Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface *** safeguard this sensitive data,image encryption technology is *** this paper,a n...
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Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface *** safeguard this sensitive data,image encryption technology is *** this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption *** encryption algorithm tailored for handling the multi-band attributes of remote sensing images is *** algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple ***,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing *** results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.
With the rapid development of Chinese urbanization and industrialization level, the Chinese real estate transaction area continues to grow, and the country's decoration industry is expanding. Due to the rapid expa...
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In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epid...
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At the intersection of personality psychology, computer science, and linguistics, more and more researchers are paying attention to personality detection based on content analysis of texts on social media. However, ex...
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Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support stud...
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Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As modelbased scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. T
Aiming at the problem of large error and long time of traditional dangerous driving behavior recognition, this paper designs a dangerous driving behavior recognition method based on Apriori algorithm in rush hour. Fir...
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The oscillatory movement, aerodynamic damping effect, gyroscopic effect, and other phenomena in floating wind turbines result in distinct response characteristics of output and load under varying wind-wave coupling sc...
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Aiming at the problems of abnormal points and noise points existing in the original high-dimensional data of wind turbine system, and considering the strict requirements of fault warning on the accuracy of prediction ...
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Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequenc...
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In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable ...
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In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,
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