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
The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely u...The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.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
Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emp...Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.展开更多
This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study are...This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.展开更多
In order to compare the aviation network of mid-south,northwest and southwest of China to reveal the structure similarity and difference for providing quantitative evidence to construct regional aviation network and i...In order to compare the aviation network of mid-south,northwest and southwest of China to reveal the structure similarity and difference for providing quantitative evidence to construct regional aviation network and improve its structure,hierarchical index model of regional aviation network was established through dividing the aviation network into layers to research its structure characters.Data matrixes were defined to record the basic state of regional aviation network.Index matrixes were constructed to describe the quantitative features of regional aviation network.On the basis of these indexes,several structure indexes of all layers of aviation network were calculated to show the structure features of aviation network,such as ratio of passenger volume within the region with across the region,share rate of passenger volume among layers,ratio of average number of airline for each airport,ratio of average passenger volume for each airline and ratio of airline rate.According to the statistical data,similar structure of share rate of passenger volume among layers and average passenger volume for each airline in their regional aviation network was found after calculating.But on the side of ratio of passenger volume within the region with across the region,ratio of average number of airlines for each airport and ratio of airline rate were different.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
In recent years,housing prices have attracted widespread attention,and the fluctuation of housing prices is due to a combination of many factors.In addition to the characteristics of the house itself,the price of a ho...In recent years,housing prices have attracted widespread attention,and the fluctuation of housing prices is due to a combination of many factors.In addition to the characteristics of the house itself,the price of a house is also affected by other factors,such as the community in which the house is located.This article used Beijing’s 2017 second-hand housing transaction data (based on second-hand housing transaction records on Lianjia.com),introduced a hierarchical linear model,and employed Stata software to analyze from different levels.It is intended to find the correlation between housing prices and different levels of characteristics,so to pin down the factors that affect prices of the second-hand housing.展开更多
This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to ...This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.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
Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in th...Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.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
It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-...It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-constrained and norm-constrained portfolios,can usually achieve much higher ex post Sharpe ratio.Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions.In this paper,we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model.We showthat such constructed portfolios are well diversified with superior out-of-sample performance.Our proposed model is tested on a number of Fama–French industry portfolios against the na飗e diversification strategy and Chevrier and McCulloch’s(2008)economically motivated prior(EMP)strategy.On average,our model outperforms Chevrier and McCulloch’s(2008)EMP strategy by over 15%and outperform the‘1/N’strategy by over 50%.展开更多
As one of the top four commercially important species in China,yellow croaker(Larimichthys polyactis)with two geographic subpopulations,has undergone profound changes during the last several decades.It is widely compr...As one of the top four commercially important species in China,yellow croaker(Larimichthys polyactis)with two geographic subpopulations,has undergone profound changes during the last several decades.It is widely comprehended that understanding its population dynamics is critically important for sustainable management of this valuable fishery in China.The only two existing population dynamics models assessed the population of yellow croaker using short time-series data,without considering geographical variations.In this study,Bayesian models with and without hierarchical subpopulation structure were developed to explore the spatial heterogeneity of the population dynamics of yellow croaker from 1968 to 2015.Alternative hypotheses were constructed to test potential temporal patterns in yellow croaker’s population dynamics.Substantial variations in population dynamics characteristics among space and time were found through this study.The population growth rate was revealed to increase since the late 1980s,and the catchability increased more than twice from 1981 to 2015.The East China Sea’s subpopulation witnesses faster growth,but suffers from higher fishing pressure than that in the Bohai Sea and Yellow Sea.The global population and two subpopulations all have high risks of overfishing and being overfished according to the MSY-based reference points in recent years.More conservative management strategies with subpopulation considerations are imperative for the fishery management of yellow croaker in China.The methodology developed in this study could also be applied to the stock assessment and fishery management of other species,especially for those species with large spatial heterogeneity data.展开更多
Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI...Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.展开更多
The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sus...The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sustainable development of large-scale water diversion projects. Through the investigation of relevant literature, books, web pages, materials, and discussions with relevant experts and scholars, a total of 23 factors influencing the sustainable supply chain implementation of water diversion projects were identified. Then using ISM (Interpretative Structural Modeling Method) to analyze the causality of each factor, a multi-level hierarchical structure model was obtained. The results showed that: 1) The surface-level influencing factors of the sustaina<span>ble supply chain implementation of the water diversion project mainly i</span>ncluded 8 factors such as water-saving awareness and water-saving intensity in the diversion area, water quality, water pollution and other disasters, effective incentive mechanisms, etc., and surface-level influencing factors were directly related to the sustainable supply chain implementation of water diversio<span>n projects. 2) The indirect influencing factors of the sustainable supply chai</span>n of water diversion projects included 12 factors such as the water quality and quantity guarantee rate of the supply chain, the government’s enforcement of laws and regulations, water distribution, ecological compensation, and compensatio<span>n mechanisms for residents in the water source area. Indirect influencing</span> factor scan acts directly on the direct influencing factors, and int<span>ervening in the factors that can be controlled by humans is one of the important ways to improve the sustainable operation of water diversion proj</span><span>e</span><span>cts. 3) T</span><span>he fundamental influencing factors for the sustainable supply chain implementation of water diversion projects included three f</span>actors: Resettlement policy, government financial support, and sound laws and regulations. Deep influencing factors had multi-channel influence and controllability, and intervening in them was the main means to improve the sustainable operation of water diversion projects.展开更多
This paper proposes the architecture of an intelligent flight launcher system as well as fundamental solutions to capability prediction and dynamic planning. This effort reflects the latest progress in the application...This paper proposes the architecture of an intelligent flight launcher system as well as fundamental solutions to capability prediction and dynamic planning. This effort reflects the latest progress in the applications of intelligent and autonomous technology for launcher flights. The paper first describes the characteristics and capabilities of intelligent and autonomous systems and classifies various related technologies. In the context of intelligent and autonomous technology in aerospace engineering, it then focuses on technical difficulties involved with intelligent flight and reviews developments in the field. An E^3 classification model of an intelligent flight launcher is then proposed and its application scenarios are discussed. Based on an intelligent flight system configuration of the launcher, the online trajectory planning and initial value guess are examined, and vertical landing is provided as an example to explain the effects of the implementation of computational intelligence to flight systems.展开更多
Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these f...Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional bio- mass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. Fromthese publications we chose 52 study sites spread out across South Korea. The statistical model for the meta- analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quanti- tative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publica- tions were 1.016 (0.800-1.299), 1.414 (1.304-1.560) and 0.260 (0.200-0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.展开更多
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.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011244).
文摘The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.
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The authors gratefully acknowledge the support of the National Natural Science Foundation of China under Grant No.62076204 and Grant No.61612385in part by the Postdoctoral Science Foundation of China under Grants No.2021M700337in part by the Fundamental Research Funds for the Central Universities under Grant No.3102019ZX016.
文摘Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.
基金supported by the National Natural Science Foundation of China(Grants No.51779074 and 41371052)the Special Fund for the Public Welfare Industry of the Ministry of Water Resources of China(Grant No.201501059)+3 种基金the National Key Research and Development Program of China(Grant No.2017YFC0404304)the Jiangsu Water Conservancy Science and Technology Project(Grant No.2017027)the Program for Outstanding Young Talents in Colleges and Universities of Anhui Province(Grant No.gxyq2018143)the Natural Science Foundation of Wanjiang University of Technology(Grant No.WG18030)
文摘This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.
文摘In order to compare the aviation network of mid-south,northwest and southwest of China to reveal the structure similarity and difference for providing quantitative evidence to construct regional aviation network and improve its structure,hierarchical index model of regional aviation network was established through dividing the aviation network into layers to research its structure characters.Data matrixes were defined to record the basic state of regional aviation network.Index matrixes were constructed to describe the quantitative features of regional aviation network.On the basis of these indexes,several structure indexes of all layers of aviation network were calculated to show the structure features of aviation network,such as ratio of passenger volume within the region with across the region,share rate of passenger volume among layers,ratio of average number of airline for each airport,ratio of average passenger volume for each airline and ratio of airline rate.According to the statistical data,similar structure of share rate of passenger volume among layers and average passenger volume for each airline in their regional aviation network was found after calculating.But on the side of ratio of passenger volume within the region with across the region,ratio of average number of airlines for each airport and ratio of airline rate were different.
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.
文摘In recent years,housing prices have attracted widespread attention,and the fluctuation of housing prices is due to a combination of many factors.In addition to the characteristics of the house itself,the price of a house is also affected by other factors,such as the community in which the house is located.This article used Beijing’s 2017 second-hand housing transaction data (based on second-hand housing transaction records on Lianjia.com),introduced a hierarchical linear model,and employed Stata software to analyze from different levels.It is intended to find the correlation between housing prices and different levels of characteristics,so to pin down the factors that affect prices of the second-hand housing.
文摘This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.
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.
文摘Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.
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.
基金This work was supported in part by US National Science Foundation(NSF)under grant DMS-1613110。
文摘It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-constrained and norm-constrained portfolios,can usually achieve much higher ex post Sharpe ratio.Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions.In this paper,we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model.We showthat such constructed portfolios are well diversified with superior out-of-sample performance.Our proposed model is tested on a number of Fama–French industry portfolios against the na飗e diversification strategy and Chevrier and McCulloch’s(2008)economically motivated prior(EMP)strategy.On average,our model outperforms Chevrier and McCulloch’s(2008)EMP strategy by over 15%and outperform the‘1/N’strategy by over 50%.
基金Foundation item:The National Key R&D Program of China under contract No.2017YFE0104400the National Natural Science Foundation of China under contract No.31772852the Fundamental Research Funds for the Central Universities under contract Nos 201512002 and 201562030.
文摘As one of the top four commercially important species in China,yellow croaker(Larimichthys polyactis)with two geographic subpopulations,has undergone profound changes during the last several decades.It is widely comprehended that understanding its population dynamics is critically important for sustainable management of this valuable fishery in China.The only two existing population dynamics models assessed the population of yellow croaker using short time-series data,without considering geographical variations.In this study,Bayesian models with and without hierarchical subpopulation structure were developed to explore the spatial heterogeneity of the population dynamics of yellow croaker from 1968 to 2015.Alternative hypotheses were constructed to test potential temporal patterns in yellow croaker’s population dynamics.Substantial variations in population dynamics characteristics among space and time were found through this study.The population growth rate was revealed to increase since the late 1980s,and the catchability increased more than twice from 1981 to 2015.The East China Sea’s subpopulation witnesses faster growth,but suffers from higher fishing pressure than that in the Bohai Sea and Yellow Sea.The global population and two subpopulations all have high risks of overfishing and being overfished according to the MSY-based reference points in recent years.More conservative management strategies with subpopulation considerations are imperative for the fishery management of yellow croaker in China.The methodology developed in this study could also be applied to the stock assessment and fishery management of other species,especially for those species with large spatial heterogeneity data.
基金supported by the National Key Research and Development Program of China (2019YFE0125300)the Shandong Provincial Key R&D Plan (2021LZGC026)the China Agriculture Research System (CARS-03)。
文摘Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.
文摘The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sustainable development of large-scale water diversion projects. Through the investigation of relevant literature, books, web pages, materials, and discussions with relevant experts and scholars, a total of 23 factors influencing the sustainable supply chain implementation of water diversion projects were identified. Then using ISM (Interpretative Structural Modeling Method) to analyze the causality of each factor, a multi-level hierarchical structure model was obtained. The results showed that: 1) The surface-level influencing factors of the sustaina<span>ble supply chain implementation of the water diversion project mainly i</span>ncluded 8 factors such as water-saving awareness and water-saving intensity in the diversion area, water quality, water pollution and other disasters, effective incentive mechanisms, etc., and surface-level influencing factors were directly related to the sustainable supply chain implementation of water diversio<span>n projects. 2) The indirect influencing factors of the sustainable supply chai</span>n of water diversion projects included 12 factors such as the water quality and quantity guarantee rate of the supply chain, the government’s enforcement of laws and regulations, water distribution, ecological compensation, and compensatio<span>n mechanisms for residents in the water source area. Indirect influencing</span> factor scan acts directly on the direct influencing factors, and int<span>ervening in the factors that can be controlled by humans is one of the important ways to improve the sustainable operation of water diversion proj</span><span>e</span><span>cts. 3) T</span><span>he fundamental influencing factors for the sustainable supply chain implementation of water diversion projects included three f</span>actors: Resettlement policy, government financial support, and sound laws and regulations. Deep influencing factors had multi-channel influence and controllability, and intervening in them was the main means to improve the sustainable operation of water diversion projects.
文摘This paper proposes the architecture of an intelligent flight launcher system as well as fundamental solutions to capability prediction and dynamic planning. This effort reflects the latest progress in the applications of intelligent and autonomous technology for launcher flights. The paper first describes the characteristics and capabilities of intelligent and autonomous systems and classifies various related technologies. In the context of intelligent and autonomous technology in aerospace engineering, it then focuses on technical difficulties involved with intelligent flight and reviews developments in the field. An E^3 classification model of an intelligent flight launcher is then proposed and its application scenarios are discussed. Based on an intelligent flight system configuration of the launcher, the online trajectory planning and initial value guess are examined, and vertical landing is provided as an example to explain the effects of the implementation of computational intelligence to flight systems.
文摘Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional bio- mass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. Fromthese publications we chose 52 study sites spread out across South Korea. The statistical model for the meta- analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quanti- tative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publica- tions were 1.016 (0.800-1.299), 1.414 (1.304-1.560) and 0.260 (0.200-0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.