The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencin...The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencing aging behaviors and lithium plating within lithium-ion batteries(LIBs).Available lithium-ion plating models often falter in detailed description when integrating the structural intricacies.To address this challenge,this study proposes an innovative hierarchical model that intricately incorporates the layered rolling structure in cells.Notably,our model demonstrates a remarkable capacity to predict the non-uniform distribution of current density and overpotential along the rolling direction of LIBs.Subsequently,we delve into an insightful exploration of the structural factors that influence lithium plating behavior,leveraging the foundation laid by our established model.Furthermore,we easily update the hierarchical model by considering aging factors.This aging model effectively anticipates capacity fatigue and lithium plating tendencies across individual layers of LIBs,all while maintaining computational efficiency.In light of our findings,this model yields novel perspectives on capacity fatigue dynamics and local lithium plating behaviors,offering a substantial advancement compared to existing models.This research paves the way for more efficient and tailored LIB design and operation,with broad implications for energy storage technologies.展开更多
In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarc...In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarchical Model Updating Strategy(HMUS)is developed for Finite Element(FE)model updating with regard to uncorrelated modes.The principle of HMUS is first elaborated by integrating hierarchical modeling concept,model updating technology with proper uncorrelated mode treatment,and parametric modeling.In the developed strategy,the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure.The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam.The proposed HMUS is then applied to the FE model updating of an aeroengine stator system(casings)to demonstrate its effectiveness.Our studies reveal that(A)parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics;(B)the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs(CMPs)between FE model and experiment;(C)the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model,complex geometry,high-nonlinearity and numerous parameters;(D)it is appropriate to update a complex structural FE model parameterized.The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data,which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.展开更多
Towards integration of supply chain, manufacturing/production and investment decision making, this paper presents a hierarchical model architecture which contains six sub-models covering the areas of manufacturing con...Towards integration of supply chain, manufacturing/production and investment decision making, this paper presents a hierarchical model architecture which contains six sub-models covering the areas of manufacturing control, production operation, design and revamp, production management, supply chain and investment decision making. Six types of flow, material, energy, information, humanware, partsware and capital are clasified. These flows connect enterprise components/subsystems to formulate system topology and logical structure. Enterprise components/subsystems are abstracted to generic elementary and composite classes. Finally, the model architecture is applied to a management system of an integrated suply chain, and suggestion are made on the usage of the model architecture and further development of the model as well as implementation issues.展开更多
Digital libraries are complex systems and this brings difficulties for their evaluation. This paper proposes a hierarchical model to solve this problem, and puts the entangled matters into a clear-layered structure. F...Digital libraries are complex systems and this brings difficulties for their evaluation. This paper proposes a hierarchical model to solve this problem, and puts the entangled matters into a clear-layered structure. Firstly, digital libraries(DLs thereafter)are classified into 5 groups in ascending gradations, i.e. mini DLs, small DLs, medium DLs,large DLs, and huge DLs by their scope of operation. Then, according to the characteristics of DLs at different operational scope and level of sophistication, they are further grouped into unitary DLs, union DLs and hybrid DLs accordingly. Based on this simulated structure,a hierarchical model for digital library evaluation is introduced, which evaluates DLs differentiatingly within a hierarchical scheme by using varying criteria based on their specific level of operational complexity such as at the micro-level, medium-level, and/or at the macro-level. Based on our careful examination and analysis of the current literature about DL evaluation system, an experiment is conducted by using the DL evaluation model along with its criteria for unitary DLs at micro-level. The main contents resulting from this evaluation experimentation and also those evaluation indicators and relevant issues of major concerns for DLs at medium-level and macro-level are also to be presented at some length.展开更多
Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for impr...Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used： one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.展开更多
Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis method is proposed bas...Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis method is proposed based on multisignal model and support vector machine （SVM）. Multisignal model is used to describe the failure propagation relationship in electronic device system, and the most probable failure printed circuit boards （PCBs） can be found by Bayes inference. The exact failure modes in the PCBs can be identified by SVM. The results show the proposed modeling and diagnosis method is effective and suitable for diagnosis for complex electronic devices.展开更多
The annual passenger volume of airport reflected its passenger transport scale and the role in aviation network.The airports in whole country were divided into three layers:first layer airports,second layer airports a...The annual passenger volume of airport reflected its passenger transport scale and the role in aviation network.The airports in whole country were divided into three layers:first layer airports,second layer airports and third layer airports.The airlines from the first layer airports consisted the first layer aviation network.The airlines from the second layer airports consisted the second layer aviation network.The airlines from the third layer airports consisted the third layer aviation network.The structure and function of different layer aviation network had significant differences.These differences were shown in the number of airlines,average number of airlines of each airport,annual passenger volume of airport and average passenger volume of each airline.National aviation network hierarchical model was constructed to describe the whole country aviation network.The matrix was built to describe the airline number,annual passenger volume,average number of airlines,average passenger volume of each airport and airline rate of aviation network.The index of national aviation network structure was constructed to show the ratio of index between different aviation network layer to describe the aviation network structure.The structure index was built to illustrate the macrostructural features of national aviation network.The statistics data in year 1988,1994,2001,2008 and 2015 of China aviation network were analyzed and basic data matrixes,basic index matrixes and structure index matrixes were calculated.The trend of ratio of corresponding index between the first layer and the second layer showed the change of basic structure of China aviation network.At meantime,the tendency of ratio of corresponding index between the third layer and the second layer also showed the change of basic structure.The trend of network general structure index illustrated that the large scaled new airports and airlines construction had significant influence on the national aviation network structure.展开更多
A major challenge in spatial transcriptomics(ST)is resolving cellular composition,especially in technologies lacking single-cell resolution.The mixture of transcriptional signals within spatial spots complicates decon...A major challenge in spatial transcriptomics(ST)is resolving cellular composition,especially in technologies lacking single-cell resolution.The mixture of transcriptional signals within spatial spots complicates deconvolution and downstream analyses.To uncover the spatial heterogeneity of tissues,we introduce SvdRFCTD,a reference-free spatial transcriptomics deconvolution method,which estimates the cell type proportions at each spot on the tissue.To fully capture the heterogeneity in the ST data,we combine SvdRFCTD with a Bayesian hierarchical negative binomial model with spatial effects incorporated in both the mean and dispersion of the gene expression,which is used to explicitly model the generative mechanism of cell type proportions.By integrating spatial information and leveraging marker gene information,SvdRFCTD accurately estimates cell type proportions and uncovers complex spatial patterns.We demonstrate the ability of SvdRFCTD to identify cell types on simulated datasets.By applying SvdRFCTD to mouse brain and human pancreatic ductal adenocarcinomas datasets,we observe significant cellular heterogeneity within the tissue sections and successfully identify regions with high proportions of aggregated cell types,along with the spatial relationships between different cell types.展开更多
Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,wi...Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,with ores usually containing secondary‘pay’metals such as Au,Mo and gangue elements such as Fe and As.Due to sensing technology limitations,secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance(MR)ore sorting.Inferring the relationships between Cu and other elemental abundances is particularly critical for mineral processing.The variations in metal grade relationships occur due to the transition into different geological domains.This raises two questions-how to define these geological domains and how the metal grade relationship is influenced by these geological domains.In this paper,linear relationship is assumed between Cu grade and other metal grades.We applies a Bayesian hierarchical(partial-pooling)model to quantify the linear relationships between Cu,Au,and Fe grades from geochemical bore core data.The hierarchical model was compared with two other models-‘complete-pooling’model and‘nopooling’model.Mining blocks were split based on spatial domain to construct hierarchical model.Geochemical bore core data records metal grades measured from laboratory assay with spatial coordinates of sample location.Two case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical model.Markov chain Monte Carlo(MCMC)was used to sample the posterior parameters.Our results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling model.In addition,the posterior inference in the hierarchical model is insensitive to the choice of prior.The data is wellrepresented in the posterior which indicates a robust model.The results show that the spatial domain can be successfully utilised for metal grade regression.Uncertainty in estimating the relationship between pay metals and both secondary and gangue elements is quantified and shown to be reduced with partial-pooling.Thus,the proposed Bayesian hierarchical model can offer a reliable and stable way to monitor the relationship between metal grades for ore sorting and other mineral processing options.展开更多
Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account fo...Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account for dependencies and variations within and between different levels of a hierarchy. By explicitly modeling these relationships, MLM provides a robust and accurate analysis of data. It has become increasingly popular in the field of education. MLM enables the investigation of various research issues, the evaluation of individual and group-level indicators, and the calculation of both fixed and random effects. Overall, MLM revolutionizes data analysis by uncovering patterns, understanding contextual effects, and making more precise statistical inferences in complex datasets. For fitting multilevel models in R, use lmer function provided by lme4 package. Through this examination, the use of a multilevel model is expected to increase and revolutionize data analysis and decision-making. The Constrained Intermediate Model (CIM) and Augmented Intermediate model (AIM) deviation are compared using the Likelihood-ratio (LR) test and the ANOVA function. This study analyzes student results from the University of Agriculture Faisalabad, collected via stratified random sampling. A linear mixed-effect model under multilevel modeling estimates the impact on CGPA, considering department, gender, intermediate marks, and entry test scores. These results indicate that Entry test is a significant predictor of CGPA, but the effect of department identifier CMC on CGPA is not statistically significant.展开更多
An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption.However,most research on eco-driving focuses on the stability and safety for vehicle operatin...An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption.However,most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy.To meet the requirements for safety and comfort,at the same time,enhance the economic performance of the vehicles,an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario.Specifically,the upper-level model predictive controller controls the velocity,inter-vehicle distance and acceleration to guarantee safety and comfort for driving.According to the velocity information obtained from the upper model predictive controller,the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation,so as to allocate energy rationally.Finally,the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09%economic improvement on the premise of assuring safety and comfort of driving.展开更多
Understanding plant community assembly is crucial for effective ecosystem conservation and restoration.The ecological filter framework describes community assembly as a process shaped by dispersal,environmental,and bi...Understanding plant community assembly is crucial for effective ecosystem conservation and restoration.The ecological filter framework describes community assembly as a process shaped by dispersal,environmental,and biotic filters.Additionally,functional traits and phylogenetic relationships are increasingly recognized as important factors influencing species coexistence and community structure.However,both the ecological filter framework and the roles of functional traits and phylogeny in community assembly remain underexplored in the Algerian steppes—particularly in the El Bayadh region,where ongoing vegetation degradation threatens ecosystem stability.This study applied Hierarchical Modeling of Species Communities(HMSC)as an integrative approach to assess how ecological filters influence plant community assembly in the El Bayadh steppe and to evaluate the roles of functional traits and phylogenetic relationships in this process.Environmental data—including soil properties,topography,precipitation,and land use types(grazing and exclosure)—were collected across 50 plots in April and October,2023,along with functional traits from 24 species.These traits include root length,leaf area,specific leaf area,clonality,life history,and seed mass.HMSC results revealed that soil properties and precipitation were the primary drivers of community structure,while sand height and elevation had a moderate influence.In contrast,competition and grazing played relatively minor roles.Species responses to environmental covariates were heterogeneous:soil fertility and texture had mixed effects,benefiting some species while limiting others;sand encroachment and precipitation variability generally had negative impacts,whereas grazing exclusion favored many species.A weak phylogenetic signal was recorded,indicating that community assembly was driven more by environmental filtering than by shared evolutionary history.Functional trait responses to environmental variation reflected plant strategies that balanced resource acquisition and conservation.Specifically,seed mass,leaf area,and root length increased under higher soil moisture and nutrient availability but declined in response to salinity,precipitation variability,and sand height.Clonality and perennial life history traits enhanced the survival of plant species under harsh conditions.Overall,this study provides a holistic understanding of community assembly processes in the El Bayadh steppe and offers valuable insights for ecosystem management and restoration in arid and degraded ecosystem environments.展开更多
In this paper, we propose an impact finite element （FE） model for an airbag landing buf- fer system. First, an impact FE model has been formulated for a typical airbag landing buffer system. We use the independence ...In this paper, we propose an impact finite element （FE） model for an airbag landing buf- fer system. First, an impact FE model has been formulated for a typical airbag landing buffer system. We use the independence of the structure FE model from the full impact FE model to develop a hierarchical updating scheme for the recovery module FE model and the airbag system FE model. Second, we define impact responses at key points to compare the computational and experimental results to resolve the inconsistency between the experimental data sampling frequency and experi- mental triggering. To determine the typical characteristics of the impact dynamics response of the airbag landing buffer system, we present the impact response confidence factors （IRCFs） to evalu- ate how consistent the computational and experiment results are. An error function is defined between the experimental and computational results at key points of the impact response （KPIR） to serve as a modified objective function. A radial basis function （RBF） is introduced to construct updating variables for a surrogate model for updating the objective function, thereby converting the FE model updating problem to a soluble optimization problem. Finally, the developed method has been validated using an experimental and computational study on the impact dynamics of a classic airbag landing buffer system.展开更多
Identification of attacks by a network intrusion detection system （NIDS） is an important task. In signature or rule based detection, the previously encountered attacks are modded, and signatures/rules are extracted....Identification of attacks by a network intrusion detection system （NIDS） is an important task. In signature or rule based detection, the previously encountered attacks are modded, and signatures/rules are extracted. These rules are used to detect such attacks in future, but in anomaly or outlier detection system, the normal network traffic is modeled. Any deviation from the normal model is deemed to be an outlier/attack. Data mining and machine learning techniques are widely used in offline NIDS. Unsupervised and supervised learning techniques differ the way NIDS dataset is treated. The characteristic features of unsupervised and supervised learning are finding patterns in data, detecting outliers, and determining a learned function for input features, generalizing the data instances respectively. The intuition is that if these two techniques are combined, better performance may be obtained. Hence, in this paper the advantages of unsupervised and supervised techniques are inherited in the proposed hierarchical model and devised into three stages to detect attacks in NIDS dataset. NIDS dataset is clustered using Dirichiet process （DP） clustering based on the underlying data distribution. Iteratively on each cluster, local denser areas are identified using local outlier factor （LOF） which in turn is discretized into four bins of separation based on LOF score. Further, in each bin the normal data instances are modeled using one class classifier （OCC）. A combination of Density Estimation method, Reconstruction method, and Boundary methods are used for OCC model. A product rule combination of the three methods takes into consideration the strengths of each method in building a stronger OCC model. Any deviation from this model is considered as an attack. Experiments are conducted on KDD CUP＇99 and SSENet-2011 datasets. The results show that the proposed model is able to identify attacks with higher detection rate and low false alarms.展开更多
Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global conte...Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global context for documentlevel neural machine translation(NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence.With this hierarchical architecture,we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.Notably,we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model.In addition,since large-scale in-domain document-level parallel corpora are usually unavailable,we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.展开更多
To ensure the integrity and security of cloud tenants＇ workload, and to prevent unexpected interference among tenants, cloud platform must make sure that system behaviors are trusted. By analyzing threats that exist ...To ensure the integrity and security of cloud tenants＇ workload, and to prevent unexpected interference among tenants, cloud platform must make sure that system behaviors are trusted. By analyzing threats that exist in the cloud platform, a novel trusted domain hierarchical model（TDHM） based on noninterference theory was proposed in this paper to solve these problems. First of all, the abstraction modeling of tenants＇ computing environment and trusted domain（TD） were introduced for designing TDHM with formal methods. Secondly, corresponding constraints for trusted running were given to satisfy security requirements of tenants＇ TD, and security properties of TDHM ware analyzed. After that, trusted behavior of TD was defined based on these properties, and the decision theorem of that was proved. It illustrated that the design and implementation of TD in cloud followed the model with characteristics of trusted behavior. Finally, the implementation of prototype system was introduced based on our previous work, and evaluation results showed that the performance loss was in the acceptable range.展开更多
In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the...In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.展开更多
Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree...Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.展开更多
This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in...This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.展开更多
基金the financial support from The National Key Research and Development Program of China(2022YFB3305402)The National Natural Science Foundation of China(12272072)+1 种基金The Key Project of Chongqing Technology Innovation and Application Development(CSTB2022TIAD-KPX0037)Research Project of the State Key Laboratory of Intel igent Vehicle Safety Technology(NVHSKL-202207)
文摘The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencing aging behaviors and lithium plating within lithium-ion batteries(LIBs).Available lithium-ion plating models often falter in detailed description when integrating the structural intricacies.To address this challenge,this study proposes an innovative hierarchical model that intricately incorporates the layered rolling structure in cells.Notably,our model demonstrates a remarkable capacity to predict the non-uniform distribution of current density and overpotential along the rolling direction of LIBs.Subsequently,we delve into an insightful exploration of the structural factors that influence lithium plating behavior,leveraging the foundation laid by our established model.Furthermore,we easily update the hierarchical model by considering aging factors.This aging model effectively anticipates capacity fatigue and lithium plating tendencies across individual layers of LIBs,all while maintaining computational efficiency.In light of our findings,this model yields novel perspectives on capacity fatigue dynamics and local lithium plating behaviors,offering a substantial advancement compared to existing models.This research paves the way for more efficient and tailored LIB design and operation,with broad implications for energy storage technologies.
基金co-supported by National Natural Science Foundation of China(No.51975124)Shanghai International Cooperation Project of One Belt and One Road of China(No.20110741700)Major Research Special Project of Aeroengine and Gas Turbine of China(No.J2019-IV-0016)。
文摘In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarchical Model Updating Strategy(HMUS)is developed for Finite Element(FE)model updating with regard to uncorrelated modes.The principle of HMUS is first elaborated by integrating hierarchical modeling concept,model updating technology with proper uncorrelated mode treatment,and parametric modeling.In the developed strategy,the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure.The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam.The proposed HMUS is then applied to the FE model updating of an aeroengine stator system(casings)to demonstrate its effectiveness.Our studies reveal that(A)parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics;(B)the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs(CMPs)between FE model and experiment;(C)the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model,complex geometry,high-nonlinearity and numerous parameters;(D)it is appropriate to update a complex structural FE model parameterized.The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data,which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.
基金Supported by the National Natural Science Foundation of China (No. 79931000) and The State Major Basic Research Development Program (No. G20000263).
文摘Towards integration of supply chain, manufacturing/production and investment decision making, this paper presents a hierarchical model architecture which contains six sub-models covering the areas of manufacturing control, production operation, design and revamp, production management, supply chain and investment decision making. Six types of flow, material, energy, information, humanware, partsware and capital are clasified. These flows connect enterprise components/subsystems to formulate system topology and logical structure. Enterprise components/subsystems are abstracted to generic elementary and composite classes. Finally, the model architecture is applied to a management system of an integrated suply chain, and suggestion are made on the usage of the model architecture and further development of the model as well as implementation issues.
文摘Digital libraries are complex systems and this brings difficulties for their evaluation. This paper proposes a hierarchical model to solve this problem, and puts the entangled matters into a clear-layered structure. Firstly, digital libraries(DLs thereafter)are classified into 5 groups in ascending gradations, i.e. mini DLs, small DLs, medium DLs,large DLs, and huge DLs by their scope of operation. Then, according to the characteristics of DLs at different operational scope and level of sophistication, they are further grouped into unitary DLs, union DLs and hybrid DLs accordingly. Based on this simulated structure,a hierarchical model for digital library evaluation is introduced, which evaluates DLs differentiatingly within a hierarchical scheme by using varying criteria based on their specific level of operational complexity such as at the micro-level, medium-level, and/or at the macro-level. Based on our careful examination and analysis of the current literature about DL evaluation system, an experiment is conducted by using the DL evaluation model along with its criteria for unitary DLs at micro-level. The main contents resulting from this evaluation experimentation and also those evaluation indicators and relevant issues of major concerns for DLs at medium-level and macro-level are also to be presented at some length.
文摘Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well known Mozetic＇s approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used： one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.
基金supported by the Defense Foundation Scientific Research Fund under Grant No.9140A17030308DZ02,9140A16060409DZ02the National Natural Science Fundation of Chinaunder Grant No.60934002Dr.Lianke for the extensive discussions on the subject and UESTC for its support under Grant No.JX0756,Y02018023601059
文摘Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis method is proposed based on multisignal model and support vector machine （SVM）. Multisignal model is used to describe the failure propagation relationship in electronic device system, and the most probable failure printed circuit boards （PCBs） can be found by Bayes inference. The exact failure modes in the PCBs can be identified by SVM. The results show the proposed modeling and diagnosis method is effective and suitable for diagnosis for complex electronic devices.
文摘The annual passenger volume of airport reflected its passenger transport scale and the role in aviation network.The airports in whole country were divided into three layers:first layer airports,second layer airports and third layer airports.The airlines from the first layer airports consisted the first layer aviation network.The airlines from the second layer airports consisted the second layer aviation network.The airlines from the third layer airports consisted the third layer aviation network.The structure and function of different layer aviation network had significant differences.These differences were shown in the number of airlines,average number of airlines of each airport,annual passenger volume of airport and average passenger volume of each airline.National aviation network hierarchical model was constructed to describe the whole country aviation network.The matrix was built to describe the airline number,annual passenger volume,average number of airlines,average passenger volume of each airport and airline rate of aviation network.The index of national aviation network structure was constructed to show the ratio of index between different aviation network layer to describe the aviation network structure.The structure index was built to illustrate the macrostructural features of national aviation network.The statistics data in year 1988,1994,2001,2008 and 2015 of China aviation network were analyzed and basic data matrixes,basic index matrixes and structure index matrixes were calculated.The trend of ratio of corresponding index between the first layer and the second layer showed the change of basic structure of China aviation network.At meantime,the tendency of ratio of corresponding index between the third layer and the second layer also showed the change of basic structure.The trend of network general structure index illustrated that the large scaled new airports and airlines construction had significant influence on the national aviation network structure.
基金supported by the Natural Science Foundation of China[grant numbers 12201219,12271168,12171229]Natural Science Foundation of Shanghai[grant numbers 23JS1400500,23JS1400800,22ZR1420500].
文摘A major challenge in spatial transcriptomics(ST)is resolving cellular composition,especially in technologies lacking single-cell resolution.The mixture of transcriptional signals within spatial spots complicates deconvolution and downstream analyses.To uncover the spatial heterogeneity of tissues,we introduce SvdRFCTD,a reference-free spatial transcriptomics deconvolution method,which estimates the cell type proportions at each spot on the tissue.To fully capture the heterogeneity in the ST data,we combine SvdRFCTD with a Bayesian hierarchical negative binomial model with spatial effects incorporated in both the mean and dispersion of the gene expression,which is used to explicitly model the generative mechanism of cell type proportions.By integrating spatial information and leveraging marker gene information,SvdRFCTD accurately estimates cell type proportions and uncovers complex spatial patterns.We demonstrate the ability of SvdRFCTD to identify cell types on simulated datasets.By applying SvdRFCTD to mouse brain and human pancreatic ductal adenocarcinomas datasets,we observe significant cellular heterogeneity within the tissue sections and successfully identify regions with high proportions of aggregated cell types,along with the spatial relationships between different cell types.
基金This research was funded by the CSIRO ResearchPlus Science Leader Grant Program.
文摘Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation.In porphyry Cu deposits,Cu is the primary target,with ores usually containing secondary‘pay’metals such as Au,Mo and gangue elements such as Fe and As.Due to sensing technology limitations,secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance(MR)ore sorting.Inferring the relationships between Cu and other elemental abundances is particularly critical for mineral processing.The variations in metal grade relationships occur due to the transition into different geological domains.This raises two questions-how to define these geological domains and how the metal grade relationship is influenced by these geological domains.In this paper,linear relationship is assumed between Cu grade and other metal grades.We applies a Bayesian hierarchical(partial-pooling)model to quantify the linear relationships between Cu,Au,and Fe grades from geochemical bore core data.The hierarchical model was compared with two other models-‘complete-pooling’model and‘nopooling’model.Mining blocks were split based on spatial domain to construct hierarchical model.Geochemical bore core data records metal grades measured from laboratory assay with spatial coordinates of sample location.Two case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical model.Markov chain Monte Carlo(MCMC)was used to sample the posterior parameters.Our results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling model.In addition,the posterior inference in the hierarchical model is insensitive to the choice of prior.The data is wellrepresented in the posterior which indicates a robust model.The results show that the spatial domain can be successfully utilised for metal grade regression.Uncertainty in estimating the relationship between pay metals and both secondary and gangue elements is quantified and shown to be reduced with partial-pooling.Thus,the proposed Bayesian hierarchical model can offer a reliable and stable way to monitor the relationship between metal grades for ore sorting and other mineral processing options.
文摘Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account for dependencies and variations within and between different levels of a hierarchy. By explicitly modeling these relationships, MLM provides a robust and accurate analysis of data. It has become increasingly popular in the field of education. MLM enables the investigation of various research issues, the evaluation of individual and group-level indicators, and the calculation of both fixed and random effects. Overall, MLM revolutionizes data analysis by uncovering patterns, understanding contextual effects, and making more precise statistical inferences in complex datasets. For fitting multilevel models in R, use lmer function provided by lme4 package. Through this examination, the use of a multilevel model is expected to increase and revolutionize data analysis and decision-making. The Constrained Intermediate Model (CIM) and Augmented Intermediate model (AIM) deviation are compared using the Likelihood-ratio (LR) test and the ANOVA function. This study analyzes student results from the University of Agriculture Faisalabad, collected via stratified random sampling. A linear mixed-effect model under multilevel modeling estimates the impact on CGPA, considering department, gender, intermediate marks, and entry test scores. These results indicate that Entry test is a significant predictor of CGPA, but the effect of department identifier CMC on CGPA is not statistically significant.
基金funding support from the National Natural Science Foundation of China under Grant No.52202465Hebei Provincial Natural Science Foundation for Excellent Young Scholars No.E2023202007+3 种基金China Postdoctoral Science Foundation Funded Project No.2023M741452Hebei Province Introduced Overseas Talents Funding Project No.C20220312supported by State Key Laboratory of Reliability and Intelligence of Electrical Equipment(No.EERI_OY2022007)Hebei University of Technology,S&T Program of Hebei No.215676146H and No.225676163 GH.
文摘An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption.However,most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy.To meet the requirements for safety and comfort,at the same time,enhance the economic performance of the vehicles,an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario.Specifically,the upper-level model predictive controller controls the velocity,inter-vehicle distance and acceleration to guarantee safety and comfort for driving.According to the velocity information obtained from the upper model predictive controller,the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation,so as to allocate energy rationally.Finally,the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09%economic improvement on the premise of assuring safety and comfort of driving.
基金supported by the Foundation of the University of Quebec in Abitibi-Témiscamingue(FUQAT)Quebec Research Fund(FRQ)(2021-SE7-282961)。
文摘Understanding plant community assembly is crucial for effective ecosystem conservation and restoration.The ecological filter framework describes community assembly as a process shaped by dispersal,environmental,and biotic filters.Additionally,functional traits and phylogenetic relationships are increasingly recognized as important factors influencing species coexistence and community structure.However,both the ecological filter framework and the roles of functional traits and phylogeny in community assembly remain underexplored in the Algerian steppes—particularly in the El Bayadh region,where ongoing vegetation degradation threatens ecosystem stability.This study applied Hierarchical Modeling of Species Communities(HMSC)as an integrative approach to assess how ecological filters influence plant community assembly in the El Bayadh steppe and to evaluate the roles of functional traits and phylogenetic relationships in this process.Environmental data—including soil properties,topography,precipitation,and land use types(grazing and exclosure)—were collected across 50 plots in April and October,2023,along with functional traits from 24 species.These traits include root length,leaf area,specific leaf area,clonality,life history,and seed mass.HMSC results revealed that soil properties and precipitation were the primary drivers of community structure,while sand height and elevation had a moderate influence.In contrast,competition and grazing played relatively minor roles.Species responses to environmental covariates were heterogeneous:soil fertility and texture had mixed effects,benefiting some species while limiting others;sand encroachment and precipitation variability generally had negative impacts,whereas grazing exclusion favored many species.A weak phylogenetic signal was recorded,indicating that community assembly was driven more by environmental filtering than by shared evolutionary history.Functional trait responses to environmental variation reflected plant strategies that balanced resource acquisition and conservation.Specifically,seed mass,leaf area,and root length increased under higher soil moisture and nutrient availability but declined in response to salinity,precipitation variability,and sand height.Clonality and perennial life history traits enhanced the survival of plant species under harsh conditions.Overall,this study provides a holistic understanding of community assembly processes in the El Bayadh steppe and offers valuable insights for ecosystem management and restoration in arid and degraded ecosystem environments.
基金co-supported by the National Natural Science Foundation of China(No.11472132)the Fundamental Research Funds for Central Universities in China(No.NS2014002)+1 种基金the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Nanjing University of Aeronautics and Astronautics)(No.0113Y01)the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions in China
文摘In this paper, we propose an impact finite element （FE） model for an airbag landing buf- fer system. First, an impact FE model has been formulated for a typical airbag landing buffer system. We use the independence of the structure FE model from the full impact FE model to develop a hierarchical updating scheme for the recovery module FE model and the airbag system FE model. Second, we define impact responses at key points to compare the computational and experimental results to resolve the inconsistency between the experimental data sampling frequency and experi- mental triggering. To determine the typical characteristics of the impact dynamics response of the airbag landing buffer system, we present the impact response confidence factors （IRCFs） to evalu- ate how consistent the computational and experiment results are. An error function is defined between the experimental and computational results at key points of the impact response （KPIR） to serve as a modified objective function. A radial basis function （RBF） is introduced to construct updating variables for a surrogate model for updating the objective function, thereby converting the FE model updating problem to a soluble optimization problem. Finally, the developed method has been validated using an experimental and computational study on the impact dynamics of a classic airbag landing buffer system.
文摘Identification of attacks by a network intrusion detection system （NIDS） is an important task. In signature or rule based detection, the previously encountered attacks are modded, and signatures/rules are extracted. These rules are used to detect such attacks in future, but in anomaly or outlier detection system, the normal network traffic is modeled. Any deviation from the normal model is deemed to be an outlier/attack. Data mining and machine learning techniques are widely used in offline NIDS. Unsupervised and supervised learning techniques differ the way NIDS dataset is treated. The characteristic features of unsupervised and supervised learning are finding patterns in data, detecting outliers, and determining a learned function for input features, generalizing the data instances respectively. The intuition is that if these two techniques are combined, better performance may be obtained. Hence, in this paper the advantages of unsupervised and supervised techniques are inherited in the proposed hierarchical model and devised into three stages to detect attacks in NIDS dataset. NIDS dataset is clustered using Dirichiet process （DP） clustering based on the underlying data distribution. Iteratively on each cluster, local denser areas are identified using local outlier factor （LOF） which in turn is discretized into four bins of separation based on LOF score. Further, in each bin the normal data instances are modeled using one class classifier （OCC）. A combination of Density Estimation method, Reconstruction method, and Boundary methods are used for OCC model. A product rule combination of the three methods takes into consideration the strengths of each method in building a stronger OCC model. Any deviation from this model is considered as an attack. Experiments are conducted on KDD CUP＇99 and SSENet-2011 datasets. The results show that the proposed model is able to identify attacks with higher detection rate and low false alarms.
基金supported by the National Natural Science Foundation of China under Grant Nos.61751206,61673290 and 61876118the Postgraduate Research&Practice Innovation Program of Jiangsu Province of China under Grant No.KYCX20_2669a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Document-level machine translation(MT)remains challenging due to its difficulty in efficiently using documentlevel global context for translation.In this paper,we propose a hierarchical model to learn the global context for documentlevel neural machine translation(NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence.With this hierarchical architecture,we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.Notably,we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model.In addition,since large-scale in-domain document-level parallel corpora are usually unavailable,we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.
基金supported by the National Natural Science Foundation of China (61272447)the National Key Technology R&D Program of China (2012BAH18B05)the National New Generation Broadband Wireless Mobile Communication Network Major Project (03 Project) of China (12H1510)
文摘To ensure the integrity and security of cloud tenants＇ workload, and to prevent unexpected interference among tenants, cloud platform must make sure that system behaviors are trusted. By analyzing threats that exist in the cloud platform, a novel trusted domain hierarchical model（TDHM） based on noninterference theory was proposed in this paper to solve these problems. First of all, the abstraction modeling of tenants＇ computing environment and trusted domain（TD） were introduced for designing TDHM with formal methods. Secondly, corresponding constraints for trusted running were given to satisfy security requirements of tenants＇ TD, and security properties of TDHM ware analyzed. After that, trusted behavior of TD was defined based on these properties, and the decision theorem of that was proved. It illustrated that the design and implementation of TD in cloud followed the model with characteristics of trusted behavior. Finally, the implementation of prototype system was introduced based on our previous work, and evaluation results showed that the performance loss was in the acceptable range.
基金supported by the National Science Foundation of China under Grant Nos.71361015,71340010,71371074the Jiangxi Provincial Natural Science Foundation under Grant No.20142BAB201013+2 种基金China Postdoctoral Science Foundation under Grant No.2013M540534China Postdoctoral Fund special Project under Grant No.2014T70615Jiangxi Postdoctoral Science Foundation under Grant No.2013KY53
文摘In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.
基金Project supported by the Natural Science Foundation of ZhejiangProvince, China (No. Y105697)the Ningbo Natural ScienceFoundation,China (No. 2005A610004)
文摘Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.
基金Supported by the Science and Technology Development Project Foundation of Tianjin (033800611, 05YFGZGX24200)
文摘This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.
