Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have ...Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.展开更多
针对无线射频识别(radio frequency identification,RFID)系统中的标签碰撞问题,在预先侦测四元查询树算法的基础上,提出一种改进的预先侦测查询树防碰撞算法。结合后退式搜索与八叉树询问机制,通过提取碰撞位信息来构建查询树。实验结...针对无线射频识别(radio frequency identification,RFID)系统中的标签碰撞问题,在预先侦测四元查询树算法的基础上,提出一种改进的预先侦测查询树防碰撞算法。结合后退式搜索与八叉树询问机制,通过提取碰撞位信息来构建查询树。实验结果表明,改进的算法在通信量、碰撞时期的标签数量、阅读器的查询次数以及系统的吞吐率方面都优于BBS、PDQT算法。展开更多
基金supported by the Liaoning Province Applied Basic Research Program Project of China(Grant:2023JH2/101300065)the Liaoning Province Science and Technology Plan Joint Fund(2023-MSLH-221).
文摘Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.
文摘针对无线射频识别(radio frequency identification,RFID)系统中的标签碰撞问题,在预先侦测四元查询树算法的基础上,提出一种改进的预先侦测查询树防碰撞算法。结合后退式搜索与八叉树询问机制,通过提取碰撞位信息来构建查询树。实验结果表明,改进的算法在通信量、碰撞时期的标签数量、阅读器的查询次数以及系统的吞吐率方面都优于BBS、PDQT算法。
