KEYWORDS: Local area networks, Design and modelling, Network security, Data transmission, Databases, Computer security, Telecommunication networks, Information security, Data storage, Control systems
In order to ensure the normal operation of the local area network (LAN), maintain the stability, accuracy and security of data transmission, it is necessary to monitor its lines in real time. Aiming at the current LAN network management without line interruption automatic monitoring function, a LAN line interruption monitoring module is designed. It realizes automatic monitoring of line interruption and timely alarm. The functional requirements of the LAN line interrupt monitoring module are analyzed in this paper. The design concept, implementation process, registration client, database, page, security policy and other design processes of the LAN line interrupt monitoring module are introduced in detail. The application and validation are given. It can be seen through the application that the module has good practical application value. It has important practical significance and guidance for network managers.
Segment the rocket target from the live image has a wide range of application scenarios in the space launch. Aiming at the problem of determining the optimal threshold in the traditional image segmentation, this paper proposed a method to determine the optimal threshold and then carry out image segmentation based on genetic algorithm. And the image segmentation result obviously enhanced, but there are many judgment and large amount of code, which need to be further optimized in the follow-up research.
The semantic segmentation technology of remote sensing image refers to labeling the semantic information of pixel-level of the image to complete the classification, namely, terrain classification. It is widely used in intelligent maps, smart cities and other aspects. With the increase of satellite image resolution and the development video communication moonlet, its application and scenes are greatly broadened. The traditional remote sensing image semantic segmentation method mainly uses statistical machine learning methods, which cannot take into account the spectral features and the context semantic relationship of pixels, and has a bottleneck in improving the accuracy of classification. In deep learning method, Using the convolutional neural networks to extract features can achieve classification results of remote sensing images with higher classification accuracy. Aiming at the problems of existing deep learning methods in multi-spectral image semantic segmentation, to make full use of the information of multi-spectral images, this paper proposes a semantic segmentation algorithm for multispectral remote sensing images based on deep learning and verified the method on open data sets.
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