This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly...This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.展开更多
Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles...Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades.In this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input data.The study was conducted in three steps.First,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance.Next,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB.We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles.Finally,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal components.The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.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
Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of tre...Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.展开更多
Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and bloo...Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P <0 05), whereas the PC3 orig was positively correlated with the chronological age ( P <0 01). The cardiovascular age thus calculated from the regression equation was significantly correlated with the chronological age among the 89 aviation personnel ( r =0.73, P <0 01). Conclusion: The cardiovascular age calculated based on a multi variate analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel.展开更多
Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phe...Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association analysis.Principle component analysis(PCA),as a popular dimension reduction method,has been broadly used in the analysis of multiple phenotypes.Since PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across phenotypes.Existing PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all PCs.In this paper,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination method.Our proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special situation.Extensive simulation shows that our method is robust and can generate powerful result in various situations.The real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.展开更多
Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency ...Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.展开更多
Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy fo...Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.展开更多
Principal component analysis(PCA)is a widely used method for multivariate data analysis that projects the original high-dimensional data onto a low-dimensional subspace with maximum variance.However,in practice,we wou...Principal component analysis(PCA)is a widely used method for multivariate data analysis that projects the original high-dimensional data onto a low-dimensional subspace with maximum variance.However,in practice,we would be more likely to obtain a few compressed sensing(CS)measurements than the complete high-dimensional data due to the high cost of data acquisition and storage.In this paper,we propose a novel Bayesian algorithm for learning the solutions of PCA for the original data just from these CS measurements.To this end,we utilize a generative latent variable model incorporated with a structure prior to model both sparsity of the original data and effective dimensionality of the latent space.The proposed algorithm enjoys two important advantages:1)The effective dimensionality of the latent space can be determined automatically with no need to be pre-specified;2)The sparsity modeling makes us unnecessary to employ multiple measurement matrices to maintain the original data space but a single one,thus being storage efficient.Experimental results on synthetic and real-world datasets show that the proposed algorithm can accurately learn the solutions of PCA for the original data,which can in turn be applied in reconstruction task with favorable results.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
Objective: To analyze the characteristics and possible mechanism of lipid metabolism in pregnant rats with intestinal flora imbalance. Methods: A total of 129 sexually mature female SD rats were divided into three gro...Objective: To analyze the characteristics and possible mechanism of lipid metabolism in pregnant rats with intestinal flora imbalance. Methods: A total of 129 sexually mature female SD rats were divided into three groups: non-pregnant group (untreated healthy rats), healthy pregnant group (natural insemination pregnant rats), and pregnant microflora disorder group (pregnant rats were given mixed antibiotics by gavage to build the modeling), with 43 rats in each group. The contents of TG, LDL, HDL and TC were detected by automatic biochemical analyzer, and the contents of SCD1, PGC-1 alpha, PEPCK, ApoE and MTTP genes were detected by fluorescence quantitative PCR technology. Regression analysis was used to explore the comprehensive influence of each gene on total cholesterol expression in rats. Principal component analysis was used to explore the internal mechanism of lipid metabolism in pregnant rats with intestinal flora disorder. Results: The contents of TG, TC, LDL and HDL were compared among the three groups of rats and the differences were statistically significant (P<0.05) . The expression levels of related genes (SCD1, PGC-1, PEPCK, ApoE, MTTP) in the three groups were statistically significant (P<0.05) . SCD1 content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.92±0.12) μg/mL, (1.20±0.15)μg/mL, and (1.53±0.20) μg/mL, respectively. PGC-1 alpha content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (1.34±0.21) μg/mL, (0.93±0.12) micron /mL, and (0.41±0.08) μg/mL, respectively. PEPCK content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.48±0.06) μg/mL, (0.35±0.09)μg/mL, and (0.22±0.05) μg/mL, and the differences were statistically significant (P<0.05) . Multivariate linear regression analysis showed that the influence of gene content on The effect of each gene content on TC content was in order from large to small: SCD1 (OR=4.572) , PGC-1 (OR=3.387) , PEPCK (OR=3.935) , ApoE (OR=3.597) , MTTP (OR=3.096) . The principal component analysis showed that three principal components could be extracted from five related genes of lipid metabolism in pregnant rats with intestinal dysbiosis: SCD1/PEPCK pathway (contribution rate: 36.28%) , PGC-1 /ApoE pathway (contribution rate: 30.42%) , and MTTP pathway (contribution rate: 15.37%) . Conclusion: After pregnancy, blood lipids in rats are significantly increased while the imbalance of intestinal flora will lead to decreased blood lipids. The disorder of lipid metabolism in pregnant rats with intestinal flora imbalance is mainly related to the disorder of gene expression, which further affects the functions of SCD1/PEPCK, PGC-1 /ApoE and MTTP pathways.展开更多
为进一步提高风电发电机组覆冰故障诊断模型的精度,缩短隐患消除的周期,提出基于多变量相关性平稳自适应增强(Gentle Adaboost)的风机叶片覆冰故障诊断方法。首先,对监控与数据采集(supervisory control and data acquisition,SCADA)系...为进一步提高风电发电机组覆冰故障诊断模型的精度,缩短隐患消除的周期,提出基于多变量相关性平稳自适应增强(Gentle Adaboost)的风机叶片覆冰故障诊断方法。首先,对监控与数据采集(supervisory control and data acquisition,SCADA)系统的多变量数据进行归一化处理,统一变量量纲;然后,通过一维卷积神经网络提取多变量间的相关性信息,并通过主成分分析法减少特征向量的维度,提取向量中的关键成分;最后,将特征向量导入具有强鲁棒性的集成学习算法Gentle Adaboost中进行学习,以获得具有高泛化能力的覆冰故障诊断模型。针对实际数据与6种经典算法进行了对比实验,结果表明,所提方法的覆冰故障诊断精度更高。展开更多
基金supported by National Natural Science Foundation of China under Grant No.60971083National International Science and Technology Cooperation Project of China (No.2010DFA11320)
文摘This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.
文摘Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure consistency.However,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades.In this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input data.The study was conducted in three steps.First,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance.Next,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB.We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles.Finally,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal components.The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.
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Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.
文摘Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P <0 05), whereas the PC3 orig was positively correlated with the chronological age ( P <0 01). The cardiovascular age thus calculated from the regression equation was significantly correlated with the chronological age among the 89 aviation personnel ( r =0.73, P <0 01). Conclusion: The cardiovascular age calculated based on a multi variate analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel.
基金supported by the Key Program of Joint Funds of the National Natural Science Foundation of China(Grant No.U19B2040)Fundamental Research Funds for Central Universities and the University of Chinese Academy of Sciences(Grant No.Y95401TXX2)Beijing Natural Science Foundation(Grant No.Z190004)。
文摘Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association analysis.Principle component analysis(PCA),as a popular dimension reduction method,has been broadly used in the analysis of multiple phenotypes.Since PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across phenotypes.Existing PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all PCs.In this paper,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination method.Our proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special situation.Extensive simulation shows that our method is robust and can generate powerful result in various situations.The real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.
基金supported by the Key Technologies R&D Program of China during the 11th Five-Year Plan period (2008BAD98B03)
文摘Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.
文摘Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.
基金Supported by National Basic Research Program of China（973 Program）（2012CB720505） the Fundamental Research Funds for the Central Universities（2012QNA5012）+1 种基金 Project of Education Department of Zhejiang Province（Y201223159） Technology Foundation for Selected Overseas Chinese Scholar of Zhejiang Province
基金This work was supported by the Key Program of the National Natural Science Foundation of China(NSFC)(Grant No.61732006).
文摘Principal component analysis(PCA)is a widely used method for multivariate data analysis that projects the original high-dimensional data onto a low-dimensional subspace with maximum variance.However,in practice,we would be more likely to obtain a few compressed sensing(CS)measurements than the complete high-dimensional data due to the high cost of data acquisition and storage.In this paper,we propose a novel Bayesian algorithm for learning the solutions of PCA for the original data just from these CS measurements.To this end,we utilize a generative latent variable model incorporated with a structure prior to model both sparsity of the original data and effective dimensionality of the latent space.The proposed algorithm enjoys two important advantages:1)The effective dimensionality of the latent space can be determined automatically with no need to be pre-specified;2)The sparsity modeling makes us unnecessary to employ multiple measurement matrices to maintain the original data space but a single one,thus being storage efficient.Experimental results on synthetic and real-world datasets show that the proposed algorithm can accurately learn the solutions of PCA for the original data,which can in turn be applied in reconstruction task with favorable results.
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.
基金supported by the Research-Based Learning and Innovation Experimental Project for College Students in Hunan Province in 2018(Grant No.255-1097)Research-Based Learning and Innovation Experimental Project for Students in Changsha Medical University(Grant No.77-264).
文摘Objective: To analyze the characteristics and possible mechanism of lipid metabolism in pregnant rats with intestinal flora imbalance. Methods: A total of 129 sexually mature female SD rats were divided into three groups: non-pregnant group (untreated healthy rats), healthy pregnant group (natural insemination pregnant rats), and pregnant microflora disorder group (pregnant rats were given mixed antibiotics by gavage to build the modeling), with 43 rats in each group. The contents of TG, LDL, HDL and TC were detected by automatic biochemical analyzer, and the contents of SCD1, PGC-1 alpha, PEPCK, ApoE and MTTP genes were detected by fluorescence quantitative PCR technology. Regression analysis was used to explore the comprehensive influence of each gene on total cholesterol expression in rats. Principal component analysis was used to explore the internal mechanism of lipid metabolism in pregnant rats with intestinal flora disorder. Results: The contents of TG, TC, LDL and HDL were compared among the three groups of rats and the differences were statistically significant (P<0.05) . The expression levels of related genes (SCD1, PGC-1, PEPCK, ApoE, MTTP) in the three groups were statistically significant (P<0.05) . SCD1 content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.92±0.12) μg/mL, (1.20±0.15)μg/mL, and (1.53±0.20) μg/mL, respectively. PGC-1 alpha content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (1.34±0.21) μg/mL, (0.93±0.12) micron /mL, and (0.41±0.08) μg/mL, respectively. PEPCK content in the non-pregnant group, healthy pregnancy group, and disordered pregnancy group was (0.48±0.06) μg/mL, (0.35±0.09)μg/mL, and (0.22±0.05) μg/mL, and the differences were statistically significant (P<0.05) . Multivariate linear regression analysis showed that the influence of gene content on The effect of each gene content on TC content was in order from large to small: SCD1 (OR=4.572) , PGC-1 (OR=3.387) , PEPCK (OR=3.935) , ApoE (OR=3.597) , MTTP (OR=3.096) . The principal component analysis showed that three principal components could be extracted from five related genes of lipid metabolism in pregnant rats with intestinal dysbiosis: SCD1/PEPCK pathway (contribution rate: 36.28%) , PGC-1 /ApoE pathway (contribution rate: 30.42%) , and MTTP pathway (contribution rate: 15.37%) . Conclusion: After pregnancy, blood lipids in rats are significantly increased while the imbalance of intestinal flora will lead to decreased blood lipids. The disorder of lipid metabolism in pregnant rats with intestinal flora imbalance is mainly related to the disorder of gene expression, which further affects the functions of SCD1/PEPCK, PGC-1 /ApoE and MTTP pathways.
文摘为进一步提高风电发电机组覆冰故障诊断模型的精度,缩短隐患消除的周期,提出基于多变量相关性平稳自适应增强(Gentle Adaboost)的风机叶片覆冰故障诊断方法。首先,对监控与数据采集(supervisory control and data acquisition,SCADA)系统的多变量数据进行归一化处理,统一变量量纲;然后,通过一维卷积神经网络提取多变量间的相关性信息,并通过主成分分析法减少特征向量的维度,提取向量中的关键成分;最后,将特征向量导入具有强鲁棒性的集成学习算法Gentle Adaboost中进行学习,以获得具有高泛化能力的覆冰故障诊断模型。针对实际数据与6种经典算法进行了对比实验,结果表明,所提方法的覆冰故障诊断精度更高。
