This paper presents a novel method for optimizing meteorological sensor placement over large geographic areas, leveraging genetic algorithm (GA) and classified Light Detection And Ranging (LiDAR) data. The airborne Li...
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Cloud computing offers benefits in the digital world, such as scalability and agility, but securing these environments requires a high level of knowledge and have the potential for misconfiguration. While cloud platfo...
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Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will b...
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Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean *** extensive research has been conducted into underwater image enhancement,little of w...
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Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean *** extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater *** this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based *** enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light ***,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation *** experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.
The SM4 algorithm is now widely used to ensure the security of data transmission, but its physical implementation is still vulnerable to side channel attack. By studying the structure of SM4 algorithm, this paper prop...
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We introduce data-driven, scalable digital twins (DTs) and real-time data imputation to improve inverter synchronization in low-inertia microgrids. The DTs act as cyber-physical replicas, enabling real-time monitoring...
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Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms ...
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Considering the advancements in autonomous driving technologies, the necessity for an advanced driver assistance system (ADAS) to incorporate a multitude of sensors for enhanced precision has become paramount. Consequ...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-de...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs. IEEE
single-inductor multiple-output (SIMO) boost converter faces an issuce of mutual interference and cross-regulation among output voltages. This paper proposes a ripple-based non-cross-regulation controller suitable for...
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