[Objective] The paper was to discuss why the top of tall barchan dunes and barchan chains widespread in single prevailing wind area had not been leveled by wind erosion. [Method] Based on the preliminary survey of dis...[Objective] The paper was to discuss why the top of tall barchan dunes and barchan chains widespread in single prevailing wind area had not been leveled by wind erosion. [Method] Based on the preliminary survey of distribution status,the morphological characteristics and environmental conditions of barchan dunes and barchan chains in Hexi desert area of Gansu were investigated in details. The significance of difference between samples and significance of correlation between indicators were examined via variance test. [Result] Barchan dunes and barchan chains in Hexi desert area of Gansu distributed at the leeward direction of desert fringe,generally in patch distribution. The distribution area was gravelly beach or cohesive gravel beach,with broader dune slack; winds in distribution area of barchan dunes and barchan chains blew obviously from one direction,while winds at other directions were light or occasionally strong but with low frequency;the barchan dune in the desert fringe of Hexi desert area of Gansu was relatively tall,while barchan chain was even more taller and larger. Coincidence or separation of the dune peak and the sand ridge might be related to distribution frequency of dominant prevailing wind or wind at opposite direction and the observation seasons.[Conclusion]Studying top stability of barchan dune has an important academic value in revealing blowing sand movement rule at desert fringe,invasion of sand flow,and expansion of desert.展开更多
Multi-way principal component analysis(MPCA)has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensiona...Multi-way principal component analysis(MPCA)has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error(SPE)statistic and the Hotelling(T2)statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system(ASCS)to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.展开更多
基金Supported by Special Project for Preceding Study of 973 Program"Stability Research of Tall Barchan Dune at Oasis Fringe(2014CB460611)National Natural Science Foundation of China"Climatic and Environmental Factors for Formation of Sand Sediment Zone and Its Ecological Effects at Minqin Oasis Fringe of Gansu Province(41261102)
文摘[Objective] The paper was to discuss why the top of tall barchan dunes and barchan chains widespread in single prevailing wind area had not been leveled by wind erosion. [Method] Based on the preliminary survey of distribution status,the morphological characteristics and environmental conditions of barchan dunes and barchan chains in Hexi desert area of Gansu were investigated in details. The significance of difference between samples and significance of correlation between indicators were examined via variance test. [Result] Barchan dunes and barchan chains in Hexi desert area of Gansu distributed at the leeward direction of desert fringe,generally in patch distribution. The distribution area was gravelly beach or cohesive gravel beach,with broader dune slack; winds in distribution area of barchan dunes and barchan chains blew obviously from one direction,while winds at other directions were light or occasionally strong but with low frequency;the barchan dune in the desert fringe of Hexi desert area of Gansu was relatively tall,while barchan chain was even more taller and larger. Coincidence or separation of the dune peak and the sand ridge might be related to distribution frequency of dominant prevailing wind or wind at opposite direction and the observation seasons.[Conclusion]Studying top stability of barchan dune has an important academic value in revealing blowing sand movement rule at desert fringe,invasion of sand flow,and expansion of desert.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2011AA11A223)
文摘Multi-way principal component analysis(MPCA)has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error(SPE)statistic and the Hotelling(T2)statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system(ASCS)to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.
