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Vestnik Tomskogo Gosudarstvennogo Universiteta-Upravlenie Vychislitelnaja Tehnika i Informatika-Tomsk State University Journal of Control and Computer Science最新文献

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Asymptotical analysis of queueing system MMPP|M|N with feedback 带反馈的排队系统MMPP|M|N的渐近分析
A. Nazarov, E. Pavlova
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引用次数: 0
Automated detection of COVID-19 coronavirus infection based on analysis of chest X-ray images by deep learning methods 基于胸部x线图像分析的深度学习方法自动检测COVID-19冠状病毒感染
Evgenii Yu. Shchetinin, L. A. Sevastyanov
Early detection of COVID-19 infected patients is essential to ensure adequate treatment and reduce the load on the healthcare systems. One of effective methods for detecting COVID-19 is deep learning models of chest X-ray images. They can detect the changes caused by COVID-19 even in asymptomatic patients, so they have great potential as auxiliary systems for diagnostics or screening tools. This paper proposed a methodology consisting of the stage of pre-processing of X-ray images, augmentation and classification using deep convolutional neural networksXception, InceptionResNetV2, MobileNetV2, DenseNet121, ResNet50 and VGG16, previously trained on thelmageNet dataset. Next, they fine-tuned and trained on prepared data set of chest X-rays images. The results of computer experiments showed that theVGG16 model with fine tuning of the parameters demonstrated the best performance in the classification of COVID-19 with accuracy 99,09%, recall=98,318%, precision=99,08% and f1_score=98,78. This signifies the performance of proposed fine-tuned deep learning models for COVID-19 detection on chest X-ray images.
早期发现COVID-19感染患者对于确保适当治疗和减轻卫生保健系统的负担至关重要。胸部x线图像的深度学习模型是检测COVID-19的有效方法之一。即使在无症状患者中,它们也能检测到COVID-19引起的变化,因此它们作为诊断或筛查工具的辅助系统具有很大潜力。本文提出了一种方法,包括x射线图像预处理,增强和分类阶段,使用深度卷积神经网络seption, InceptionResNetV2, MobileNetV2, DenseNet121, ResNet50和VGG16,之前在magenet数据集上训练。接下来,他们对准备好的胸部x光图像数据集进行微调和训练。计算机实验结果表明,参数微调后的vgg16模型在COVID-19分类中表现最佳,准确率为99,09%,召回率为98,318%,精度为99,08%,f1_score=98,78。这表明所提出的用于COVID-19胸部x射线图像检测的微调深度学习模型的性能。
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引用次数: 0
Queuing systems with heterogeneous servers and state-dependent jump priorities informatics and programming 具有异构服务器和状态依赖跳转优先级的排队系统的信息学和编程
A. Melikov, E. V. Mekhbaliyeva
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引用次数: 0
The probabilistic model of sharing system with collisions, H-persistence and rejections data processing 具有碰撞、h -持久性和拒绝数据处理的共享系统概率模型
Anna V. Polkhovskaya, et al.
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引用次数: 0
Implementation of the deviation control principle in models of mechanical engineering production based on Petri nets 基于Petri网的机械工程生产模型偏差控制原理的实现
Aleksey N. Sochnev
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引用次数: 0
Application of the finite element grouping procedure to improve the efficiency of unsteady multiphase flow simulation in high-heterogeneous 3D porous media 应用有限元分组法提高高非均质三维多孔介质非定常多相流模拟效率
M. Persova, Y. Soloveichik, I. I. Patrushev, Anastasia S. Ovchinnikova
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引用次数: 0
Memory Yury I. Paraev 回忆尤里·i·帕拉耶夫
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引用次数: 0
The effectiveness of forward error correction in transport protocol at the intrasegment level 在段内层传输协议中前向纠错的有效性
P. Pristupa, P. Mikheev, Vasiliy V. Poddubnyy, S. Suschenko
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引用次数: 0
Asymptotic-diffusion analysis of retrial queue with two-way communication and unreliable server 具有双向通信且服务器不可靠的重审队列的渐近扩散分析
A. Nazarov, S. Paul, Olga Lizyura, Ksenia Shulgina
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引用次数: 0
Control of a robot manipulator in conditions of uncertainty 不确定条件下机械臂的控制
Yu. I. Paraev, S. Kolesnikova, S. Tsvetnitskaya
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引用次数: 0
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Vestnik Tomskogo Gosudarstvennogo Universiteta-Upravlenie Vychislitelnaja Tehnika i Informatika-Tomsk State University Journal of Control and Computer Science
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