Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
In the environment of customization, disturbances such as rush orders and material shortages often occur in the manufacturing system, so rescheduling is necessary for the manufacturing system. The rescheduling methodo...In the environment of customization, disturbances such as rush orders and material shortages often occur in the manufacturing system, so rescheduling is necessary for the manufacturing system. The rescheduling methodology should be able to dispose of the disturbance efficiently so as to keep production going smoothly. This aims researching flow shop rescheduling problem (FSRP) necessitated by rush orders. Disjunctive graph is employed to demonstrate the FSRP. For a flow shop processing n jobs, after the original schedule has been made, and z out of n jobs have been processed in the flow shop, x rush orders come, so the original n jobs together with x rush orders should be rescheduled immediately so that the rush orders would be processed in the shortest time and the original jobs could be processed subject to some optimized criteria. The weighted mean flow time of both original jobs and rush orders is used as objective function. The weight for rush orders is much bigger than that of the original jobs, so the rush orders should be processed early in the new schedule. The ant colony optimization (ACO) algorithm used to solve the rescheduling problem has a weakness in that the search may fall into a local optimum. Mutation operation is employed to enhance the ACO performance. Numerical experiments demonstrated that the proposed algorithm has high computation repeatability and efficiency.展开更多
The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numeri...The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical bus...Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.展开更多
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial charact...The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.展开更多
To combat the well-known state-space explosion problem in Prop ositional Linear T emp o- ral Logic （PLTL） model checking, a novel algo- rithm capable of translating PLTL formulas into Nondeterministic Automata （NA...To combat the well-known state-space explosion problem in Prop ositional Linear T emp o- ral Logic （PLTL） model checking, a novel algo- rithm capable of translating PLTL formulas into Nondeterministic Automata （NA） in an efficient way is proposed. The algorithm firstly transforms PLTL formulas into their non-free forms, then it further translates the non-free formulas into their Normal Forms （NFs）, next constructs Normal Form Graphs （NFGs） for NF formulas, and it fi- nally transforms NFGs into the NA which ac- cepts both finite words and int-mite words. The experimental data show that the new algorithm re- duces the average number of nodes of target NA for a benchmark formula set and selected formulas in the literature, respectively. These results indi- cate that the PLTL model checking technique em- ploying the new algorithm generates a smaller state space in verification of concurrent systems.展开更多
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
MODIS-EVI time series data from 2000 to 2009 in Chongqing were selected for this study.By the use of best index slope extraction （BISE） method for cloud elimination,analysis on the change vector of EVI time series d...MODIS-EVI time series data from 2000 to 2009 in Chongqing were selected for this study.By the use of best index slope extraction （BISE） method for cloud elimination,analysis on the change vector of EVI time series data were conducted to investigate the EVI response on drought; then,four typical regions were selected to study the relationship between precipitation,temperature and EVI when the sever drought occurred in 2006; finally,based on the time series of vegetation condition index （VCI） and precipitation abnormity percentage,the temporal and spatial distributions of drought were studied.The results showed that,the EVI value of the summer in 2006 was significantly lower than the average EVI at the corresponding period of the other years in Chongqing.In addition,summer drought occurred mainly during the hot and dry weather.Except the southeast area,most of the other regions in Chongqing were all under severe drought.展开更多
This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to...Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.展开更多
The earthquake stress-drop values of two sequences were accurately calculated after taking away the effects due to regional earthquake anelastic attenuation and station site response,using waveform data and seismic ph...The earthquake stress-drop values of two sequences were accurately calculated after taking away the effects due to regional earthquake anelastic attenuation and station site response,using waveform data and seismic phase data of sequences of the Jinggu M_S6. 6,and Ludian M_S6. 5 earthquakes in Yunnan. These results show that the stress drop with magnitude increases within the scope of this study of magnitude. After eliminating the influence of the magnitude,the average value of stress-drop in the Jinggu sequence is higher than that of the Ludian sequence at the same magnitude range. This may be related to the stress state in different regions. In terms of the changes of time and space of stress-drop,before M_S5. 8 strong aftershock,the stress-drop is "slowing down-turning up-keeping a high value"after the mainshock,meanwhile,almost all of the abnormally high stress drop value is distributed around the M_S5. 8 strong aftershock, showing that the stress environment in the region was increasing after the mainshock. And after the M_S5. 9 strong aftershock,stress-drop rapidly declines to a relatively stable state,meanwhile,the high value of stress-drop is distributed around the strong aftershock,showing that the regional tectonic stress gets more fully release,its stress environment begins to rapidly decrease.For the Ludian sequence without a strong aftershock occurring,the average value of stress drop is lower than that of the Jinggu earthquake sequence at the same magnitude range,while at the same time,the stress-drop of the aftershock sequence almost hasn't changed much. In the time after the mainshock,combined with the release characteristics of the main energy,the stress in the region is excessively released,the subsequent stress in the region gradually returns to normal. This may be the reason why the activity of Ludianaftershocks significantly was weaker and subsequently there were no strong aftershocks occurred.展开更多
Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
Large thin walled cylindrical above ground tanks have become more susceptible to failure by buckling during earthquakes. In this study, three different geometries of tanks with H/D （height to diameter） ratios of 2.0...Large thin walled cylindrical above ground tanks have become more susceptible to failure by buckling during earthquakes. In this study, three different geometries of tanks with H/D （height to diameter） ratios of 2.0, 0.56, 1.0, and D/t （depth to thickness） ratios of 960.0, 1,706.67 and 640.0 respectively were analyzed for stability when subjected to the E1 Centro earthquake at the base. The Budiansky and Roth procedure was used to find the buckling loads when the tanks were empty and when they were filled with liquid up to 90% of their depth. Also, nonlinear time history analysis using ANSYS finite element computer program was performed. Analysis results show that the dynamic buckling occurs for empty tanks at very high PGA （peak ground accelerations） which are unrealistic even for major earthquakes. Furthermore, when the tanks filled with water up to 90% of its height, analysis results show that when the H/D ratio reduced by two times （i.e., from 2 to 1）, the PGA for the buckling increased by six times （increase from 0.25g to 1 .Sg）. Hence, H/D ratio plays an important role in the earthquake stability design of over ground steel tanks.展开更多
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘In the environment of customization, disturbances such as rush orders and material shortages often occur in the manufacturing system, so rescheduling is necessary for the manufacturing system. The rescheduling methodology should be able to dispose of the disturbance efficiently so as to keep production going smoothly. This aims researching flow shop rescheduling problem (FSRP) necessitated by rush orders. Disjunctive graph is employed to demonstrate the FSRP. For a flow shop processing n jobs, after the original schedule has been made, and z out of n jobs have been processed in the flow shop, x rush orders come, so the original n jobs together with x rush orders should be rescheduled immediately so that the rush orders would be processed in the shortest time and the original jobs could be processed subject to some optimized criteria. The weighted mean flow time of both original jobs and rush orders is used as objective function. The weight for rush orders is much bigger than that of the original jobs, so the rush orders should be processed early in the new schedule. The ant colony optimization (ACO) algorithm used to solve the rescheduling problem has a weakness in that the search may fall into a local optimum. Mutation operation is employed to enhance the ACO performance. Numerical experiments demonstrated that the proposed algorithm has high computation repeatability and efficiency.
基金Under the auspices of National Natural Science Foundation of China(No.41471289,41301368)Natural Science Foundation of Jilin Province(No.20140101158JC)Foundation of State Key Laboratory of Remote Sensing Science(No.OFSLRSS201517)
文摘The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
基金the National Natural Science Foundation of China(Nos.71731001,61573181,71971114)the Fundamental Research Funds for the Central Universities(No.NS2020045)。
文摘Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.
基金Projects(LQ16E080012,LY14F030012)supported by the Zhejiang Provincial Natural Science Foundation,ChinaProject(61573317)supported by the National Natural Science Foundation of ChinaProject(2015001)supported by the Open Fund for a Key-Key Discipline of Zhejiang University of Technology,China
文摘The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
基金The first author of this paper would like to thank the follow- ing scholars, Prof. Joseph Sifakis, 2007 Turing Award Winner, for his invaluable help with my research and Dr. Kevin Lu at Brunel University, UK for his excellent suggestions on this paper. This work was supported by the National Natural Sci- ence Foundation of China under Grant No.61003079 the Chi- na Postdoctoral Science Foundation under Grant No. 2012M511588.
文摘To combat the well-known state-space explosion problem in Prop ositional Linear T emp o- ral Logic （PLTL） model checking, a novel algo- rithm capable of translating PLTL formulas into Nondeterministic Automata （NA） in an efficient way is proposed. The algorithm firstly transforms PLTL formulas into their non-free forms, then it further translates the non-free formulas into their Normal Forms （NFs）, next constructs Normal Form Graphs （NFGs） for NF formulas, and it fi- nally transforms NFGs into the NA which ac- cepts both finite words and int-mite words. The experimental data show that the new algorithm re- duces the average number of nodes of target NA for a benchmark formula set and selected formulas in the literature, respectively. These results indi- cate that the PLTL model checking technique em- ploying the new algorithm generates a smaller state space in verification of concurrent systems.
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
基金Supported by Foundation for Science and Technology Research Project of Chongqing (2009AC0125)Natural Science Foundation of Chongqing (2008BB1379)The Major Project of Ministry of Science and Technology," Science and Technology Action for Western Development" (2005BA901A01)~~
文摘MODIS-EVI time series data from 2000 to 2009 in Chongqing were selected for this study.By the use of best index slope extraction （BISE） method for cloud elimination,analysis on the change vector of EVI time series data were conducted to investigate the EVI response on drought; then,four typical regions were selected to study the relationship between precipitation,temperature and EVI when the sever drought occurred in 2006; finally,based on the time series of vegetation condition index （VCI） and precipitation abnormity percentage,the temporal and spatial distributions of drought were studied.The results showed that,the EVI value of the summer in 2006 was significantly lower than the average EVI at the corresponding period of the other years in Chongqing.In addition,summer drought occurred mainly during the hot and dry weather.Except the southeast area,most of the other regions in Chongqing were all under severe drought.
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61521003)The National Key R&D Program of China (No.2016YFB0800101)+1 种基金the National Science Foundation for Distinguished Young Scholars of China (No.61602509)Henan Province Key Technologies R&D Program of China(No.172102210615)
文摘Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.
基金supported by the“Catalogue of Earthquake Sequence in the Chinese Mainland”of Department of Monitoring and Prediction,China Earthquake Administration(1740503502)
文摘The earthquake stress-drop values of two sequences were accurately calculated after taking away the effects due to regional earthquake anelastic attenuation and station site response,using waveform data and seismic phase data of sequences of the Jinggu M_S6. 6,and Ludian M_S6. 5 earthquakes in Yunnan. These results show that the stress drop with magnitude increases within the scope of this study of magnitude. After eliminating the influence of the magnitude,the average value of stress-drop in the Jinggu sequence is higher than that of the Ludian sequence at the same magnitude range. This may be related to the stress state in different regions. In terms of the changes of time and space of stress-drop,before M_S5. 8 strong aftershock,the stress-drop is "slowing down-turning up-keeping a high value"after the mainshock,meanwhile,almost all of the abnormally high stress drop value is distributed around the M_S5. 8 strong aftershock, showing that the stress environment in the region was increasing after the mainshock. And after the M_S5. 9 strong aftershock,stress-drop rapidly declines to a relatively stable state,meanwhile,the high value of stress-drop is distributed around the strong aftershock,showing that the regional tectonic stress gets more fully release,its stress environment begins to rapidly decrease.For the Ludian sequence without a strong aftershock occurring,the average value of stress drop is lower than that of the Jinggu earthquake sequence at the same magnitude range,while at the same time,the stress-drop of the aftershock sequence almost hasn't changed much. In the time after the mainshock,combined with the release characteristics of the main energy,the stress in the region is excessively released,the subsequent stress in the region gradually returns to normal. This may be the reason why the activity of Ludianaftershocks significantly was weaker and subsequently there were no strong aftershocks occurred.
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
文摘Large thin walled cylindrical above ground tanks have become more susceptible to failure by buckling during earthquakes. In this study, three different geometries of tanks with H/D （height to diameter） ratios of 2.0, 0.56, 1.0, and D/t （depth to thickness） ratios of 960.0, 1,706.67 and 640.0 respectively were analyzed for stability when subjected to the E1 Centro earthquake at the base. The Budiansky and Roth procedure was used to find the buckling loads when the tanks were empty and when they were filled with liquid up to 90% of their depth. Also, nonlinear time history analysis using ANSYS finite element computer program was performed. Analysis results show that the dynamic buckling occurs for empty tanks at very high PGA （peak ground accelerations） which are unrealistic even for major earthquakes. Furthermore, when the tanks filled with water up to 90% of its height, analysis results show that when the H/D ratio reduced by two times （i.e., from 2 to 1）, the PGA for the buckling increased by six times （increase from 0.25g to 1 .Sg）. Hence, H/D ratio plays an important role in the earthquake stability design of over ground steel tanks.
