A recent method for assessing the local influence is introduced by Cook(1986), in which the normal curvature of the influence graph based on the likelihood displacement is used to monitor the influence of small pertur...A recent method for assessing the local influence is introduced by Cook(1986), in which the normal curvature of the influence graph based on the likelihood displacement is used to monitor the influence of small perturbation. Since then this method has been applied to various kind of models. However, the local influence in multivariate analysis is still an unexplored area because the influence for many statistics in multivariate analysis is not convenient to handle based on the Cook's likelihood displacement. In this paper, we suggest a method with a slight modification in Cook's approach to assess the local influence of small perturbation on a certain statistic. The local influence of the perturbation on eigenvalue and eigenvector of variance-covariance matrix in theoretical and sample version is assessed, some results for the other statistics in multivariate analysis such as generalized variance, canonical correlations are studied. Finally, two examples are analysed for illustration.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
Sanhuang chicken is a popular native breed in China and well-known for delicious flavour.Spices could enhance the chicken meat flavour and work well in preservation.Chinese 5-spice blend(CS)and garam masala(GM)are rou...Sanhuang chicken is a popular native breed in China and well-known for delicious flavour.Spices could enhance the chicken meat flavour and work well in preservation.Chinese 5-spice blend(CS)and garam masala(GM)are routinely using spices in China and Pakistan,respectively.The flavour profiles of Sanhuang chicken breast(CB)and its blends with CS and GM were obtained by electronic nose(E-nose),solid-phase microextraction gas chromatography-mass spectrometry(SPME GC-MS)and GC-ion mobility spectrometry(GC-IMS).Principal component analysis(PCA)efficiently discriminated the aroma profiles of three chicken formulations.The GC-chromatographs revealed the significant aroma alterations of chicken breast meat after marination with spices.Aldehydes were the major contributors of chicken aroma,while most of the aromatic hydrocarbons were generated by spices.Almost all chicken key-compounds produced by oxidation reaction were either reduced or eliminated by marination,showing the antioxidation capacity of spices leading to meat preservation.GC-IMS is not only a rapid and comprehensive detection method,but also proved to be more sensitive than GC-MS.The substantial role of both traditional spices in enhancing flavour quality of chicken meat,and their exposure as functional ingredients in Chinese and Pakistan cuisines could lead to the cross-cultural meat trade opportunities.展开更多
A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direct...A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.展开更多
The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigatio...The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time series.We propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties.The obtained results suggest that these two network measures show distinct changes between different subjects.This is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system.Consequently,the complexity of the cardiac system is reduced.After that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed approach.Accuracy of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two datasets.Therefore,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.展开更多
A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out....A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.展开更多
This paper describes a novel approach to explore a multidimensional design space and guide multi-actor decision making in the design of sustainable buildings.The aim is to provide proactive and holistic guidance of th...This paper describes a novel approach to explore a multidimensional design space and guide multi-actor decision making in the design of sustainable buildings.The aim is to provide proactive and holistic guidance of the design team.We propose to perform exhaustive Monte Carlo simulations in an iterative design approach that consists of tw o steps:1) preparation by the modeler,and 2) a multi-collaborator meeting.In the preparation phase,the simulation modeler performs Morris sensitivity analysis to fixate insignificant model inputs and to identify non-linearity and interaction effects.Next,a representation of the global design space is obtained from thousands of simulations using low-discrepancysequences(LPτ) for sampling.From these simulations,the modeler constructs fast metamodels and performs quantitative sensitivity analysis.During the meeting,the design team explores the global design space by filtering the thousands of simulations.Variable filter criteria are easily applied using an interactive parallel coordinate plot w hich provide immediate feedback on requirements and design choices.Sensitivity measures and metamodels show the combined effects of changing a single input and how to remedy unw anted output changes.The proposed methodology has been developed and tested through real building cases using a normative model to assess energy demand,thermal comfort,and daylight.展开更多
文摘A recent method for assessing the local influence is introduced by Cook(1986), in which the normal curvature of the influence graph based on the likelihood displacement is used to monitor the influence of small perturbation. Since then this method has been applied to various kind of models. However, the local influence in multivariate analysis is still an unexplored area because the influence for many statistics in multivariate analysis is not convenient to handle based on the Cook's likelihood displacement. In this paper, we suggest a method with a slight modification in Cook's approach to assess the local influence of small perturbation on a certain statistic. The local influence of the perturbation on eigenvalue and eigenvector of variance-covariance matrix in theoretical and sample version is assessed, some results for the other statistics in multivariate analysis such as generalized variance, canonical correlations are studied. Finally, two examples are analysed for illustration.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
基金funded by National Natural Science Foundation of China (Grant No. 32001824, 31972198, 31901816, 31901813, 32001827)
文摘Sanhuang chicken is a popular native breed in China and well-known for delicious flavour.Spices could enhance the chicken meat flavour and work well in preservation.Chinese 5-spice blend(CS)and garam masala(GM)are routinely using spices in China and Pakistan,respectively.The flavour profiles of Sanhuang chicken breast(CB)and its blends with CS and GM were obtained by electronic nose(E-nose),solid-phase microextraction gas chromatography-mass spectrometry(SPME GC-MS)and GC-ion mobility spectrometry(GC-IMS).Principal component analysis(PCA)efficiently discriminated the aroma profiles of three chicken formulations.The GC-chromatographs revealed the significant aroma alterations of chicken breast meat after marination with spices.Aldehydes were the major contributors of chicken aroma,while most of the aromatic hydrocarbons were generated by spices.Almost all chicken key-compounds produced by oxidation reaction were either reduced or eliminated by marination,showing the antioxidation capacity of spices leading to meat preservation.GC-IMS is not only a rapid and comprehensive detection method,but also proved to be more sensitive than GC-MS.The substantial role of both traditional spices in enhancing flavour quality of chicken meat,and their exposure as functional ingredients in Chinese and Pakistan cuisines could lead to the cross-cultural meat trade opportunities.
基金Sponsored by the Scientific Research Foundation for Returned Overseas Chinese Scholars of the Ministry of Education of China
文摘A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.
基金Project supported by the Xuzhou Key Research and Development Program(Social Development)(Grant No.KC21304)the National Natural Science Foundation of China(Grant No.61876186)。
文摘The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time series.We propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties.The obtained results suggest that these two network measures show distinct changes between different subjects.This is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system.Consequently,the complexity of the cardiac system is reduced.After that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed approach.Accuracy of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two datasets.Therefore,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.
基金Huo Yingdong Education Foundation Young Teachers Fund for Higher Education Institutions(171043)Sichuan Outstanding Young Science and Technology Talent Project(2019JDJQ0036)。
文摘A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.
文摘This paper describes a novel approach to explore a multidimensional design space and guide multi-actor decision making in the design of sustainable buildings.The aim is to provide proactive and holistic guidance of the design team.We propose to perform exhaustive Monte Carlo simulations in an iterative design approach that consists of tw o steps:1) preparation by the modeler,and 2) a multi-collaborator meeting.In the preparation phase,the simulation modeler performs Morris sensitivity analysis to fixate insignificant model inputs and to identify non-linearity and interaction effects.Next,a representation of the global design space is obtained from thousands of simulations using low-discrepancysequences(LPτ) for sampling.From these simulations,the modeler constructs fast metamodels and performs quantitative sensitivity analysis.During the meeting,the design team explores the global design space by filtering the thousands of simulations.Variable filter criteria are easily applied using an interactive parallel coordinate plot w hich provide immediate feedback on requirements and design choices.Sensitivity measures and metamodels show the combined effects of changing a single input and how to remedy unw anted output changes.The proposed methodology has been developed and tested through real building cases using a normative model to assess energy demand,thermal comfort,and daylight.
