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
Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
As a main charging burden of blast furnace(BF)ironmaking process,pellets play an important role in ironmaking process.However,compared with sinters,there are some inevitable disadvantages for traditional acid pellets,...As a main charging burden of blast furnace(BF)ironmaking process,pellets play an important role in ironmaking process.However,compared with sinters,there are some inevitable disadvantages for traditional acid pellets,e.g.,reduction swell,low melting temperature.Therefore,the fluxed-pellets have been applied in BF,especially MgO-fluxed pellets.In the present study,the effects of category and content of MgO bearing additive on the compressive strength(CS),reduction swelling index(RSI),reduction disintegration index(RDI)and melting-dripping properties of the pellets were investigated.Minerals composition,pore distribution and microstructure of MgO-flux pellets were studied by X-ray powder diffraction(XRD),mercury intrusion method and scanning electron microscopy(SEM),respectively.The results show that the light burned magnesite(LBM)is more suitable MgO bearing additive for fluxed-pellets.With increasing LBM content from 0 to 2.0%,the CS decreases from 3066 to 2689 N,RSI decreases from 16.43%to 9.97%and RDI decreases from 19.2%to 12.99%.The most appropriate MgO bearing additive content in the fluxed-pellets is 2.0%according to principal component analysis(PCA).展开更多
To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
The relationship between Solidago canadensis L. invasion and soil microbial community diversity including functional and structure diversities was studied across the invasive gradients varying from 0 to 40%, 80%, and ...The relationship between Solidago canadensis L. invasion and soil microbial community diversity including functional and structure diversities was studied across the invasive gradients varying from 0 to 40%, 80%, and 100% coverage of Solidago canadensis L. using sole carbon source utilization profiles analyses, principle component analysis （PCA） and phospholipid fatty acids （PLFA） profiles analyses. The results show the characteristics of soil microbial community functional and structure diversity in invaded soils strongly changed by Solidago canadensis L. invasion. Solidago canadensis L. invasion tended to result in higher substrate richness, and functional diversity. As compared to the native and ecotones, average utilization of specific substrate guilds of soil microbe was the highest in Solidago canadensis L. monoculture. Soil microbial functional diversity in Solidago canadensis L. monoculture was distinctly separated from the native area and the ecotones. Aerobic bacteria, fungi and actinomycetes population significantly increased but anaerobic bacteria decreased in the soil with Solidago canadensis L. monoculture. The ratio of cyl9：0 to 18：1 co7 gradually declined but mono/sat and fung/bact PLFAs increased when Solidago canadensis L. became more dominant. The microbial community composition clearly separated the native soil from the invaded soils by PCA analysis, especially 18： lco7c, 16： lco7t, 16： lco5c and 18：2co6, 9 were present in higher concentrations for exotic soil. In conclusion, Solidago canadensis L. invasion could create better soil conditions by improving soil microbial community structure and functional diversity, which in turn was more conducive to the growth ofSolidago canadensis L.展开更多
Source apportionment of particulate matters with aerodynamic diameter less than 10 μm （PM10） was conducted in the suburban area of Changsha, China. PM10 samples for 24 h collected with TEOM 1400a and ACCU system in...Source apportionment of particulate matters with aerodynamic diameter less than 10 μm （PM10） was conducted in the suburban area of Changsha, China. PM10 samples for 24 h collected with TEOM 1400a and ACCU system in July and October 2008 were chemically analyzed by the wavelength dispersive X-ray fluorescence （WD-XRF）. Source appointment was implemented by the principal component analysis/absolute principal component analysis （PCA/APCA） to identify the possible sources and to quantify the contributions of the sources to PM10. Results show that as the PM10 concentration is increased from （85.6±43.7） μg/m3 in July 2008 to （107.6±35.7） μg/m^3 in October 2008, the concentrations of the anthropogenic elements （P, S, C1, K, Mn, Ni, Cu, Zn, and Pb） are basically increased but concentrations of the natural elements （Na, Mg, Al, Si, Ca, Ti, and Fe） are essentially decreased. Six main sources of PM10 are identified in the suburban of Changsha, China： soil dust, secondary aerosols, domestic oil combustion, waste incineration, traffic emission, and industrial emission contribute 57.7%, 24.0%, 9.8%, 5.0%, 2.0%, and 1.5%, respectively. Soil dust and secondary aerosols are the two major sources of particulate air pollution in suburban area of Changsha, China, so effective measures should be taken to control these two particulate pollutants.展开更多
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal ...Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.展开更多
A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ...A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.展开更多
Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving wa...Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
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
A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized ...Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.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National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
基金Projects(51874080,51604069)supported by the National Natural Science Foundation of ChinaProject(N162504004)supported by the Fundamental Research Funds for the Central Universities,China
文摘As a main charging burden of blast furnace(BF)ironmaking process,pellets play an important role in ironmaking process.However,compared with sinters,there are some inevitable disadvantages for traditional acid pellets,e.g.,reduction swell,low melting temperature.Therefore,the fluxed-pellets have been applied in BF,especially MgO-fluxed pellets.In the present study,the effects of category and content of MgO bearing additive on the compressive strength(CS),reduction swelling index(RSI),reduction disintegration index(RDI)and melting-dripping properties of the pellets were investigated.Minerals composition,pore distribution and microstructure of MgO-flux pellets were studied by X-ray powder diffraction(XRD),mercury intrusion method and scanning electron microscopy(SEM),respectively.The results show that the light burned magnesite(LBM)is more suitable MgO bearing additive for fluxed-pellets.With increasing LBM content from 0 to 2.0%,the CS decreases from 3066 to 2689 N,RSI decreases from 16.43%to 9.97%and RDI decreases from 19.2%to 12.99%.The most appropriate MgO bearing additive content in the fluxed-pellets is 2.0%according to principal component analysis(PCA).
基金Project(52005358)supported by the National Natural Science Foundation of ChinaProject(2018YFB1307902)supported by the National Key R&D Program of China+1 种基金Project(201901D111243)supported by the Natural Science Foundation of Shanxi Province,ChinaProject(2019-KF-25-05)supported by the Natural Science Foundation of Liaoning Province,China。
文摘To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
基金Project(2009QNA6015) supported by the Fundamental Research Funds for the Central Universities of ChinaProject(Y3110055)supported by the Natural Science Foundation of Zhejiang Province,ChinaProject(Y200803219) supported by the Foundation of Zhejiang Educational Committee of China
文摘The relationship between Solidago canadensis L. invasion and soil microbial community diversity including functional and structure diversities was studied across the invasive gradients varying from 0 to 40%, 80%, and 100% coverage of Solidago canadensis L. using sole carbon source utilization profiles analyses, principle component analysis （PCA） and phospholipid fatty acids （PLFA） profiles analyses. The results show the characteristics of soil microbial community functional and structure diversity in invaded soils strongly changed by Solidago canadensis L. invasion. Solidago canadensis L. invasion tended to result in higher substrate richness, and functional diversity. As compared to the native and ecotones, average utilization of specific substrate guilds of soil microbe was the highest in Solidago canadensis L. monoculture. Soil microbial functional diversity in Solidago canadensis L. monoculture was distinctly separated from the native area and the ecotones. Aerobic bacteria, fungi and actinomycetes population significantly increased but anaerobic bacteria decreased in the soil with Solidago canadensis L. monoculture. The ratio of cyl9：0 to 18：1 co7 gradually declined but mono/sat and fung/bact PLFAs increased when Solidago canadensis L. became more dominant. The microbial community composition clearly separated the native soil from the invaded soils by PCA analysis, especially 18： lco7c, 16： lco7t, 16： lco5c and 18：2co6, 9 were present in higher concentrations for exotic soil. In conclusion, Solidago canadensis L. invasion could create better soil conditions by improving soil microbial community structure and functional diversity, which in turn was more conducive to the growth ofSolidago canadensis L.
基金Project (FANEDD 200545) supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China Project (50408019) supported by the National Natural Science Foundation of China Project (2008BAJ12B03) supported by National Key Project of Scientific and Technical Supporting Programs of China
文摘Source apportionment of particulate matters with aerodynamic diameter less than 10 μm （PM10） was conducted in the suburban area of Changsha, China. PM10 samples for 24 h collected with TEOM 1400a and ACCU system in July and October 2008 were chemically analyzed by the wavelength dispersive X-ray fluorescence （WD-XRF）. Source appointment was implemented by the principal component analysis/absolute principal component analysis （PCA/APCA） to identify the possible sources and to quantify the contributions of the sources to PM10. Results show that as the PM10 concentration is increased from （85.6±43.7） μg/m3 in July 2008 to （107.6±35.7） μg/m^3 in October 2008, the concentrations of the anthropogenic elements （P, S, C1, K, Mn, Ni, Cu, Zn, and Pb） are basically increased but concentrations of the natural elements （Na, Mg, Al, Si, Ca, Ti, and Fe） are essentially decreased. Six main sources of PM10 are identified in the suburban of Changsha, China： soil dust, secondary aerosols, domestic oil combustion, waste incineration, traffic emission, and industrial emission contribute 57.7%, 24.0%, 9.8%, 5.0%, 2.0%, and 1.5%, respectively. Soil dust and secondary aerosols are the two major sources of particulate air pollution in suburban area of Changsha, China, so effective measures should be taken to control these two particulate pollutants.
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
基金Project(1390/2)supported by Khuzestan Gas Company,Iran
文摘Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.
文摘A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.
基金Project(2012ZX07501002-001)supported by Major Science and Technology Program for Water Pollution Control and Treatment of the Ministry of Science and Technology,China
文摘Assessment of temporal and spatial variations in surface water quality is important to evaluate the health of a watershed and make necessary management decisions to control current and future pollution of receiving water bodies. In this work, surface water quality data for 12 physical and chemical parameters collected from 10 sampling sites in the Nenjiang River basin during the years(2012-2013) were analyzed. The results show that river water quality has significant temporal and spatial variations. Hierarchical cluster analysis(HCA) grouped 12 months into three periods(LF, MF and HF) and classified 10 monitoring sites into three regions(LP, MP and HP) based on the similarity of water quality characteristics. The principle component analysis(PCA)/factor analysis(FA) was used to recognize the factors or origins responsible for temporal and spatial water quality variations. Temporal and spatial PCA/FA revealed that the Nenjiang River water chemistry was strongly affected by rock/water interaction, hydrologic processes and anthropogenic activities. This work demonstrates that the application of HCA and PCA/FA has achieved meaningful classification based on temporal and spatial criteria.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
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.
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金Project ( 2001AA411040 ) supported by the National High Technology Development Program of China project(2002CB312200) supported by the National Fundamental Research and Development Program of China
文摘Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.
