This paper designs an indirect power control method for brushless doubly fed induction generator (BDFIG), in which the stator is attached to grid with back-to-back space vector modulation (SVM) converter that converts the generated wind power. Our control method is a sliding mode control based on the theory of variable structure control. Specifically, the active and reactive powers, which are exchanged between the stator of the BDFIG and the grid in a linear and decoupled manner, are subjected to decoupled, vector control. In addition, a proportional integral (PI) controller was implemented to keep the DC-voltage constant for the back-to-back SVM converter. The efficiency of our control strategy was validated through simulation. The research greatly promotes the control of renewable energy generators.

This paper designs an indirect power control method for brushless doubly fed induction generator (BDFIG), in which the stator is attached to grid with back-to-back space vector modulation (SVM) converter that converts the generated wind power. Our control method is a sliding mode control based on the theory of variable structure control. Specifically, the active and reactive powers, which are exchanged between the stator of the BDFIG and the grid in a linear and decoupled manner, are subjected to decoupled, vector control. In addition, a proportional integral (PI) controller was implemented to keep the DC-voltage constant for the back-to-back SVM converter. The efficiency of our control strategy was validated through simulation. The research greatly promotes the control of renewable energy generators.

This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.

This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.

This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.