Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high com...Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.展开更多
The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This...The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.展开更多
It is of critical importance to determine the endpoint of stabilization for landfills that are stabilized by aeration acceleration.Current stabilization evaluation methods are inconsistent and mostly fail to account f...It is of critical importance to determine the endpoint of stabilization for landfills that are stabilized by aeration acceleration.Current stabilization evaluation methods are inconsistent and mostly fail to account for the effect of oxygen concentration.In this study,degradation experiments were conducted to quantitatively analyze the impact of oxygen on microbial communities and metabolic functions.High-throughput sequencing analysis demonstrated that an oxygen concentration exceeding 10%significantly enhances amino acid metabolism,secondary metabolite biosynthesis,and exogenous biodegradation.Three-dimensional fluorescence data were analyzed using the PARAFAC method,and a novel fluorescence-based stabilization indicator was proposed based on the ratio of fulvic-like to tyrosine-like substances.When the growth multiples of the fluorescence index exceed 10-fold,it can be inferred that degradation has been met the stabilization endpoint.Principal component analysis was employed to establish multiple regression equations between the physicochemical parameters of landfill waste and dissolved fluorescent substants,offering an innovative insight to evaluate the stabilization process of aerated landfills.展开更多
Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamformin...Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric...This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.展开更多
We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of t...We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.展开更多
In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the d...In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.展开更多
[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experim...[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.展开更多
[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal ...[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.展开更多
In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algori...In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
This research uses eigenvalue characteristics of nonlinear principal component analysis （NLPCA） and principal component analysis （PCA） to investigate total electron content （TEC） anomalies associated with Taiwan...This research uses eigenvalue characteristics of nonlinear principal component analysis （NLPCA） and principal component analysis （PCA） to investigate total electron content （TEC） anomalies associated with Taiwan＇s Chi-Chi earthquake of 21 September 1999 （LT） （M_w=7.6）. The transforms are used for ionospheric TEC from 01 August to 20 September 1999 （local time） using data from 13 GPS receivers. The data were collected at 22°N-26°N Lat. and 120°E-122°E Long.. Applying the NLPCA to the multi-channel total electron content records of GPS receivers, the earthquake-associated TEC anomalies were represented by large principal eigenvalues of NLPCA （〉0.5 in a normalized set） on 14 August and 17, 18, and 20 September, with allowance given for the Dst index, which was quiet for the study period. Comparisons were then made with other researchers who also found TEC anomalies on September 17, 18, and 19 associated with the Chi-Chi earthquake, which cannot be detected by PCA.Consideration is also given for reported ground level geomagnetic field activity that occurred between mid-August and late October, leading up to and including the Chi-Chi and Chia-Yi earthquakes, which are associated with the same series of faults. It is possible that Aug. 14 is representative of an earthquake-associated TEC anomaly. This is an interesting result given how much earlier than the earthquake it occurred.展开更多
Objective] This study was conducted to investigate the main factors affect-ing the lodging resistance of plateau japonica rice. [Method] Twenty agronomic traits related to lodging resistance of plateau japonica rice w...Objective] This study was conducted to investigate the main factors affect-ing the lodging resistance of plateau japonica rice. [Method] Twenty agronomic traits related to lodging resistance of plateau japonica rice were analyzed by principal component analysis and correlation analysis among 26 varieties/lines of plateau japonica rice. [Result] The lodging resistance of the 26 varieties/lines had great dif-ference among different agronomic traits. Plant height, and wal thickness of the 4th, 3rd and 2nd internodes under the panicle had the most important influence on lodging resistance, while the diameter of the 3rd, 2nd, 4th, 1st nodes under the panicle, length of the 4th and 3rd internodes under the panicle, wal thickness of the 1st internode under the panicle had less influence. The other nine agronomic traits of rice culm did not affect or indirectly affected lodging resistance through above-mentioned agro-nomic traits. Lodging resistance had significant correlations with plant height, length of the 4th and 3rd internodes under the panicle, wal thickness of the 1st, 2nd, 3rd and 4th internodes under the panicle and diameter of the 1st, 2nd, 3rd and 4th node sunder the panicle, had insignificant correlations with panicle length, panicle weight, length of the 1st and 2nd internodes under the panicle, diameter of the 1st, 2nd, 3rd and 4th internodes under the panicle, diameter of the 5th node under the panicle. [Conclu-sion] More attention should be paid to the main factors affecting lodging resistance in breeding to improve lodging resistance of plateau japonica rice.展开更多
[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering anal...[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.展开更多
With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with th...With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with the emissions of do- mestic wastewater, the discharge amount of pollutants has exceeded standard in many cities, which not only pollutes the water resources, but also greatly threatens the environment, and does great harm to people＇s health. The principal component analysis was conducted based on the principal components extracted from the data of major pollutants emission conditions in the wastewater of major cities from the China Statistical Yearbook 2014.展开更多
Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Althou...Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.展开更多
The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal compon...The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
文摘Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.
基金supported by the Natural Science Foundation of China(Grant Nos.42374032,42174103,42004073)Provincial Natural Science Foundation(2024JJ8348)the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y01)。
文摘The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.
基金supported by the National Natural Science Foundation of China(No.42177177).
文摘It is of critical importance to determine the endpoint of stabilization for landfills that are stabilized by aeration acceleration.Current stabilization evaluation methods are inconsistent and mostly fail to account for the effect of oxygen concentration.In this study,degradation experiments were conducted to quantitatively analyze the impact of oxygen on microbial communities and metabolic functions.High-throughput sequencing analysis demonstrated that an oxygen concentration exceeding 10%significantly enhances amino acid metabolism,secondary metabolite biosynthesis,and exogenous biodegradation.Three-dimensional fluorescence data were analyzed using the PARAFAC method,and a novel fluorescence-based stabilization indicator was proposed based on the ratio of fulvic-like to tyrosine-like substances.When the growth multiples of the fluorescence index exceed 10-fold,it can be inferred that degradation has been met the stabilization endpoint.Principal component analysis was employed to establish multiple regression equations between the physicochemical parameters of landfill waste and dissolved fluorescent substants,offering an innovative insight to evaluate the stabilization process of aerated landfills.
基金supported by the National Key Research and Development Plan of China(No.2023YFB3406500)the National Natural Science Foundation of China(No.52475132)+2 种基金the Aeronautical Science Foundation of China(No.20200015053001)the Shaanxi Key Research Program Project,China(No.2024GX-ZDCYL-01–16)the Xi’an Key Industrial Chain Technology Research Project,China(No.2023JH-RGZNGG-0033)。
文摘Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
基金supported by the National Natural Science Foundation of China(Grant Numbers 42374195 and 42188101)the fellowship of China National Postdoctoral Program for Innovative Talents(Grant Number BX20230273)+1 种基金the Hubei Provincial Natural Science Foundation of China(Grant Number 2024AFB-097)the Postdoctor Project of Hubei Province(Grant Number 2024HBBHCXA054).
文摘This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104075 and 12347101).
文摘We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.
基金The National Natural Science Foundation of China(No.6120134461271312+7 种基金6140108511301074)the Research Fund for the Doctoral Program of Higher Education(No.20120092120036)the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031)Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11)"333"Project(No.BRA2015288)High-End Foreign Experts Recruitment Program(No.GDT20153200043)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
文摘[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.
基金Supported by the Fund of Anhui Provincial Tobacco Monopoly Bureau(AHKJ2008-03)Anhui Provincial University Key Project of Natural Science(KJ2010A114)Undergraduate Student Science and Technology Innovation Fund of Anhui Agricultural University(2010233)~~
文摘[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.
文摘In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
文摘This research uses eigenvalue characteristics of nonlinear principal component analysis （NLPCA） and principal component analysis （PCA） to investigate total electron content （TEC） anomalies associated with Taiwan＇s Chi-Chi earthquake of 21 September 1999 （LT） （M_w=7.6）. The transforms are used for ionospheric TEC from 01 August to 20 September 1999 （local time） using data from 13 GPS receivers. The data were collected at 22°N-26°N Lat. and 120°E-122°E Long.. Applying the NLPCA to the multi-channel total electron content records of GPS receivers, the earthquake-associated TEC anomalies were represented by large principal eigenvalues of NLPCA （〉0.5 in a normalized set） on 14 August and 17, 18, and 20 September, with allowance given for the Dst index, which was quiet for the study period. Comparisons were then made with other researchers who also found TEC anomalies on September 17, 18, and 19 associated with the Chi-Chi earthquake, which cannot be detected by PCA.Consideration is also given for reported ground level geomagnetic field activity that occurred between mid-August and late October, leading up to and including the Chi-Chi and Chia-Yi earthquakes, which are associated with the same series of faults. It is possible that Aug. 14 is representative of an earthquake-associated TEC anomaly. This is an interesting result given how much earlier than the earthquake it occurred.
基金Supported by Program for the Breeding and Industrial Development of Conventional Rice Varieties(2010BB013)Training Plan of Technological Innovation Talents of Yunnan Province(2010CI075)~~
文摘Objective] This study was conducted to investigate the main factors affect-ing the lodging resistance of plateau japonica rice. [Method] Twenty agronomic traits related to lodging resistance of plateau japonica rice were analyzed by principal component analysis and correlation analysis among 26 varieties/lines of plateau japonica rice. [Result] The lodging resistance of the 26 varieties/lines had great dif-ference among different agronomic traits. Plant height, and wal thickness of the 4th, 3rd and 2nd internodes under the panicle had the most important influence on lodging resistance, while the diameter of the 3rd, 2nd, 4th, 1st nodes under the panicle, length of the 4th and 3rd internodes under the panicle, wal thickness of the 1st internode under the panicle had less influence. The other nine agronomic traits of rice culm did not affect or indirectly affected lodging resistance through above-mentioned agro-nomic traits. Lodging resistance had significant correlations with plant height, length of the 4th and 3rd internodes under the panicle, wal thickness of the 1st, 2nd, 3rd and 4th internodes under the panicle and diameter of the 1st, 2nd, 3rd and 4th node sunder the panicle, had insignificant correlations with panicle length, panicle weight, length of the 1st and 2nd internodes under the panicle, diameter of the 1st, 2nd, 3rd and 4th internodes under the panicle, diameter of the 5th node under the panicle. [Conclu-sion] More attention should be paid to the main factors affecting lodging resistance in breeding to improve lodging resistance of plateau japonica rice.
基金Supported by Fund of Sichuan Provincial Administration of traditional Chinese Medicine(2008-12)~~
文摘[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.
文摘With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with the emissions of do- mestic wastewater, the discharge amount of pollutants has exceeded standard in many cities, which not only pollutes the water resources, but also greatly threatens the environment, and does great harm to people＇s health. The principal component analysis was conducted based on the principal components extracted from the data of major pollutants emission conditions in the wastewater of major cities from the China Statistical Yearbook 2014.
基金the National Natural Science Foundation of China （No.60421002）.
文摘Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(No.51974023)State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(No.41621005)。
文摘The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
