Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Hongxing Peng"'
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-6 (2024)
Abstract Background Observational studies have suggested a suspected association between gastroesophageal reflux disease (GERD) and respiratory diseases, but the causality remains equivocal. The goal of this study was to evaluate the causal role of G
Externí odkaz:
https://doaj.org/article/e3ac3f838fd944b1aeacd0be326041e4
Publikováno v:
Agronomy, Vol 14, Iss 6, p 1334 (2024)
This paper proposes PestNet, a lightweight method for classifying crop pests, which improves upon MobileNet-V2 to address the high model complexity and low classification accuracy commonly found in pest classification research. Firstly, the training
Externí odkaz:
https://doaj.org/article/78932e090bdf478abab967dca04386d4
Autor:
Hongxing Peng, Zhifa Tian, Xingwang Li, Weizheng Wang, Galymzhan Nauryzbayev, Khaled Rabie, Thippa Reddy Gadekallu
Publikováno v:
Alexandria Engineering Journal, Vol 67, Iss , Pp 39-49 (2023)
This paper investigates the covert communication of cooperative non-orthogonal multiple access (NOMA) systems, where the near user serves as a decode-and-forward (DF) relay and the far user receives the covert information from both the source and the
Externí odkaz:
https://doaj.org/article/3f4d1144637a46729137c5495fa215cd
Autor:
Shuling Zhang, Xiaoguang Li, Haili Ma, Mengpei Zhu, Yuequan Zhou, Qianqian Zhang, Hongxing Peng
Publikováno v:
COPD, Vol 19, Iss 1, Pp 353-364 (2022)
We aimed to explore the role of antithrombin III (AT-III) activity in diagnosing patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and chronic bronchitis, and its relationship with all-cause mortality of AECOPD patien
Externí odkaz:
https://doaj.org/article/b2c4d3504bb64610ac90ed5475db796c
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 12, Pp 13582-13606 (2022)
Red imported fire ants (RIFA) are an alien invasive pest that can cause serious ecosystem damage. Timely detection, location and elimination of RIFA nests can further control the spread of RIFA. In order to accurately locate the RIFA nests, this pape
Externí odkaz:
https://doaj.org/article/88be0c1c96004a07af0efe2234fd39fe
Publikováno v:
Engenharia Agrícola, Vol 43, Iss 2 (2023)
ABSTRACT In China, low levels of accuracy in predicting when pineapple crops will reach maturity can result from environmental variation such as light changes, fruit overlap, and shading. Therefore, this study proposed the use of an improved RetinaNe
Externí odkaz:
https://doaj.org/article/7a634d264a144b088485bd565f8595e0
Autor:
Hongxing Peng, Huiming Xu, Zongmei Gao, Zhiyan Zhou, Xingguo Tian, Qianting Deng, Huijun He, Chunlong Xian
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
IntroductionCrop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.MethodsTo address the lack of data set and poor classification accura
Externí odkaz:
https://doaj.org/article/ad26be7a2973414ab7d6ab8a0ebd4a93
Autor:
Xingwang Li, Qunshu Wang, Hongxing Peng, Hui Zhang, Dinh-Thuan Do, Khaled M. Rabie, Rupak Kharel, Charles C. Cavalcante
Publikováno v:
IEEE Access, Vol 8, Pp 13329-13340 (2020)
In this paper, we propose a unified framework for hybrid satellite/unmanned aerial vehicle (HSUAV) terrestrial non-orthogonal multiple access (NOMA) networks, where satellite aims to communicate with ground users with the aid of a decode-forward (DF)
Externí odkaz:
https://doaj.org/article/d852b0683c564be3b59d108dd3fd05be
Publikováno v:
IEEE Access, Vol 8, Pp 164546-164555 (2020)
Litchi is often harvested by clamping and cutting the branches, which are small and can easily be damaged by the picking robot. Therefore, the detection of litchi branches is particularly significant. In this article, an fully convolutional neural ne
Externí odkaz:
https://doaj.org/article/247bc8c866e14be5b2cbe892861382c8
Autor:
Guantao Xuan, Chong Gao, Yuanyuan Shao, Meng Zhang, Yongxian Wang, Jingrun Zhong, Qingguo Li, Hongxing Peng
Publikováno v:
IEEE Access, Vol 8, Pp 216772-216780 (2020)
It is a challenging problem to detect the apple in natural environment using traditional object recognition algorithms due to occlusion, fluctuating illumination and complex backgrounds. Deep learning methods for object detection make impressive prog
Externí odkaz:
https://doaj.org/article/ca1fd27c2bbd4d6db29082f411a70ea5