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
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components o...As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.展开更多
Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPM...Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPMAS-^(13)C-NMR)and ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS)to characterize the chemical components of humic acids.Tomato seed germination index(GI)was determined with the goal of screening the key active components of humic acids.Humic acids had a significantly higher nutrient content,except for the total nitrogen(TN)and the total phosphorus(TP)contents.Humic acids had a higher content of O-CH_(3)/NCH,aromatic C-O and carbonyl C compared to weathered coal,with significantly lower anomeric C,aromatic C and O-alkyl C/alkyl C.There were 611 different compounds identified among the test materials using UHPLC-MS.Humic acids also had a significantly higher GI(158.0%and 153.1%)than weathered coal(85.5%).The organic matter(OM),TP and available potassium(AK)contents in humic acids were significantly positively correlated with GI,and available phosphorus(AP)was significantly negatively correlated.Among the carbon components,O-CH3/NCH,aromatic C-O and O-alkyl C/alkyl C were significantly positively correlated with GI,while anomeric C was significantly negatively correlated.Furthermore,among the top 10 positive and five negative correlation compounds,lipids and lipid-like molecules[armexifolin,boviquinone 4,3-methyladipic acid,lxocarpalactone A,monic acid,DG(20:1(11Z)/18:4(6Z,9Z,12Z,15Z)/0:0),and brassinolide]and organic acids and derivatives(N-acetylglutamic acid,8-hydroxy-5,6-octadienoic acid,acetyl-L-tyrosine,and hydroxyprolyl-methionine)in humic acids might be crucial active components for improving tomato seed germination.The results provided direct evidence for the identification of bioactive molecules of humic acids,and a scientific basis for the precise utilization of bioactive molecular components of humic acids in sustainable agricultural development.展开更多
Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conve...Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conversed into the independent indexes of principal component based on the principal component analysis method. The results showed that the rice kernel types influenced the meanings of principal components indexes. For long and short rice, the first principal component was comprehensive index. But the second principal component was springiness for the short rice, while it was adhesiveness for long rice. Therefore, the first principal component can be used to express the quality of cooked rice with a few of indexes, and the rice type can be recognized according to the second principal component.展开更多
Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could n...Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.展开更多
To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on princip...To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.展开更多
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t...A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.展开更多
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited...A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.展开更多
The effect of key parameters on fatigue life of common metallic components, e.g. steel, aluminium alloy, has been analysed quantitatively. The influential coverage and degree of these parameters have been investigated...The effect of key parameters on fatigue life of common metallic components, e.g. steel, aluminium alloy, has been analysed quantitatively. The influential coverage and degree of these parameters have been investigated systematically, some phenomena which can′t be discovered with qualitative analysis method have been revealed, a series of valuable conclusions has been obtained, which would be very beneficial to fatigue resistant design and improvement of anti fatigue ability of metallic components.展开更多
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
The cohesion weakening and friction strengthening(CWFS)model for rock reveals the strength components mobilization process during progressive brittle failure process of rock,which is very helpful in understanding mech...The cohesion weakening and friction strengthening(CWFS)model for rock reveals the strength components mobilization process during progressive brittle failure process of rock,which is very helpful in understanding mechanical properties of rock.However,the used incremental cyclic loading−unloading compression test for the determination of strength components is very complicated,which limits the application of CWFS model.In this paper,incremental cyclic loading−unloading compression test was firstly carried out to study the evolution of deformation and the strength properties of Beishan granite after various temperatures treated under different confining pressures.We found the axial and lateral unloading modulus are closely related to the applied stress and damage state of rock.Based on these findings,we can accurately determine the plastic strain during the entire failure process using conventional tri-axial compression test data.Furthermore,a strength component(cohesive and frictional strength)determination method was developed using conventional triaxial compression test.Using this method,we analyzed the variation of strength mobilization and deformation properties of Beishan granite after various temperatures treated.At last,a non-simultaneous strength mobilization model for thermally treated granite was obtained and verified by numerical simulation,which demonstrated the effectiveness of the proposed strength determination method.展开更多
Determining the number of components is a crucial issue in a mixture model. A moment-based criterion is considered to estimate the number of components arising from a normal mixture model. This criterion is derived fr...Determining the number of components is a crucial issue in a mixture model. A moment-based criterion is considered to estimate the number of components arising from a normal mixture model. This criterion is derived from an omnibus statistic involving the skewness and kurtosis of each component. The proposed criterion additionally provides a measurement for the model fit in an absolute sense. The performances of our criterion are satisfactory compared with other classical criteria through Monte-Carlo experiments.展开更多
This trial was conducted to evaluate the effect of quercetin on egg quality and components in laying hens of different weeks. A total of 240 healthy Hessian laying hens at 29, 39-week-old with similar body weight and ...This trial was conducted to evaluate the effect of quercetin on egg quality and components in laying hens of different weeks. A total of 240 healthy Hessian laying hens at 29, 39-week-old with similar body weight and laying rate were randomly divided into four groups with six replicates of 10 each replicate, respectively. The treatments were fed with basal diet supplemented with 0, 0.2, 0.4 and 0.6 g-kg-1 quercetin for 8 weeks. The results showed that compared with the control, broken or soft shell rate significantly decreased at 0.2 and 0.4 g.kg-1 quercetin and eggshell thickness significantly increased at 0.4 g.kg-1 quercetin （P〈0.01） in laying hens at 39-47 weeks old; yolk protein significantly decreased at 0.6 g kg-1 quercetin （P〈0.05） in laying hens at 29-37 weeks old; while yolk protein significantly increased at three quercetin treatments in laying hens at 39-47 weeks old; yolk cholesterol significantly decreased by quercetin in laying hens at 29-37 weeks old （P〈0.05）; yolk total phospholipids significantly increased at 0.4 and 0.6 g kg-1 quercetin （P〈0.01） and yolk cholesterol significantly decreased at 0.6 g kg-1 quercetin （P〈0.05） in laying hens at 39-47 weeks old. In a word, quercetin affected egg quality and components to some extents in laying hens of different weeks, the older the hens became, the better improvement they would be. The optimum level of quercetin was 0.4 g kg-1 in the basal diet.展开更多
Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order princip...Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit （HOPCP）, since it is critical in multi-way data analysis. Unlike the convexification （nuclear norm） for matrix rank function, the tensorial nuclear norm is stil an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme： the augmented Lagrangian alternating direction method （ALADM） and its truncated higher-order singular value decomposition （ALADM-THOSVD） version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.展开更多
Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dim...Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.展开更多
The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results...The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results show that 38 volatile chemical components of RPR are determined, accounting for 95.21% of total contents of volatile chemical components of RPR. The main volatile chemical components of RPR are (Z, Z)-9,12-octadecadienoic acid, n-hexadecanoic acid, 2-hydroxy- benzaldehyde, 1-(2-hydroxy-4-methoxyphenyl)-ethanone, 6,6-dimethyl-bicyclo[3.1.1] heptane-2-methanol, 4,7-dimethyl-benzofuran, 4-(1-methylethenyl)-1-cyclohexene-1-carboxaldehyde, and cyclohexadecane.展开更多
In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effect...In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.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funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
基金the support of the National Natural Science Foundation of China(Grant No.12302440)China Postdoctoral Science Foundation(Grant No.2023M741713)。
文摘As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.
基金Supported by the National Natural Science Foundation of China(42207371)the Technological Project of Jiangsu Vocational College of Agriculture and Forestry(2021kj17)+1 种基金Yafu Technology Innovation and Service Major Project of Jiangsu Vocational College of Agriculture and Forestry(2024kj01)Key Research Projects of Jiangsu Vocational College of Agriculture and Forestry(2023kj14)。
文摘Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPMAS-^(13)C-NMR)and ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS)to characterize the chemical components of humic acids.Tomato seed germination index(GI)was determined with the goal of screening the key active components of humic acids.Humic acids had a significantly higher nutrient content,except for the total nitrogen(TN)and the total phosphorus(TP)contents.Humic acids had a higher content of O-CH_(3)/NCH,aromatic C-O and carbonyl C compared to weathered coal,with significantly lower anomeric C,aromatic C and O-alkyl C/alkyl C.There were 611 different compounds identified among the test materials using UHPLC-MS.Humic acids also had a significantly higher GI(158.0%and 153.1%)than weathered coal(85.5%).The organic matter(OM),TP and available potassium(AK)contents in humic acids were significantly positively correlated with GI,and available phosphorus(AP)was significantly negatively correlated.Among the carbon components,O-CH3/NCH,aromatic C-O and O-alkyl C/alkyl C were significantly positively correlated with GI,while anomeric C was significantly negatively correlated.Furthermore,among the top 10 positive and five negative correlation compounds,lipids and lipid-like molecules[armexifolin,boviquinone 4,3-methyladipic acid,lxocarpalactone A,monic acid,DG(20:1(11Z)/18:4(6Z,9Z,12Z,15Z)/0:0),and brassinolide]and organic acids and derivatives(N-acetylglutamic acid,8-hydroxy-5,6-octadienoic acid,acetyl-L-tyrosine,and hydroxyprolyl-methionine)in humic acids might be crucial active components for improving tomato seed germination.The results provided direct evidence for the identification of bioactive molecules of humic acids,and a scientific basis for the precise utilization of bioactive molecular components of humic acids in sustainable agricultural development.
基金Education Department of Heilongjiang Province in China for the Oversea Researcher Projects(1151HZ01,10531002)
文摘Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conversed into the independent indexes of principal component based on the principal component analysis method. The results showed that the rice kernel types influenced the meanings of principal components indexes. For long and short rice, the first principal component was comprehensive index. But the second principal component was springiness for the short rice, while it was adhesiveness for long rice. Therefore, the first principal component can be used to express the quality of cooked rice with a few of indexes, and the rice type can be recognized according to the second principal component.
基金Supported by the Science and Technology Research Project Fund of Provincial Department of Education(12531004)Project of Heilongjiang Leading Talent Echelon Talented(2012)
文摘Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.
基金supported by the National Natural Science Foundation of China(71401052)the Key Project of National Social Science Fund of China(12AZD108)+2 种基金the Doctoral Fund of Ministry of Education(20120094120024)the Philosophy and Social Science Fund of Jiangsu Province Universities(2013SJD630073)the Central University Basic Service Project Fee of Hohai University(2011B09914)
文摘To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.
基金Project(217/s/458)supported by Azarbaijan Shahid Madani University,Iran
文摘A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.
文摘A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.
基金TheFoundationforScholarsReturnedfromOverseaCNNC! (No .970 48 2 )
文摘The effect of key parameters on fatigue life of common metallic components, e.g. steel, aluminium alloy, has been analysed quantitatively. The influential coverage and degree of these parameters have been investigated systematically, some phenomena which can′t be discovered with qualitative analysis method have been revealed, a series of valuable conclusions has been obtained, which would be very beneficial to fatigue resistant design and improvement of anti fatigue ability of metallic components.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
基金Project(41902301)supported by the National Natural Science Foundation of ChinaProject(20201Y185)supported by the Science and Technology Foundation of Guizhou Province,China+2 种基金Project(Z018023)supported by the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,IRSM,CASProject(201822)supported by the Foundation for Young Talents of Guizhou University,ChinaProject(2017-5402)supported by the Mountain Geohazard Prevention R&D Center of Guizhou Province,China。
文摘The cohesion weakening and friction strengthening(CWFS)model for rock reveals the strength components mobilization process during progressive brittle failure process of rock,which is very helpful in understanding mechanical properties of rock.However,the used incremental cyclic loading−unloading compression test for the determination of strength components is very complicated,which limits the application of CWFS model.In this paper,incremental cyclic loading−unloading compression test was firstly carried out to study the evolution of deformation and the strength properties of Beishan granite after various temperatures treated under different confining pressures.We found the axial and lateral unloading modulus are closely related to the applied stress and damage state of rock.Based on these findings,we can accurately determine the plastic strain during the entire failure process using conventional tri-axial compression test data.Furthermore,a strength component(cohesive and frictional strength)determination method was developed using conventional triaxial compression test.Using this method,we analyzed the variation of strength mobilization and deformation properties of Beishan granite after various temperatures treated.At last,a non-simultaneous strength mobilization model for thermally treated granite was obtained and verified by numerical simulation,which demonstrated the effectiveness of the proposed strength determination method.
基金supported by the National Natural Sciences Foundation of China(7137102271401193+2 种基金71671193)the Program for Innovation Research in Central University of Finance and Economicsthe Innovation Foundation of BUAA for Ph.D.Graduates
文摘Determining the number of components is a crucial issue in a mixture model. A moment-based criterion is considered to estimate the number of components arising from a normal mixture model. This criterion is derived from an omnibus statistic involving the skewness and kurtosis of each component. The proposed criterion additionally provides a measurement for the model fit in an absolute sense. The performances of our criterion are satisfactory compared with other classical criteria through Monte-Carlo experiments.
基金Supported by Heilongjiang Department of Education(12541010)Heilongjiang Department of Human Resources and Social Security(2014-2015)+1 种基金Harbin Science and Technology Bureau(2015RQXXJ014)Academic Team Construction of Northeast Agricultural University(2014-2017)
文摘This trial was conducted to evaluate the effect of quercetin on egg quality and components in laying hens of different weeks. A total of 240 healthy Hessian laying hens at 29, 39-week-old with similar body weight and laying rate were randomly divided into four groups with six replicates of 10 each replicate, respectively. The treatments were fed with basal diet supplemented with 0, 0.2, 0.4 and 0.6 g-kg-1 quercetin for 8 weeks. The results showed that compared with the control, broken or soft shell rate significantly decreased at 0.2 and 0.4 g.kg-1 quercetin and eggshell thickness significantly increased at 0.4 g.kg-1 quercetin （P〈0.01） in laying hens at 39-47 weeks old; yolk protein significantly decreased at 0.6 g kg-1 quercetin （P〈0.05） in laying hens at 29-37 weeks old; while yolk protein significantly increased at three quercetin treatments in laying hens at 39-47 weeks old; yolk cholesterol significantly decreased by quercetin in laying hens at 29-37 weeks old （P〈0.05）; yolk total phospholipids significantly increased at 0.4 and 0.6 g kg-1 quercetin （P〈0.01） and yolk cholesterol significantly decreased at 0.6 g kg-1 quercetin （P〈0.05） in laying hens at 39-47 weeks old. In a word, quercetin affected egg quality and components to some extents in laying hens of different weeks, the older the hens became, the better improvement they would be. The optimum level of quercetin was 0.4 g kg-1 in the basal diet.
基金supported by the National Natural Science Foundationof China(51275348)
文摘Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit （PCP）. This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit （HOPCP）, since it is critical in multi-way data analysis. Unlike the convexification （nuclear norm） for matrix rank function, the tensorial nuclear norm is stil an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme： the augmented Lagrangian alternating direction method （ALADM） and its truncated higher-order singular value decomposition （ALADM-THOSVD） version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.
基金the National Natural Science of China (50675167)a Foundation for the Author of National Excellent Doctoral Dissertation of China(200535)
文摘Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.
基金Project(20235020) supported by the National Natural Science Foundation of China
文摘The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results show that 38 volatile chemical components of RPR are determined, accounting for 95.21% of total contents of volatile chemical components of RPR. The main volatile chemical components of RPR are (Z, Z)-9,12-octadecadienoic acid, n-hexadecanoic acid, 2-hydroxy- benzaldehyde, 1-(2-hydroxy-4-methoxyphenyl)-ethanone, 6,6-dimethyl-bicyclo[3.1.1] heptane-2-methanol, 4,7-dimethyl-benzofuran, 4-(1-methylethenyl)-1-cyclohexene-1-carboxaldehyde, and cyclohexadecane.
基金Project(2003AA430200) supported by the National High-Tech Research and Development Program of China
文摘In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.
