An improved half-vehicle model has been proposed for active suspension control systems, in contrast to existing models, it allows to explore the nature of the effect of vehicle speed changes by introducing a state vec...An improved half-vehicle model has been proposed for active suspension control systems, in contrast to existing models, it allows to explore the nature of the effect of vehicle speed changes by introducing a state vector of vehicle pitch angle. Three control strategies of linear quadratic control （LQ）, improved LQ （ILQ） and wheelbase preview LQ （WLQ） have been implemented into the proposed model. ILQ has integrated an additional control parameter into LQ by concerning the correlation between acceleration values and their corresponding pitch angles. Simulation results have showed the effectiveness of the proposed model in terms of LQ, ILQ and WLQ control strategies.展开更多
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee...Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.展开更多
文摘An improved half-vehicle model has been proposed for active suspension control systems, in contrast to existing models, it allows to explore the nature of the effect of vehicle speed changes by introducing a state vector of vehicle pitch angle. Three control strategies of linear quadratic control （LQ）, improved LQ （ILQ） and wheelbase preview LQ （WLQ） have been implemented into the proposed model. ILQ has integrated an additional control parameter into LQ by concerning the correlation between acceleration values and their corresponding pitch angles. Simulation results have showed the effectiveness of the proposed model in terms of LQ, ILQ and WLQ control strategies.
基金This work was supported by Liaoning Provincial Science Public Welfare Research Fund Project(No.2016002006)Liaoning Provincial Department of Education Scientific Research Service Local Project(No.L201708).
文摘Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.
