Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

Zhang, Tianhao; Shen, Huanfeng; Xia, Xuezhi; Wang, Lunche; Mao, Feiyue; Yuan, Qiangqiang; Gu, Yu; Zhao, Bin; Zhongmin, Zhu; Lin, Yuanhua; Bo, Yanchen; Gong, Wei

doi:10.1109/tgrs.2023.3262785

Cited by 3 publications

(2 citation statements)

References 87 publications

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“…Yang, X. et al [22] established a maximum entropy rule selection model by using the boundary word information of non-terminators. Zhang, T. et al [23] used the maximum entropy model to integrate lexicalization features such as location information into the hierarchical phrase model. Shirani, K. et al [24] use the number and types of non-terminators to classify hierarchical phrase rules and solve the problem of rule selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Lu,

Yin

2024

Frontiers in Artificial Intelligence and Applications

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In order to improve the quality of Japanese-Chinese machine translation, the research on improving the quality of Japanese-Chinese machine translation based on deep learning is put forward. The maximum entropy model is used to effectively classify Japanese possible states and passive states, and then the features of Japanese possible states and passive states are effectively integrated into the log-linear model to improve the translation model, so as to improve the accuracy of selecting possible states and passive states in translation rules. The experimental results show that this method can effectively improve the translation quality of Japanese possible sentences and passive sentences. On large-scale Japanese Chinese materials, the maximum translation BLEU value can be increased from 41.50 to 42.01, and the overall intelligibility of translation results has been improved by 2.71% in manual evaluation. Conclusion: This method can improve the accuracy of rule selection.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Lu,

Yin

2024

Frontiers in Artificial Intelligence and Applications

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“…Compared to UAVs [2], [3], Low Earth Orbit (LEO) remote-sensing satellites have a broader vision and can achieve stable global coverage with not much difference in latency. Besides, the observation data from sun-synchronous orbits have similar time and angle conditions for the same Earth region, which has unique advantages in data correlation analysis [4]. However, the traditional remotesensing systems can only transmit data in the visible range of ground stations, which have been unable to meet the increasing demand for real-time and a mass of observation data [5].…”

Section: Introductionmentioning

confidence: 99%

Task-Aware Distributed Inter-Layer Topology Optimization Method in Resource-Limited LEO-LEO Satellite Networks

Guo,

Liu,

Zhong

2024

Preprint

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Due to the increasing demand for real-time observation data, remote-sensing satellites can be considered as users to access mega communication constellations, which can constitute a Low Earth Orbit (LEO) Double-Layered Satellite Network (DLSN) composed of Remote-sensing Layer Satellites (RLSs) and Communication Layer Satellites (CLSs). In this LEO DLSN, a Distributed Inter-Layer Topology Optimization (DITO) method is proposed based on time-space relationships between two layers, which can maximize task-aware observation benefits with limited onboard resources. For practical purposes, we take link switching interval constraints into account for the recapture and track of laser transceivers. Besides, we put forward a new time-slot division method based on the link switching interval for the linear modeling of the time-sequential coupling problem. With the simulation results in four constellation systems, the proposed distributed interactive method can obtain an optimal solution close to the centralized global solution with much less time. Simulation results indicate that the proposed method can support onboard optimization for future space networks.

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Task-Aware Distributed Inter-Layer Topology Optimization Method in Resource-Limited LEO-LEO Satellite Networks

Guo,

Liu,

Zhong

2024

IEEE Trans. Wireless Commun.

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Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

Cited by 3 publications

References 87 publications

Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Task-Aware Distributed Inter-Layer Topology Optimization Method in Resource-Limited LEO-LEO Satellite Networks

Task-Aware Distributed Inter-Layer Topology Optimization Method in Resource-Limited LEO-LEO Satellite Networks

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