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计算机系统应用:2020,29(6):126-131
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基于改进CNN的部队门禁系统
(1.海军航空大学 岸防兵学院, 烟台 264001;2.91213部队, 烟台 264001)
Force Access Control System Based on Improved CNN
(1.Coarst Guard College, Naval Aeronautical University, Yantai 264001, China;2.Troops 91212, Yantai 264001, China)
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投稿时间:2019-11-13    修订日期:2019-12-09
中文摘要: 针对部队武器仓库等重要场所的门禁管理方式安全性较低等问题, 设计了基于改进卷积神经网络的门禁系统. 首先对卷积神经网络进行介绍, 引入PSO算法设计优化的卷积神经网络的初始权值以及阈值. 然后对手写数字数据集进行分类实验. 实验结果证明, 基于PSO算法的卷积神经网络改进方案能够使得训练过程收敛速度较快, 损失较小, 效果优于传统卷积神经网络. 在此基础上, 根据部队实际工作情况, 将粒子群算法应用于MTCNN以及孪生ResNet算法, 设计基于改进卷积神经网络的门禁系统, 使得部队重要场所的门禁管理具有更高的安全性和可靠性.
Abstract:The access control management methods for important places such as military weapons warehouses are insufficient in security. In order to solve the defects, we design an access control system based on improved convolutional neural network. This paper first introduces the basic knowledge of convolutional neural networks, then introduces Particle Swarm Optimization (PSO) algorithm to design and optimize initial weights and thresholds of convolutional neural networks. After designing, experiment with the MNIST handwritten digital dataset is carried out. The results demonstrate that the modified convolutional neural network can make the convergence speed faster, and the loss is smaller, so the outcome is obviously better than the traditional convolutional neural network. On this basis, according to the actual working conditions of the troops, PSO is applied in the MTCNN and SIAM-ResNet face detection algorithm, the access control system based on improved convolutional neural network is designed, which makes the access control of important places in the army have higher security and reliability.
文章编号:7453     中图分类号:    文献标志码:
基金项目:国家自然科学基金(51605487)
引用文本:
何伟鑫,邓建球,方轶,丛林虎,李俊达.基于改进CNN的部队门禁系统.计算机系统应用,2020,29(6):126-131
HE Wei-Xin,DENG Jian-Qiu,FANG Yi,CONG Lin-Hu,LI Jun-Da.Force Access Control System Based on Improved CNN.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):126-131

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