In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance c...In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Design of internal combustion engine (ICE) components is one of the earliest and also the most active areas in which computer aided modeling techniques are applied. Computer aided modeling techniques could provide req...Design of internal combustion engine (ICE) components is one of the earliest and also the most active areas in which computer aided modeling techniques are applied. Computer aided modeling techniques could provide requisite information for follow up designing segments such as structural analysis, design of technological process and manufacturing etc, and thereby lead to the reduction of product design period and the quality and reliability improvement of ICE components. So the developing situations of ICE components' 2 D drafting, 3 D modeling of ICE, overall CAD of ICE as well as component design expert system etc. are surveyed, which are the typical applications of computer aided modeling techniques in ICE component design process, and some existent problems and tasks are pointed out so as to make some references for the further research work.展开更多
Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, whi...Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.展开更多
Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that t...Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.展开更多
For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component b...For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.展开更多
With the rapid progress of component technology,the software development methodology of gathering a large number of components for designing complex software systems has matured.But,how to assess the application relia...With the rapid progress of component technology,the software development methodology of gathering a large number of components for designing complex software systems has matured.But,how to assess the application reliability accurately with the information of system architecture and the components reliabilities together has become a knotty problem.In this paper,the defects in formal description of software architecture and the limitations in existed model assumptions are both analyzed.Moreover,a new software reliability model called Component Interaction Mode(CIM) is proposed.With this model,the problem for existed component-based software reliability analysis models that cannot deal with the cases of component interaction with non-failure independent and non-random control transition is resolved.At last,the practice examples are presented to illustrate the effectiveness of this model.展开更多
The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-pr...The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.展开更多
To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic "leakage rate–leakage factors"(LRLF) model. In this model, we consider the pipe attributes(quality, dia...To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic "leakage rate–leakage factors"(LRLF) model. In this model, we consider the pipe attributes(quality, diameter,age), maintenance cost, valve replacement cost, and annual average pressure. Based on variable selection and principal component analysis results, we extracted three main principle components—the pipe attribute principal component(PAPC), operation management principal component, and water pressure principal component. Of these, we found PAPC to have the most influence. Using principal component regression, we established an LRLF model with no detectable serial correlations. The adjusted R2 and RMSE values of the model were 0.717 and 2.067, respectively.This model represents a potentially useful tool for controlling leakage rate from the macroscopic viewpoint.展开更多
A sub-regular solution model SELFSReM4 used to evaluate activities of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Lab of Ferrometallurgy. This paper introduces...A sub-regular solution model SELFSReM4 used to evaluate activities of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Lab of Ferrometallurgy. This paper introduces the application of SELFSReM4 in evaluating activities of the components in C-Mn-Fe-Si system without SiC precipitation.展开更多
A sub-regular solution model SELF-SReM4 used to evaluate activity of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Laboratory of Ferrometallurgy.The application ...A sub-regular solution model SELF-SReM4 used to evaluate activity of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Laboratory of Ferrometallurgy.The application of SELF-SReM4 in C-Mn-Fe-Si system without the SiC formation has been introduced in previous paper.It’s application for molten slag of MnO-SiO2-Al2O3-CaO was introduced in this paper.They provide a basis for the prediction of the metal-slag equilibrium conditions.展开更多
The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring f...The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.展开更多
Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treati...Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.展开更多
A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,...Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.展开更多
Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and ...Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.展开更多
Adsorption is one of the several techniques that has been successfully used for dyes removal.Since most industrial colored effluents contain several components including dyes,having a strong knowledge about the scope ...Adsorption is one of the several techniques that has been successfully used for dyes removal.Since most industrial colored effluents contain several components including dyes,having a strong knowledge about the scope of competitive adsorption process is a powerful key to design an appropriate system.This is mainly because of the complexity brought about by the increasing number of parameters needed for process description which complicates not only the process modeling but also the experimental data collection.A multicomponent adsorption model should be based on fundamental soundness,speed,and simplicity of calculation.For such systems,competition will change the adsorbent-adsorbate attractions.Thus,there is major concern to develop an accurate and reliable method to predict dye adsorption behavior in multi-component systems.This article covers topics such as the theory of dyes adsorption in multi-component systems along with applicable models according to the consistent theories presented by researchers.展开更多
The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necess...The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.展开更多
A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault inje...A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.展开更多
基金supported by the National Natural Science Foundation of China(No.42174011)。
文摘In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
文摘Design of internal combustion engine (ICE) components is one of the earliest and also the most active areas in which computer aided modeling techniques are applied. Computer aided modeling techniques could provide requisite information for follow up designing segments such as structural analysis, design of technological process and manufacturing etc, and thereby lead to the reduction of product design period and the quality and reliability improvement of ICE components. So the developing situations of ICE components' 2 D drafting, 3 D modeling of ICE, overall CAD of ICE as well as component design expert system etc. are surveyed, which are the typical applications of computer aided modeling techniques in ICE component design process, and some existent problems and tasks are pointed out so as to make some references for the further research work.
基金the National Natural Science Foundation of China(No.61572033)the Natural Science Foundation of Education Department of Anhui Province of China(No.KJ2015ZD08)the Higher Education Promotion Plan of Anhui Province of China(No.TSKJ2015B14)
文摘Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.
基金The project is partly supported by NSFC (19971085)the Doctoral Program Foundation of the Institute of High Education and the Special Foundation of Chinese Academy of Sciences.
文摘Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.
基金National Natural Science Foundation of China(61673279)。
文摘For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.
基金Supported by the National Natural Science Foundation of China (No. 60873195,60873003,and 61070220)the Doctoral Foundation of Ministry of Education (No.20090111110002)
文摘With the rapid progress of component technology,the software development methodology of gathering a large number of components for designing complex software systems has matured.But,how to assess the application reliability accurately with the information of system architecture and the components reliabilities together has become a knotty problem.In this paper,the defects in formal description of software architecture and the limitations in existed model assumptions are both analyzed.Moreover,a new software reliability model called Component Interaction Mode(CIM) is proposed.With this model,the problem for existed component-based software reliability analysis models that cannot deal with the cases of component interaction with non-failure independent and non-random control transition is resolved.At last,the practice examples are presented to illustrate the effectiveness of this model.
基金supported by the National Natural Science Foundation of China(No.41874001 and No.41664001)Support Program for Outstanding Youth Talents in Jiangxi Province(No.20162BCB23050)National Key Research and Development Program(No.2016YFB0501405)。
文摘The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.
基金supported by the Ministry of Science and Technology of China (No.2014ZX07203-009)the Fundamental Research Funds for the Central Universitiesthe Program for New Century Excellent Talents at the University of China
文摘To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic "leakage rate–leakage factors"(LRLF) model. In this model, we consider the pipe attributes(quality, diameter,age), maintenance cost, valve replacement cost, and annual average pressure. Based on variable selection and principal component analysis results, we extracted three main principle components—the pipe attribute principal component(PAPC), operation management principal component, and water pressure principal component. Of these, we found PAPC to have the most influence. Using principal component regression, we established an LRLF model with no detectable serial correlations. The adjusted R2 and RMSE values of the model were 0.717 and 2.067, respectively.This model represents a potentially useful tool for controlling leakage rate from the macroscopic viewpoint.
文摘A sub-regular solution model SELFSReM4 used to evaluate activities of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Lab of Ferrometallurgy. This paper introduces the application of SELFSReM4 in evaluating activities of the components in C-Mn-Fe-Si system without SiC precipitation.
文摘A sub-regular solution model SELF-SReM4 used to evaluate activity of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Laboratory of Ferrometallurgy.The application of SELF-SReM4 in C-Mn-Fe-Si system without the SiC formation has been introduced in previous paper.It’s application for molten slag of MnO-SiO2-Al2O3-CaO was introduced in this paper.They provide a basis for the prediction of the metal-slag equilibrium conditions.
基金supported by the National Natural Science Foundation of China (61903326, 61933015)。
文摘The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.
基金supported by grants The Natural Science Foundation of Inner Mongolia(2019MS08104)The Natural Science Foundation of Inner Mongolia(2022ZD09)The Central Government Guiding Special Funds for Development of Local Science and Technology(2020ZY0020).
文摘Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金supported by the National Natural Science Foundation of China (No.11402288)
文摘Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.
文摘Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.
文摘Adsorption is one of the several techniques that has been successfully used for dyes removal.Since most industrial colored effluents contain several components including dyes,having a strong knowledge about the scope of competitive adsorption process is a powerful key to design an appropriate system.This is mainly because of the complexity brought about by the increasing number of parameters needed for process description which complicates not only the process modeling but also the experimental data collection.A multicomponent adsorption model should be based on fundamental soundness,speed,and simplicity of calculation.For such systems,competition will change the adsorbent-adsorbate attractions.Thus,there is major concern to develop an accurate and reliable method to predict dye adsorption behavior in multi-component systems.This article covers topics such as the theory of dyes adsorption in multi-component systems along with applicable models according to the consistent theories presented by researchers.
基金supported by the National Natural Science Foundation of China under Grant No.60672184
文摘The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.
基金Project(513150601)supported by the National Pre-Research Project Foundation of China
文摘A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.
