The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced usin...The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.展开更多
为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进...为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进模型,通过自适应采样机制、MQTT 5.0和深度学习算法实现电力设备状态的实时监测与智能诊断。实验结果表明,该系统在有效采样数据占比、平均通信时延、数据丢包率、平均诊断准确率以及整体能耗5项关键性能指标上均显著优于传统数据采集与监控(Supervisory Control And Data Acquisition,SCADA)系统,为智能电网建设提供可靠的技术支撑。展开更多
基金This research was supported by the Science and Technology Department of Jilin Province[20210202128NC http://kjt.jl.gov.cn]The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03 http://www.most.gov.cn]+1 种基金the Jilin Province Development and Reform Commission[2019C021 http://jldrc.jl.gov.cn]the Science and Technology Bureau of Changchun City[21ZGN27 http://kjj.changchun.gov.cn].
文摘The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.
文摘为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进模型,通过自适应采样机制、MQTT 5.0和深度学习算法实现电力设备状态的实时监测与智能诊断。实验结果表明,该系统在有效采样数据占比、平均通信时延、数据丢包率、平均诊断准确率以及整体能耗5项关键性能指标上均显著优于传统数据采集与监控(Supervisory Control And Data Acquisition,SCADA)系统,为智能电网建设提供可靠的技术支撑。
