The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).A...The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.展开更多
In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purpos...In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.展开更多
The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of build...The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.展开更多
As members of a super gene family, WRKY transcription factors are widely distributed in higher plants. ln this study, bioinformatic analysis of WRKY35, a member of the WRKY gene family, was carried out. Results indica...As members of a super gene family, WRKY transcription factors are widely distributed in higher plants. ln this study, bioinformatic analysis of WRKY35, a member of the WRKY gene family, was carried out. Results indicated that tran-scription factor WRKY35 harbors a WRKYGQK core domain and a Cys2His2 or Cys2His/Cys zinc finger in the 5’ end without transmembrane domain. After PCR amplification and restriction digestion, WRKY35 gene fragment was ligated to prokaryotic expression vector PET28. This study provided basis for expression anal-ysis of WRKY35 protein and subsequent functional identification of WRKY35 gene.展开更多
The vibration characteristics and dynamic responses of rock and soil under seismic load can be estimated with dynamic finite element method （DFEM）. Combining with the DFEM, the vector sum analysis method （VSAM） is...The vibration characteristics and dynamic responses of rock and soil under seismic load can be estimated with dynamic finite element method （DFEM）. Combining with the DFEM, the vector sum analysis method （VSAM） is employed in seismic stability analysis of a slope in this paper. Different from other conventional methods, the VSAM is proposed based on the vector characteristic of force and current stress state of the slope. The dynamic stress state of the slope at any moment under seismic load can he obtained by the DFEM, thus the factor of safety of the slope at any moment during earthquake can be easily obtained with the VSAM in consideration of the DFEM. Then, the global stability of the slope can be estimated on the basis of time-history curve of factor of safety and reliability theory. The VSAM is applied to a homogeneous slope under seismic load. The factor of safety of the slope is 1.30 under gravity only and the dynamic factor of safety under seismic load is 1.21. The calculating results show that the dynamic characteristics and stability state of the slope with input ground motion can be actually analyzed. It is believed that the VSAM is a feasible and practical approach to estimate the dynamic stability of slopes under seismic load.展开更多
Change vector analysis (CVA) and principal component analysis in NDVI time-trajectories space are powerful tools to analyze land-cover change. The magnitude of the change vector indicates amplitude of the change, whil...Change vector analysis (CVA) and principal component analysis in NDVI time-trajectories space are powerful tools to analyze land-cover change. The magnitude of the change vector indicates amplitude of the change, while its direction indicates the nature of the change. CVA is applied to two remotely sensed indicators of land surface conditions, NDVI and spatial structure, in order to improve the capability to detect and categorize land-cover change. The magnitude and type of changes are calculated in China from 1989 to 1999. Through the research, the main conclusions are drawn as follows: 1) The changes of NDVI are quite different between eastern China and western China, and the change range in the east is bigger than that in the west. The trend in NDVI time series is smoothly increasing, the increases happen mostly in Taiwan, Fujian, Sichuan and Henan provinces and the decreases occur in Yunnan and Xinjiang. 2) The spatial structure index can indicate changes in the seasonal ecosystem dynamics for spatially heterogeneous landscapes. Most of spatial structure changes, which occurred in southern China, correlated with vegetation growth processes and strike of mountains.展开更多
Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is...Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.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
The excellent vectoring characteristic of Dual Synthetic Jet(DSJ)provides a new control strategy for the active flow control,such as thrust vectoring control,large area cooling,separated flow control and so on.For inc...The excellent vectoring characteristic of Dual Synthetic Jet(DSJ)provides a new control strategy for the active flow control,such as thrust vectoring control,large area cooling,separated flow control and so on.For incompressible flow,the influence relation of source variables,such as structure parameters of actuators,driving parameters and material attributes of piezoelectric vibrating diaphragm,on the vectoring DSJ and a theoretical model are established based on theoretical and regression analysis,which are all verified by numerical simulations.The two synthetic jets can be deemed as a main flow with a higher jet velocity and a disturbing flow with a lower jet velocity.The results indicate that the influence factors contain the low-pressure area formed at the exit of the disturbing flow,which could promote the vectoring deflection,and the impact effect of the disturbing flow and the suppressive effect of the main flow with the effect of restraining the vectoring deflection.The vectoring angle is a complex parameter coupled by all source variables.The detailed theoretical model,whose error is controlled within 3.6 degrees,can be used to quantitatively assess the vectoring feature of DSJ and thus to provide a guidance for designing the control law applied in the active flow control.展开更多
Robust numerical models that describe the complex behaviors of risers are needed because these constitute dynamically sensitive systems. This paper presents a simple and efficient algorithm for the nonlinear static an...Robust numerical models that describe the complex behaviors of risers are needed because these constitute dynamically sensitive systems. This paper presents a simple and efficient algorithm for the nonlinear static and dynamic analyses of marine risers. The proposed approach uses the vector form intrinsic finite element(VFIFE) method, which is based on vector mechanics theory and numerical calculation. In this method, the risers are described by a set of particles directly governed by Newton's second law and are connected by weightless elements that can only resist internal forces. The method does not require the integration of the stiffness matrix, nor does it need iterations to solve the governing equations. Due to these advantages, the method can easily increase or decrease the element and change the boundary conditions, thus representing an innovative concept of solving nonlinear behaviors, such as large deformation and large displacement. To prove the feasibility of the VFIFE method in the analysis of the risers, rigid and flexible risers belonging to two different categories of marine risers, which usually have differences in modeling and solving methods, are employed in the present study. In the analysis, the plane beam element is adopted in the simulation of interaction forces between the particles and the axial force, shear force, and bending moment are also considered. The results are compared with the conventional finite element method(FEM) and those reported in the related literature. The findings revealed that both the rigid and flexible risers could be modeled in a similar unified analysis model and that the VFIFE method is feasible for solving problems related to the complex behaviors of marine risers.展开更多
Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v...Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.展开更多
This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC).Firstly,we applied a pose feature extracted from silhouette image by employing Procrustes analysi...This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC).Firstly,we applied a pose feature extracted from silhouette image by employing Procrustes analysis and local preserving projection(LPP).Secondly,the extracted feature can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation,rotation and scaling.Finally,after the pose feature was extracted,a recognition framework based on FVC and multi-class supporting vector machine was employed to classify the human action.Experimental results on benchmarks demonstrate the effectiveness of the proposed method.展开更多
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
Three new vorticity vectors have been proposed by Gao et al to study the two-dimensional tropical convection. In the present paper, phase relations between surface rain rate and the vorticity vectors are analysed with...Three new vorticity vectors have been proposed by Gao et al to study the two-dimensional tropical convection. In the present paper, phase relations between surface rain rate and the vorticity vectors are analysed with the calculations of lag correlation coefficients based on hourly zonally-averaged mass-integrated cloud-resolving simulation data. The cloud-resolving model is integrated with the vertical velocity, zonal wind, horizontal thermal and moisture advections, and sea surface temperature observed and derived from tropical ocean global atmosphere - coupled ocean atmosphere response experiment （TOGA-COARE） for 10 days. Maximum local increase of the vertical component of the convective vorticity vector leads maximum surface rain rate by 2 hours mainly due to the interaction between vorticity and zonal gradient of ice heating. While maximum local increase of the vertical component of the moist vorticity vector leads maxfinum surface rain rate by 2 hours mainly because of the interaction between zonal specific humidity gradient and zonal buoyancy gradient. And the maximum local decrease of the zonal component of the dynamic vorticity vector leads maximum surface rain rate by 2 hours mainly due to the interactions between vorticity and vertical pressure gradient as well as vorticity and buoyancy.展开更多
Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawin...Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.展开更多
This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based sche...This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.展开更多
In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Ga...In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.展开更多
On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process m...On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.展开更多
Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters.However,because of the complexity and non-stationary of rainfall data,it is difficult ...Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters.However,because of the complexity and non-stationary of rainfall data,it is difficult to forecast.In this paper,a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method.Firstly,SSA is used for extracting the trend components of the hydrological data.Then,SVR is utilized to deal with the volatility and irregularity of the precipitation series.Finally,the parameter of SVR is optimized by DA.The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai,Panshui,Lanma and Jiulongchi stations.To validate the efficiency of the method,four compared models,DA-SVR,SSA-GWO-SVR,SSA-PSO-SVR and SSA-CS-SVR are established.The result shows that the proposed method has the best performance among all five models,and its prediction has high precision and accuracy.展开更多
A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
基金The National Key Research and Development Program of China under contract Nos 2017YFC1501803 and2018YFC1506903the National Natural Science Foundation of China under contract Nos 91730304,41475021 and 41575026
文摘The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.
基金The Fundamental Research Funds for the Central Universities,the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX_0177)
文摘In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.
基金Supported by Scientific Research Project for Commonwealth (GYHY200806017)Innovation Project for Graduate of Jiangsu Province (CX09S-018Z)
文摘The evaluation model was established to estimate the number of houses collapsed during typhoon disaster for Zhejiang Province.The factor leading to disaster,the environment fostering disaster and the exposure of buildings were processed by Principal Component Analysis.The key factor was extracted to support input of vector machine model and to build an evaluation model;the historical fitting result kept in line with the fact.In the real evaluation of two typhoons landed in Zhejiang Province in 2008 and 2009,the coincidence of evaluating result and actual value proved the feasibility of this model.
基金Supported by National Natural Science Foundation of China "Functional analysis of transcription factor AtWRKY28 in development and morphogenesis of Orychophragmus violaceus"(31360262)Key Agricultural Science and Technology Project of Department of Science and Technology of Guizhou Province "Molecular Marker Development and Assisted Breeding of Recessive Epistatic Genic Male Sterile Lines of Rapeseed"(QKHNYZ[2012]3033)Special Fund of Guizhou Academy of Agricultural Sciences "BnaC.Tic40 (tic40) and BnRf (rf) Marker-assisted Breeding of Recessive Genic Male Sterile Three Lines of Rapeseed"(QNKYYZX[2012]002)~~
文摘As members of a super gene family, WRKY transcription factors are widely distributed in higher plants. ln this study, bioinformatic analysis of WRKY35, a member of the WRKY gene family, was carried out. Results indicated that tran-scription factor WRKY35 harbors a WRKYGQK core domain and a Cys2His2 or Cys2His/Cys zinc finger in the 5’ end without transmembrane domain. After PCR amplification and restriction digestion, WRKY35 gene fragment was ligated to prokaryotic expression vector PET28. This study provided basis for expression anal-ysis of WRKY35 protein and subsequent functional identification of WRKY35 gene.
基金Supported by the Program of Yunnan Provincial Institute of Communications Planning,Design and Research (2011(D)11-b)
文摘The vibration characteristics and dynamic responses of rock and soil under seismic load can be estimated with dynamic finite element method （DFEM）. Combining with the DFEM, the vector sum analysis method （VSAM） is employed in seismic stability analysis of a slope in this paper. Different from other conventional methods, the VSAM is proposed based on the vector characteristic of force and current stress state of the slope. The dynamic stress state of the slope at any moment under seismic load can he obtained by the DFEM, thus the factor of safety of the slope at any moment during earthquake can be easily obtained with the VSAM in consideration of the DFEM. Then, the global stability of the slope can be estimated on the basis of time-history curve of factor of safety and reliability theory. The VSAM is applied to a homogeneous slope under seismic load. The factor of safety of the slope is 1.30 under gravity only and the dynamic factor of safety under seismic load is 1.21. The calculating results show that the dynamic characteristics and stability state of the slope with input ground motion can be actually analyzed. It is believed that the VSAM is a feasible and practical approach to estimate the dynamic stability of slopes under seismic load.
基金National Natural Science Foundation of China No. 30000027 No.39899374
文摘Change vector analysis (CVA) and principal component analysis in NDVI time-trajectories space are powerful tools to analyze land-cover change. The magnitude of the change vector indicates amplitude of the change, while its direction indicates the nature of the change. CVA is applied to two remotely sensed indicators of land surface conditions, NDVI and spatial structure, in order to improve the capability to detect and categorize land-cover change. The magnitude and type of changes are calculated in China from 1989 to 1999. Through the research, the main conclusions are drawn as follows: 1) The changes of NDVI are quite different between eastern China and western China, and the change range in the east is bigger than that in the west. The trend in NDVI time series is smoothly increasing, the increases happen mostly in Taiwan, Fujian, Sichuan and Henan provinces and the decreases occur in Yunnan and Xinjiang. 2) The spatial structure index can indicate changes in the seasonal ecosystem dynamics for spatially heterogeneous landscapes. Most of spatial structure changes, which occurred in southern China, correlated with vegetation growth processes and strike of mountains.
基金Project supported by the National Natural Science Foundation of China(Grant No.11075184)the Knowledge Innovation Program of the Chinese Academy of Sciences(CAS)(Grant No.Y03RC21124)the CAS President’s International Fellowship Initiative Foundation(Grant No.2015VMA007)
文摘Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.
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.
基金supported by the National Natural Science Foundation of China(Nos.11972369 and 11872374)。
文摘The excellent vectoring characteristic of Dual Synthetic Jet(DSJ)provides a new control strategy for the active flow control,such as thrust vectoring control,large area cooling,separated flow control and so on.For incompressible flow,the influence relation of source variables,such as structure parameters of actuators,driving parameters and material attributes of piezoelectric vibrating diaphragm,on the vectoring DSJ and a theoretical model are established based on theoretical and regression analysis,which are all verified by numerical simulations.The two synthetic jets can be deemed as a main flow with a higher jet velocity and a disturbing flow with a lower jet velocity.The results indicate that the influence factors contain the low-pressure area formed at the exit of the disturbing flow,which could promote the vectoring deflection,and the impact effect of the disturbing flow and the suppressive effect of the main flow with the effect of restraining the vectoring deflection.The vectoring angle is a complex parameter coupled by all source variables.The detailed theoretical model,whose error is controlled within 3.6 degrees,can be used to quantitatively assess the vectoring feature of DSJ and thus to provide a guidance for designing the control law applied in the active flow control.
基金supported by the National Key Research and Development Program (No. 2016YFC0802301)the Shandong Province Science and Technology Major Project (No. 2015ZDZX04003)the Natural Science Foundation of Shandong Province (No. ZR2016GM06)
文摘Robust numerical models that describe the complex behaviors of risers are needed because these constitute dynamically sensitive systems. This paper presents a simple and efficient algorithm for the nonlinear static and dynamic analyses of marine risers. The proposed approach uses the vector form intrinsic finite element(VFIFE) method, which is based on vector mechanics theory and numerical calculation. In this method, the risers are described by a set of particles directly governed by Newton's second law and are connected by weightless elements that can only resist internal forces. The method does not require the integration of the stiffness matrix, nor does it need iterations to solve the governing equations. Due to these advantages, the method can easily increase or decrease the element and change the boundary conditions, thus representing an innovative concept of solving nonlinear behaviors, such as large deformation and large displacement. To prove the feasibility of the VFIFE method in the analysis of the risers, rigid and flexible risers belonging to two different categories of marine risers, which usually have differences in modeling and solving methods, are employed in the present study. In the analysis, the plane beam element is adopted in the simulation of interaction forces between the particles and the axial force, shear force, and bending moment are also considered. The results are compared with the conventional finite element method(FEM) and those reported in the related literature. The findings revealed that both the rigid and flexible risers could be modeled in a similar unified analysis model and that the VFIFE method is feasible for solving problems related to the complex behaviors of marine risers.
基金Projects(2013BAB02B01,2013BAB02B03)supported by the National Key Technologies R&D Program of ChinaProjects(41072224,41272347)supported by the National Natural Science Foundation of China
文摘Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.
基金National Natural Science Foundation of China(No.61602148)Natural Science Foundation of Fujian Province,China(No.2016J01040)Xiamen University of Technology High Level Talents Project,China(No.YKJ15018R)
文摘This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC).Firstly,we applied a pose feature extracted from silhouette image by employing Procrustes analysis and local preserving projection(LPP).Secondly,the extracted feature can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation,rotation and scaling.Finally,after the pose feature was extracted,a recognition framework based on FVC and multi-class supporting vector machine was employed to classify the human action.Experimental results on benchmarks demonstrate the effectiveness of the proposed method.
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 40433007 and 40505012)the Knowledge Innovation Program of the Chinese Academy of Sciences (IAP07214)"Outstanding Oversea Scholars" project (Grant No 2005-2-17)
文摘Three new vorticity vectors have been proposed by Gao et al to study the two-dimensional tropical convection. In the present paper, phase relations between surface rain rate and the vorticity vectors are analysed with the calculations of lag correlation coefficients based on hourly zonally-averaged mass-integrated cloud-resolving simulation data. The cloud-resolving model is integrated with the vertical velocity, zonal wind, horizontal thermal and moisture advections, and sea surface temperature observed and derived from tropical ocean global atmosphere - coupled ocean atmosphere response experiment （TOGA-COARE） for 10 days. Maximum local increase of the vertical component of the convective vorticity vector leads maximum surface rain rate by 2 hours mainly due to the interaction between vorticity and zonal gradient of ice heating. While maximum local increase of the vertical component of the moist vorticity vector leads maxfinum surface rain rate by 2 hours mainly because of the interaction between zonal specific humidity gradient and zonal buoyancy gradient. And the maximum local decrease of the zonal component of the dynamic vorticity vector leads maximum surface rain rate by 2 hours mainly due to the interactions between vorticity and vertical pressure gradient as well as vorticity and buoyancy.
文摘Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.
基金Supported by the Natural Science Foundation ofHubei Province (2005ABA256)
文摘This article presents an anomaly detection system based on principal component analysis （PCA） and support vector machine （SVM）. The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.
文摘In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.
文摘On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.
文摘Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters.However,because of the complexity and non-stationary of rainfall data,it is difficult to forecast.In this paper,a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method.Firstly,SSA is used for extracting the trend components of the hydrological data.Then,SVR is utilized to deal with the volatility and irregularity of the precipitation series.Finally,the parameter of SVR is optimized by DA.The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai,Panshui,Lanma and Jiulongchi stations.To validate the efficiency of the method,four compared models,DA-SVR,SSA-GWO-SVR,SSA-PSO-SVR and SSA-CS-SVR are established.The result shows that the proposed method has the best performance among all five models,and its prediction has high precision and accuracy.
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
