotton.Both 2017 and 2018,PCA results indicated that BL,BU,FL,FE,and LI contributed to their variability in PC1,and BU,AU,CU,FD,LP,and FWPB have shown their variability in PC2.Conclusion:We describe here the systematic study of the mechanism involved in the regulation of enhancing fiber quality and yield by stem bending strength,acupuncture,and compression properties of G.hirsutum.
基金Supported by the National Natural Science Foundation of China (No. 50877004)
文摘Serial Analysis of Gene Expression (SAGE) is a powerful tool to analyze whole-genome expression profiles. SAGE data, characterized by large quantity and high dimensions, need reducing their dimensions and extract feature to improve the accuracy and efficiency when they are used for pattern recognition and clustering analysis. A Poisson Model-based Kernel (PMK) was proposed based on the Poisson distribution of the SAGE data. Kernel Principle Component Analysis (KPCA) with PMK was proposed and used in feature-extract analysis of mouse retinal SAGE data. The computa-tional results show that this algorithm can extract feature effectively and reduce dimensions of SAGE data.
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.
文摘Today,securing devices connected to the internet is challenging as security threats are generated through various sources.The protection of cyber-physical systems from external attacks is a primary task.The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters.The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis(MEDA)through Principle Component Analysis(PCA)and Singular Value Decompo-sition(SVD)for the extraction of unique parameters.The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network(R2CNN)and Gradient Boost Regression(GBR)to identify the maximum correlation.Novel Late Fusion Aggregation enabled with Cyber-Net(LFAEC)is the robust derived algorithm.The quantitative analysis uses predicted threat points with actual threat variables from which mean and difference vectors areevaluated.The performance of the presented system is assessed against the parameters such as Accuracy,Precision,Recall,and F1 Score.The proposed method outperformed by 98% to 100% in all quality measures compared to existing methods.
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.
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supported by the Natural Science Foundation of China(Grant No.31470715),(Grant No.31470714)the Fundamental Research Funds for the Central Universities(2572016EBT1)
文摘Lumber moisture content(LMC) is the important parameter to judge the dryness of lumber and the quality of wooden products.Nevertheless the data acquired are mostly redundant and incomplete because of the complexity of the course of drying,by interference factors that exist in the dryness environment and by the physical characteristics of the lumber itself.To improve the measuring accuracy and reliability of LMC,the optimal support vector machine(SVM) algorithm was put forward for regression analysis LMC.Environmental factors such as air temperature and relative humidity were considered,the data of which were extracted with the principle component analysis method.The regression and prediction of SVM was optimized based on the grid search(GS) technique.Groups of data were sampled and analyzed,and simulation comparison of forecasting performance shows that the main component data were extracted to speed up the convergence rate of the optimum algorithm.The GS-SVM shows a better performance in solving the LMC measuring and forecasting problem.
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.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘The classification of pathological voice from healthy voice was studied based upon 27 acoustic features derived from a single sound signal of vowel /a:/. First, the feature space was transferred to reduce the data dimension by principle component analysis (PCA). Then the voice samples were classified according to the reduced PCA parameters by support vector machine (SVM) using radial basis function (RBF) as a kernel function. Meanwhile, by changing the ratio of opposite class samples, the accuracy under different features combinations was tested. Experimental data were provided by the voice database of Massachusetts Eye and Ear Infirmary (MEEI) in which 216 vowel /a:/ samples were collected from subjects of healthy and pathological cases, and tested with 5 fold cross-validation method. The result shows the positive rate of pathological voices was improved from 92% to 98% through the PCA method. STD, Fatr, Tasm, NHR, SEG, and PER are pathology sensitive features in illness detection. Using these sensitive features the accuracy of detection of pathological voice from healthy voice can reach 97%.
基金Sponsored bythe Basic Research Foundation of Beijing Institute of Technology (BIT-UBF-200301F03) BIT &Ericsson Cooperation Project
文摘Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include： principle component analysis （PCA） based filters, linear discriminant analysis （LDA） based filters and minimum classification error （MCE） based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.
文摘This study aimed to provide relevant knowledge about the dynamics of the hydrological parameters in the river-estuary continuum of the Wouri-Nkam river estuary for a sustainable management program. The hydrological parameters were recorded in eleven stations spanned out on the bas<span style="white-space:normal;"><span style="font-family:;" "="">is</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> of population density and human activities. Water quality parameters (Temperature, salinity, dissolved oxygen, pH, Total dissolved solutes, Redox potential and conductivity) were collected in subsurface water using a multiple parameter. Surface currents and morphometric (depth and width) parameters were recorded using a drifter, sonar depth and GPS. The field measurements took placed between 18/05/2019 to 08/09/2020 and were divided into six (06) cruises. The data were later subjected to an analysis of variance (ANOVA) and Principle Component Analysis using XLSTAT 2017 (2.7 version) software. Results obtained revealed that, the water quality parameters were spatially more stable no</span></span><span style="white-space:normal;"><span style="font-family:;" "="">t</span></span><span style="white-space:normal;"><span style="font-family:;" "="">signficant at (df = 9, p <</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> </span></span><span style="white-space:normal;"><span style="font-family:;" "="">0.05) with a relatively low temperature (25.5</span></span><span style="white-space:normal;"><span style="font-family:;" "=""><span style="white-space:nowrap;">°</span>C - 27<span style="white-space:nowrap;">°</span>C) during the wet period. The limit of the frontal zone extended to S5 (Bonalokan, 8.25</span></span><span style="white-space:normal;"><span style="font-family:;" "=""> </span></span><span style="white-space:normal;"><span style="font-family:;" "="">km from S1) during the snapshot of the dry period, spring phase and flood tide conditions. Inversely, during wet period, this extension reduced to S1 (Bridge) and relatively increases slightly to S3 (Bonangang) during the neap phase and ebb tides of this season. This result revealed a change in the axial gradient of about eight (08) and four (04) kilometers during the seasonal and tidal scales (lunar and diurnal periods) respectively. These changes were also accompanied by changes in the water quality signatures, that may affect the fishery distribution and compositions. However, futures studies to buttress the results of this investigation should focus on longer time series sampling methods and model developments.</span></span>
