Research and implementation of non-linear management and monitoring system for classified information network

IF 2.4 Q2 ENGINEERING, MECHANICAL Nonlinear Engineering - Modeling and Application Pub Date : 2023-01-01 DOI:10.1515/nleng-2022-0254
Jun He
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Abstract

Abstract In order to better realize the secret-related information monitoring system, an algorithm based on a nonlinear network is proposed and is combined with the traditional algorithm. This article mainly analyzes the theory of nonlinear networks, designs and trains new network parameters according to their own needs, and combines the nonlinear network as a feature extractor with the existing intrusion detection and wandering detection algorithms, which greatly improves the recognition ability of traditional algorithms. The main feature of a nonlinear network is that it can extract the positional features of objects from the network while also extracting object features, that is, positioning and classification are realized in the same network. As a feature extractor, this network can not only have a higher recognition rate than background difference, hog, and other algorithms but also have a greater ability to extract position information than other convolutional neural networks. The successful application of nonlinear production network systems in TV stations at all levels has greatly improved the editing and production capability and efficiency of TV programs. How to ensure the safe, reliable, stable, orderly, and efficient operation of nonlinear production network systems requires vendors and TVS Taiwan technical staff to jointly conduct in-depth research and summarize their findings. In this article, from the perspective of TV users, information components in nonlinear production network systems are analyzed, including class, title management mode, storage space management, material management, security management, and workflow management in nonlinear systems. Make some analysis, discussion, and summaries of network system and operation management problems. The experimental results show that the nonlinear algorithm in this article has a significant advantage over the original tracking algorithm; that is, most tracking algorithms do not have the ability of category recognition during the initial tracking process, which means that these tracking algorithms cannot accurately know what they are tracking. Because the nonlinear network has the ability to output categories, whether it is initial tracking or tracking loss recovery, nonlinearity has fundamentally better advantages than other tracking algorithms. Therefore, it can be predicted that there is a strong recognition ability in the later monitoring and wandering detection. It has been proved that the nonlinear algorithm can be effectively applied to the secret information monitoring system.
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分类信息网络非线性管理与监控系统的研究与实现
摘要为了更好地实现涉密信息监控系统,提出了一种基于非线性网络的算法,并将其与传统算法相结合。本文主要分析了非线性网络的理论,根据自己的需要设计和训练新的网络参数,并将非线性网络作为特征提取器与现有的入侵检测和漫游检测算法相结合,大大提高了传统算法的识别能力。非线性网络的主要特点是可以在提取目标特征的同时,从网络中提取目标的位置特征,即在同一网络中实现定位和分类。作为特征提取器,该网络不仅具有比背景差分、hog等算法更高的识别率,而且具有比其他卷积神经网络更强的位置信息提取能力。非线性制作网络系统在各级电视台的成功应用,极大地提高了电视节目的编辑制作能力和效率。如何保证非线性生产网络系统安全、可靠、稳定、有序、高效地运行,需要厂商与TVS台湾技术人员共同进行深入研究,总结研究成果。本文从电视用户的角度,分析了非线性制作网络系统中的信息构成,包括非线性系统中的类别、标题管理模式、存储空间管理、素材管理、安全管理和工作流管理。对网络系统和运营管理问题进行分析、讨论和总结。实验结果表明,本文提出的非线性跟踪算法相对于原有的跟踪算法具有明显的优势;也就是说,大多数跟踪算法在最初的跟踪过程中不具备类别识别的能力,这意味着这些跟踪算法不能准确地知道自己在跟踪什么。由于非线性网络具有输出类别的能力,无论是初始跟踪还是跟踪损失恢复,非线性从根本上比其他跟踪算法具有更好的优势。因此,可以预见在后期的监测和流浪检测中有较强的识别能力。实验证明,该非线性算法可以有效地应用于保密信息监控系统。
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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