As an essential part of DC-Link in the power converter,capacitor plays a crucial role in absorbing ripple current and suppressing ripple voltage.The health and residual service life of the DC-Link capacitor is one of ...As an essential part of DC-Link in the power converter,capacitor plays a crucial role in absorbing ripple current and suppressing ripple voltage.The health and residual service life of the DC-Link capacitor is one of the decisive factors for the safety,stability,and efficiency of the system in which it is located.Aiming at the shortcomings of existing methods,such as low dynamic sensitivity of data update and fluctuation of identification results,a capacitor state identification method based on improved RLS is proposed in this paper.The proposed method is optimized by introducing the forgetting factor algorithm and root means square algorithm to modify the iterative formula and final identification results.Compared with existing methods,this method can identify the capacitor’s current state in real time and accurately.Finally,we successfully verified the accuracy,robustness,and adaptability of the proposed method by a series of experimental tests on a dSPACE platform.展开更多
Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing ...Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.展开更多
Identification of steady state and transient state plays an important role in modeling,control,optimiza-tion,and fault detection of industrial processes.Many existing methods for state identification are not satisfact...Identification of steady state and transient state plays an important role in modeling,control,optimiza-tion,and fault detection of industrial processes.Many existing methods for state identification are not satisfactory in practical applications due to problems of ideal hypothesis,too many parameters,and poor robustness.In this paper,a novel state identification approach is proposed.The problem of state identification is transformed into finding the noise band of differential signal.For practical application,automatic selection of noise band amplitude is proposed to make the method convenient to be used.Problems of gross errors,low signal-to-noise ratio and online identification are considered.And comparison with other two methods shows that the proposed method has better identification performance.Simulations and experiments also prove the effectiveness and practicability of the proposed method.展开更多
A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model wi...A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.展开更多
基金Natural Science Foundation of Hunan Province(2020JJ5757).
文摘As an essential part of DC-Link in the power converter,capacitor plays a crucial role in absorbing ripple current and suppressing ripple voltage.The health and residual service life of the DC-Link capacitor is one of the decisive factors for the safety,stability,and efficiency of the system in which it is located.Aiming at the shortcomings of existing methods,such as low dynamic sensitivity of data update and fluctuation of identification results,a capacitor state identification method based on improved RLS is proposed in this paper.The proposed method is optimized by introducing the forgetting factor algorithm and root means square algorithm to modify the iterative formula and final identification results.Compared with existing methods,this method can identify the capacitor’s current state in real time and accurately.Finally,we successfully verified the accuracy,robustness,and adaptability of the proposed method by a series of experimental tests on a dSPACE platform.
基金Supported by the Public Welfare Technology Application Research Project of China(No.LGN21C190009)the Science and Technology Project of Zhoushan Municipality,Zhejiang Province(No.2022C41003)。
文摘Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.
文摘Identification of steady state and transient state plays an important role in modeling,control,optimiza-tion,and fault detection of industrial processes.Many existing methods for state identification are not satisfactory in practical applications due to problems of ideal hypothesis,too many parameters,and poor robustness.In this paper,a novel state identification approach is proposed.The problem of state identification is transformed into finding the noise band of differential signal.For practical application,automatic selection of noise band amplitude is proposed to make the method convenient to be used.Problems of gross errors,low signal-to-noise ratio and online identification are considered.And comparison with other two methods shows that the proposed method has better identification performance.Simulations and experiments also prove the effectiveness and practicability of the proposed method.
基金Project (Nos.50775096 and 51075176) supported by the National Natural Science Foundation of China
文摘A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.
