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  1. 52301

    Semantic communication aware reinforcement learning for communication fault-tolerant UAV collaborative control by ZHANG Yang, GU Hongyu, FENG Bohao, WANG Ran

    Published 2024-04-01
    “…This interference can prevent UAV from accurately transmitting and receiving information, thereby jeopardizing the success of collaborative missions To address this challenge, a fault-tolerant UAV collaboration method grounded in reinforcement learning and semantic communication was developed to cater to the leader-follower UAV mission pattern within environments constrained by limited communication capabilities To enhance the follower UAV's strategy for reinforcement learning-based following, a semantic communication mechanism coupled with a Proximal Policy Optimization (PPO) method was implemented. …”
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  2. 52302

    Effect of Nape Acupuncture Combined with Swallowing Training on Dysphagia in Patients with Parkinson's Disease by Linjing WANG, Xue WANG, Jiyao ZHANG, Jiashuai LI, Xinlei HOU, Luwen ZHU

    Published 2020-06-01
    “…Objective:To explore the effect of acupuncture combined with swallowing training in patients with Parkinson's disease with swallowing dysfunction.Methods:From August 2017 to August 2019, a total of 120 patients with Parkinson's dysphagia with gradesⅡtoⅣof the"Kubota Water Swallowing Test"were selected and randomly divided into treatment group (<italic>n</italic>=60) and control group (<italic>n</italic>=60). …”
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  3. 52303
  4. 52304

    Research on the robustness of convolutional neural networks in image recognition by Dian LIN, Li PAN, Ping YI

    Published 2022-06-01
    “…Convolutional neural network is one of the key technologies in the application of image recognition and processing in artificial intelligence.Its wide application makes researches on its robustness more and more important.Previous researches on robustness of neural networks were too sweeping and most of them focused on adversarial robustness, which causes difficulty in further study in the mechanism of neural network robustness.The related researches of neuroscience were introduced and the concept of visual robustness was put forward.By studying the similarity and difference between neural network models and human visual system, the internal mechanism and faults of neural network robustness were revealed.The researches of neural network robustness in recent years were reviewed, and the reasons for the lack of robustness of neural network models were analyzed.The lack of robustness of neural networks is reflected in their sensitivity to small perturbations.The reason is that neural networks tend to learn high-frequency information for calculation and inference, which is difficult for humans to recognize.High-frequency information is easily affected by perturbations, and eventually causes mistakes of models.Previous researches on robustness mostly focused on mathematical properties of models, and were limited in the natural faults of neural networks.Visual robustness extends the traditional concept of robustness.The traditional concept of robustness measures the discrimination ability of models for distorted image examples.Distorted examples and clean examples can get correct outputs through robust models.Visual robustness measures the consistency between models and humans in discrimination ability.Visual robustness combines the research methods and achievements of neuroscience and psychology with artificial intelligence.The development of neuroscience in the field of vision were reviewed, and the application of research methods of cognitive psychology in neural network robustness were discussed.Human visual system has advantages in learning and abstract ability, whill neural network models have better performance in calculation speed and memory.The difference between the physiological structure of human brain and the logical structure of neural network models is the key factor leading to the problem of robustness of neural networks.The research of visual robustness requires deeper understanding of human visual system.Revealing the differences in cognitive mechanism between human visual system and neural network models and effectively improving the algorithm are the development trends of neural network robustness and even artificial intelligence.…”
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  5. 52305
  6. 52306

    Water Quality Evaluation of the Middle and Lower Reaches of Hanjiang River Based on Comprehensive Water Quality Identification Index by MA Jingjiu, YU Ting, CHEN Yanfei, YAO Huaming, LI Yang

    Published 2020-01-01
    “…Moreover,NH<sub>3</sub>-N influences the water quality of Yujiahu section in the middle reaches greatly, and TP is the limiting factor for the comprehensive water quality of Luohanzha and Huangzhuang sections in the lower reaches.In terms of space, the water quality of Shenwan in the middle and lower reaches of Hanjiang River is the best,and that of Shilou is the worst.In terms of time,the overall water quality of the middle and lower reaches of Hanjiang River improved constantly from 2001 to 2014.The results can serve as theoretical basis for water pollution control in this basin.Although consistent results were obtained by all the evaluation methods,the comprehensive index method contains more information,which can fully present the number of over-standard items and the pollution degree of each section.Consequently,it is suggested to be widely popularized and applied.…”
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  10. 52310

    Spatial-temporal Evolution Rule Analysis of Runoff of Hanjiang River under Changing Environment by ZHONG Huayu, HUANG Qiang, YANG Yuanyuan, LIU Dengfeng, MING Bo, REN Kang

    Published 2020-01-01
    “…Based on the 48-year measureddaily runoff series of the hydrological stations of Yangxian, Huangjiagang and Huangzhuang in themain stream of the Hanjiang River, this paper comprehensively analyzes the spatial-temporalevolution trend, year of abrupt change, periodic variation, interannual variation and annualdistribution of runoff in Hanjiang River Basin via multi methods and multi dimensions. The resultsshow that the runoff consistency was broken as follows: ① The annual runoff from the upper,middle and lower reaches of the Hanjiang River decreased with the trend of -312 million m<sup>3</sup>/(10a),-1 478 million m<sup>3</sup>/(10a) and -593 million m<sup>3</sup>/(10a) respectively, and the downward trend continuedstrongly, but the monthly runoff of lower reaches of the Hanjang River was increased significantlyin January and February; ② The abrupt change of the Hanjiang River occurred in 1985 and 1990, andthe inter-annual variation characteristic values and the annual distribution characteristicindexes of these stations decreased slightly after the abrupt change; ③ The runoff series of theHanjiang River had three types of periodic changes: 40~48 a, 11a and 4~7 a; ④ The inter-annualrunoff variation coefficient of the Hanjiang River was between 0.28 and 0.46, and the extremeratio was between 2.72 and 7.73; ⑤ The maximum monthly average runoff of the three stations wasin July or September, and the minimum monthly average runoff was in December or February, and theannual distribution of runoff was concentrated in July to October, accounting for more than 50% ofthe annual runoff. …”
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