Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SIND...Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses.展开更多
This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear iden...This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.展开更多
Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification ...Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification issues of a 5-DoF parallel machining robot,this paper proposes an adaptive and weighted identification method to achieve high-precision kinematic calibration while maintaining reliable stability.First,a kinematic error propagation mechanism model considering the non-ideal constraints and the screw self-rotation is formulated by incorporating the intricate structure of multiple chains and a unique driven screw arrangement of the robot.To address the challenge of accurately identifying such a sophisticated error model,a novel adaptive and weighted identification method based on generalized cross validation(GCV)is proposed.Specifically,this approach innovatively introduces Gauss-Markov estimation into the GCV algorithm and utilizes prior physical information to construct both a weighted identification model and a weighted cross-validation function,thus eliminating the inaccuracy caused by significant differences in dimensional magnitudes of pose errors and achieving accurate identification with flexible numerical stability.Finally,the kinematic calibration experiment is conducted.The comparative experimental results demonstrate that the presented approach is effective and has enhanced accuracy performance over typical least squares methods,with maximum position and orientation errors reduced from 2.279 mm to 0.028 mm and from 0.206°to 0.017°,respectively.展开更多
Gust front is a kind of meso-and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan...Gust front is a kind of meso-and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan samples selected from 21 gust front weather processes that occurred in China between 2009 and 2012 are examined and analyzed. Gust front echo statistical features in reflectivity, velocity, and spectrum width fields are obtained. Based on these features, an algorithm is designed to recognize gust fronts and generate output products and quantitative indices. Then, 315 samples are used to verify the algorithm and 3 typical cases are analyzed. Major conclusions include： 1） for narrow band echoes intensity is between 5 and 30 dBZ, widths are between 2 and 10 km, maximum heights are less than 4 km （89.33%are lower than 3 km）, and the lengths are between 50 and 200 km. The narrow-band echo is higher than its surrounding echo. 2） Gust fronts present a convergence line or a wind shear in the velocity field;the frontal wind speed gradually decreases when the distance increases radially outward. Spectral widths of gust fronts are large, with 87.09% exceeding 4 m s-1 . 3） Using 315 gust front volume-scan samples to test the algorithm reveals that the algorithm is highly stable and has successfully recognized 277 samples. The algorithm also works for small-scale or weak gust fronts. 4） Radar data quality has certain impact on the algorithm.展开更多
MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,i...MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,it is significantly to accurately identify the temperature compensation model in this paper,the calibration parameters were first extracted by using the fast calibration algorithm based on the Persistent Excitation Signal Criterion,and then,MEMS gyro temperature compensation model was established by utilizing the thin plate spline interpolation method,and the corresponding identification results were compared with the results from the polynomial fitting method.The effectiveness of the proposed algorithm has been validated through the comparative experiment.展开更多
By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identific...By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method.展开更多
In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model ...In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.展开更多
A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To pr...A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.展开更多
Transmission Control Protocol (TCP) optimization in Mobile Ad hoc NETworks (MANETs) is a challenging issue because of some unique characteristics of MANETs. In this paper,a new end-to-end mechanism based on multiple m...Transmission Control Protocol (TCP) optimization in Mobile Ad hoc NETworks (MANETs) is a challenging issue because of some unique characteristics of MANETs. In this paper,a new end-to-end mechanism based on multiple metrics measurement is proposed to improve TCP performance in MANETs. Multi-metric Measurement based Enhancement of TCP (MME-TCP) designs the metrics and the identification algorithm according to the characteristics of MANETs and the experiment results. Furthermore,these metrics are measured at the sender node to reduce the overhead of control information over networks. Simulation results show that MME-TCP mechanism achieves a significant performance improvement over standard TCP in MANETs.展开更多
Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep le...Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep learning-based automatic gust front identification algorithm is proposed in this study.The algorithm utilizes Mask Region-based Convolutional Neural Network(Mask R-CNN),a state-of-the-art instance segmentation model,trained on a large dataset of 2623 gust front samples from S-band weather radar volume scans in East China and the North China Plain between 2009 and 2016.Extensive data preprocessing and manual annotation are performed to prepare the training dataset.The optimized model achieves impressive performance on a test set of 604 samples,with a detection probability of 93.21%,a false alarm rate of 3.60%,a missed alarm rate of 6.79%,and a critical success index of 90.08%.The algorithm demonstrates robust identification capabilities across gust fronts of varying scales,types,and parent thunderstorm systems,highlighting its operational applicability.展开更多
Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geom...Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea（SCS）. However, very few studies attempt to examine eddies＇ internal evolution processes. In this study, we reported a hybrid method to trace eddies＇ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies（DCEs） and the three long-lived anticyclonic eddies（ACEs） in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.展开更多
In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at ...In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at the surface weather stations in the Pearl River Delta(PRD)during five warm seasons,the properties and environmental conditions of the EXHP-associated MVs are analyzed.Further,the spatiotemporal distributions of the MV,instantaneous rain rate,and EXHP are illustrated for three events with the most abundant EXHP records.The major findings are as follows.About 42%EXHP records are accompanied by 57 MVs,including 84%of weak shear intensity,12%of weak mesocyclone intensity,and 4%of moderate mesocyclone intensity,with the rotational speeds between 5 and12 m s^(-1),12 and 16 m s^(-1),and 16 and 21 m s^(-1),respectively.The duration and core depth of the MVs are highly correlated(coefficient of 0.67)with averages of 39 min and 699 m,respectively.The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP,while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating.Relative to the EXHP events with MVs in the United States,those in the PRD feature smaller environmental 0-3-km storm relative helicity(SRH)and 0-1-km vertical wind shear(VWS).However,compared to the non-rotational EXHP in the PRD,the rotational EXHP events are associated with significantly higher 0-1-km VWS,0-3-km SRH,humidity,and larger convective available potential energy.In the three selected events,rainstorms exhibit an irregular shape,a quasi-circular shape,and a quasi-banded shape of strong echo,respectively.The MVs are often located inside the strong radar reflectivity region,and some are next to its bow-shaped portion.Those longer-lived MVs with stronger rotation are collocated with the extreme 6-min rainfall accumulation(≥10 mm)in space and time,suggesting presence of positive feedback between low-level rotation and short-term rain rate.In the event influenced by a tropical storm,four MVs appear at almost the same location in succession and move along roughly the same path,forming an MV back-building process.展开更多
Traditional control does not pay much attention to information security problems in system identification enough, which are important in practical applications. This paper focuses on the security problem of input info...Traditional control does not pay much attention to information security problems in system identification enough, which are important in practical applications. This paper focuses on the security problem of input information in a class of system identification problems with noise and binary-valued observations, presents a cryptography based security protocol, and improves it in the range of allowed errors. During solving the identification problem, the improved security protocol can ensure that the input information is not leaked, and thus, can deal with passive attacks effectively. Besides, a quantitative relationship among the input information, the public key in encryption and the number of partieipailts in the improved protocol is shown. Finally, the simulation results show that, the identification algorithm can still achieve the estimation accuracy by adding the improved security protocol. However, compared with the original identification algorithm, the time complexity of the algorithm with the improved security protocol increases.展开更多
The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system sta...The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-area 4-generator and New England-New York 68-bus 16-generator systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and inter-area modes, and indicate the improvements in oscillation damping and power system stability.展开更多
基金The work was supported by the National Science Foundation of China(grant nos.11772218 and 11872044)China-UK NSFC-RS Joint Project(grant nos.11911530177 in China and IE181496 in the UK)Tianjin Research Program of Application Foundation and Advanced Technology(grant no.17JCYBJC18900).
文摘Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses.
基金supported by the National Natural Science Foundation of China under Grant No.62025306CAS Project for Young Scientists in Basic Research under Grant No.YSBR-008。
文摘This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.
基金Supported by National Key R&D Program of China(Grant No.2022YFB3404101)National Natural Science Foundation of China(Grant Nos.52375018,92148301)。
文摘Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification issues of a 5-DoF parallel machining robot,this paper proposes an adaptive and weighted identification method to achieve high-precision kinematic calibration while maintaining reliable stability.First,a kinematic error propagation mechanism model considering the non-ideal constraints and the screw self-rotation is formulated by incorporating the intricate structure of multiple chains and a unique driven screw arrangement of the robot.To address the challenge of accurately identifying such a sophisticated error model,a novel adaptive and weighted identification method based on generalized cross validation(GCV)is proposed.Specifically,this approach innovatively introduces Gauss-Markov estimation into the GCV algorithm and utilizes prior physical information to construct both a weighted identification model and a weighted cross-validation function,thus eliminating the inaccuracy caused by significant differences in dimensional magnitudes of pose errors and achieving accurate identification with flexible numerical stability.Finally,the kinematic calibration experiment is conducted.The comparative experimental results demonstrate that the presented approach is effective and has enhanced accuracy performance over typical least squares methods,with maximum position and orientation errors reduced from 2.279 mm to 0.028 mm and from 0.206°to 0.017°,respectively.
基金Supported by the Phased Array Weather Radar Pre-Research Program(40701030101)
文摘Gust front is a kind of meso-and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan samples selected from 21 gust front weather processes that occurred in China between 2009 and 2012 are examined and analyzed. Gust front echo statistical features in reflectivity, velocity, and spectrum width fields are obtained. Based on these features, an algorithm is designed to recognize gust fronts and generate output products and quantitative indices. Then, 315 samples are used to verify the algorithm and 3 typical cases are analyzed. Major conclusions include： 1） for narrow band echoes intensity is between 5 and 30 dBZ, widths are between 2 and 10 km, maximum heights are less than 4 km （89.33%are lower than 3 km）, and the lengths are between 50 and 200 km. The narrow-band echo is higher than its surrounding echo. 2） Gust fronts present a convergence line or a wind shear in the velocity field;the frontal wind speed gradually decreases when the distance increases radially outward. Spectral widths of gust fronts are large, with 87.09% exceeding 4 m s-1 . 3） Using 315 gust front volume-scan samples to test the algorithm reveals that the algorithm is highly stable and has successfully recognized 277 samples. The algorithm also works for small-scale or weak gust fronts. 4） Radar data quality has certain impact on the algorithm.
文摘MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,it is significantly to accurately identify the temperature compensation model in this paper,the calibration parameters were first extracted by using the fast calibration algorithm based on the Persistent Excitation Signal Criterion,and then,MEMS gyro temperature compensation model was established by utilizing the thin plate spline interpolation method,and the corresponding identification results were compared with the results from the polynomial fitting method.The effectiveness of the proposed algorithm has been validated through the comparative experiment.
文摘By applying genetic algorithms (GA) to on-line identification of linear time-varying systems; a number of modifications are made to the Simple Genetic Algorithm to improve the performance of the algorithm in identification applications. The simulation results indicate that the method is not only capable of following the changing parameters of the system, but also has improved the identification accuracy compared with that using the least square method.
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.
基金supported by the Open Research Project of CAS Large Research InfrastructuresCAS Key Technology Talent ProgramNational Natural Science Foundations of China (Nos.U2031206 and 12273086)
文摘A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.
基金Supported by the National Natural Science Foundation of China (No.60496314)the Chinese 863 National High Technology Program (No.2002AA783043).
文摘Transmission Control Protocol (TCP) optimization in Mobile Ad hoc NETworks (MANETs) is a challenging issue because of some unique characteristics of MANETs. In this paper,a new end-to-end mechanism based on multiple metrics measurement is proposed to improve TCP performance in MANETs. Multi-metric Measurement based Enhancement of TCP (MME-TCP) designs the metrics and the identification algorithm according to the characteristics of MANETs and the experiment results. Furthermore,these metrics are measured at the sender node to reduce the overhead of control information over networks. Simulation results show that MME-TCP mechanism achieves a significant performance improvement over standard TCP in MANETs.
基金Supported by the National Key Research and Development Program of China(2023YFC3007501)Open Fund of China Meteorological Administration Key Laboratory for Aviation Meteorology(HKQXZ-2024005)Innovation Ability Promotion Plan of Chengdu University of Information Technology(KYQN202307).
文摘Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep learning-based automatic gust front identification algorithm is proposed in this study.The algorithm utilizes Mask Region-based Convolutional Neural Network(Mask R-CNN),a state-of-the-art instance segmentation model,trained on a large dataset of 2623 gust front samples from S-band weather radar volume scans in East China and the North China Plain between 2009 and 2016.Extensive data preprocessing and manual annotation are performed to prepare the training dataset.The optimized model achieves impressive performance on a test set of 604 samples,with a detection probability of 93.21%,a false alarm rate of 3.60%,a missed alarm rate of 6.79%,and a critical success index of 90.08%.The algorithm demonstrates robust identification capabilities across gust fronts of varying scales,types,and parent thunderstorm systems,highlighting its operational applicability.
基金The National Science Foundation of China under contract Nos 41071250 and 41371378the Innovation Projects of the State Key Laboratory of Resource and Environment Information System,Chinese Academy of Sciences,under contract No.088RA500TA
文摘Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea（SCS）. However, very few studies attempt to examine eddies＇ internal evolution processes. In this study, we reported a hybrid method to trace eddies＇ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies（DCEs） and the three long-lived anticyclonic eddies（ACEs） in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.
基金Supported by the National Natural Science Foundation of China(42030610)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at the surface weather stations in the Pearl River Delta(PRD)during five warm seasons,the properties and environmental conditions of the EXHP-associated MVs are analyzed.Further,the spatiotemporal distributions of the MV,instantaneous rain rate,and EXHP are illustrated for three events with the most abundant EXHP records.The major findings are as follows.About 42%EXHP records are accompanied by 57 MVs,including 84%of weak shear intensity,12%of weak mesocyclone intensity,and 4%of moderate mesocyclone intensity,with the rotational speeds between 5 and12 m s^(-1),12 and 16 m s^(-1),and 16 and 21 m s^(-1),respectively.The duration and core depth of the MVs are highly correlated(coefficient of 0.67)with averages of 39 min and 699 m,respectively.The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP,while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating.Relative to the EXHP events with MVs in the United States,those in the PRD feature smaller environmental 0-3-km storm relative helicity(SRH)and 0-1-km vertical wind shear(VWS).However,compared to the non-rotational EXHP in the PRD,the rotational EXHP events are associated with significantly higher 0-1-km VWS,0-3-km SRH,humidity,and larger convective available potential energy.In the three selected events,rainstorms exhibit an irregular shape,a quasi-circular shape,and a quasi-banded shape of strong echo,respectively.The MVs are often located inside the strong radar reflectivity region,and some are next to its bow-shaped portion.Those longer-lived MVs with stronger rotation are collocated with the extreme 6-min rainfall accumulation(≥10 mm)in space and time,suggesting presence of positive feedback between low-level rotation and short-term rain rate.In the event influenced by a tropical storm,four MVs appear at almost the same location in succession and move along roughly the same path,forming an MV back-building process.
基金supported by the National Key Basic Research Program of China(973 Program)under Grant No.2014CB845301the National Natural Science Foundation of China under Grant No.61227902
文摘Traditional control does not pay much attention to information security problems in system identification enough, which are important in practical applications. This paper focuses on the security problem of input information in a class of system identification problems with noise and binary-valued observations, presents a cryptography based security protocol, and improves it in the range of allowed errors. During solving the identification problem, the improved security protocol can ensure that the input information is not leaked, and thus, can deal with passive attacks effectively. Besides, a quantitative relationship among the input information, the public key in encryption and the number of partieipailts in the improved protocol is shown. Finally, the simulation results show that, the identification algorithm can still achieve the estimation accuracy by adding the improved security protocol. However, compared with the original identification algorithm, the time complexity of the algorithm with the improved security protocol increases.
文摘The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-area 4-generator and New England-New York 68-bus 16-generator systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and inter-area modes, and indicate the improvements in oscillation damping and power system stability.
