The Autonomous and controllable of the spacecraft component is of great importance for the development of our space industry. The key step of the spacecraft component is the production. Many researchers focus on the e...
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This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep n...
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As the development of Internet of things (IOT), massive sensors have been deployed as the public infrastructure. With the development of in-depth applications in IOT, service discovery and composition are challenges t...
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As the development of Internet of things (IOT), massive sensors have been deployed as the public infrastructure. With the development of in-depth applications in IOT, service discovery and composition are challenges to the end users. To handle this challenge, we develop a service mining scheme based on semantic for IOT to provide users with interesting composite services. In this scheme, services can be combined and recommended to users actively according to the calculation of service similarity and an updatable semantic database. By the results of service similarity, useless compositions can be filtered out so that energy consumption on service flooding will be reduced. The update strategy of semantic database is also given out, by which the composite services can keep up with time and be more applicative. The benefits of the proposed method are that all operations such as calculating, filtering, and updating are simple enough to be performed in sensor networks.
To enhance the convergence capability of grey wolf optimizer (GWO), this research investigates an evolved GWO using weighted-leader strategy (WLS), namely WLSGWO. The key issue of WLS is realizing the adaptive adjustm...
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Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing si...
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For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical i...
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Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not ad...
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The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static envir...
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The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.
Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphe...
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In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its ...
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ISBN:
(纸本)9798400709753
In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. Therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. The process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. Then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions.
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