For the firefly algorithm in the solution of complex functions are prone to low accuracy and "premature" phenomenon. In this paper, we propose a tolerance-based elite mutation firefly algorithm (MOFA-TEM). T...
详细信息
We investigate the wavefront shaping technique to improve the transmission distance of VCSEL-MMF optical links. Impacts of the number of sub-blocks and phase accuracy of wavefront shaping on the improving performance ...
详细信息
Low-rate Distributed Denial-of-Service attacks, abbreviated as LDDoS, are experiencing an explosive and continuous growth in recent years. Meanwhile, people worked hard for making great contributions to prevent LDDoS ...
详细信息
Federated learning (FL) trains a model collaboratively but is susceptible to backdoor attacks for its privacy-preserving nature. Existing defenses against backdoor attacks in FL always make specific assumptions on dat...
详细信息
Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations r...
详细信息
Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE
With the increase of demand side flexible load and the development of new energy technology, the power transaction of distribution network is gradually improved. This paper considers the application of blockchain tech...
详细信息
The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. Howeve...
Endmember extraction is the crucial process of hyperspectral unmixing. In this study, we introduce a nonlinear endmember extraction algorithm for hyperspectral remote sensing image analysis. The algorithm use the mani...
详细信息
Renewable energy is a green and low-carbon energy source, which is of great significance for improving energy structure, protecting the ecological environment, addressing climate change, and achieving sustainable deve...
详细信息
For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is...
详细信息
For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is *** is to continuously design algorithms with better *** this end, reinforcement learning (RL) has demonstrated great success in ***, little is known on the other direction: whether RL helps explore how hard an online problem *** this paper, we study a generalized model of OBM, named online matching with stochastic rewards (OMSR, FOCS 2012), for which the optimal competitive ratio is still *** adopt an adversarial RL approach that trains two RL agents adversarially and iteratively: the algorithm agent learns for algorithms with larger competitive ratios, while the adversarial agent learns to produce a family of hard *** such a framework, agents converge at the end with a robust algorithm, which empirically outperforms the state of the art (STOC 2020).Much more significantly, it allows to track how the hard instances are *** succeed in distilling two structural properties from the learned graph patterns, which remarkably reduce the action space, and further enable theoretical improvement on the best-known hardness result of OMSR, from 0.621 (FOCS 2012) to *** the best of our knowledge, this gives the first evidence that RL can help enhance the theoretical understanding of an online problem. Copyright 2024 by the author(s)
暂无评论