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| 1 | Novel dammarane-type glycosides from Gynostemma pentaphyllum 显示文摘 | Yin Feng Hu Lihong Pan Ruixiang | 2004 | Chem Pharm Bull2004，52，： | 1 |
| 2 | Chiral (6,3) Network Assembled by Lanthanide and Changeful Dihydroxyfumaric Acid显示文摘一系列 chiral 二维的 lanthanide 协作聚合物,[Ln2 (L) 3 (H2O ) 6 ] n 潲祸牴慩潺敬 s 醨? 醨? | Zhang,Manbo Lü,Jing Hu,Ruixiang | 2012 | Chinese Journal of Chemistry2012，30，2： | 0 |
| 3 | Measurement and influencing factors of urban traffic ecological resilience in developing countries: A case study of 31 Chinese cities显示文摘An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspective of system optimization,it is important to study the level of urban traffic ecological resilience and analyze its influencing factors.In this study,we evaluated traffic ecological resilience,characterized its spatio-temporal differentiation,and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018.By conducting Kernel density analysis,standard deviation ellipse,comprehensive weight determination,panel data regression analysis,andχ2test,we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of“decreasing first and then increasing”and the spatial characteristic of“high in the east,second in the middle,and low in the west”.The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast.Governance capability,market activity,technological innovation capability,opening degree,and financial resources had significant effects on urban traffic ecological resilience.Finally,we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world. | HU Xiwu SU Yunqing REN Kefeng SONG Fang XUE Ruixiang | 2021 | Regional Sustainability2021，2，3： | 1 |
| 4 | Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach显示文摘In ophthalmology,retinal optical coherence tomography(OCT)images with noticeable structural features help identify human eyes as healthy or diseased.The recently hot arti ficial intelligence(AI)realized this recognition process automatically.However,speckle noise in the original retinal OCT image reduces the accuracy of disease classi fication.This study presents a timesaving approach based on deep learning to improve classi fication accuracy by removing the noise from the original dataset.Firstly,four pre-trained convolutional neural networks(CNNs)from the ImageNet Large Scale Visual Recognition Challenge(ILSVRC)were trained to classify the original images into two categories:The noise reduction required(NRR)and the noise-free(NF)images.Among the CNNs,VGG19 BN performed best with 98%accuracy and 99%recall.Then,we used the block-matching and 3D filtering(BM3D)algorithm to denoise the NRR images.Those noise-removed NRR and the NF images form the processed dataset.The quality of images in the dataset is prominently ameliorated after denoising,which is valid to improve the models'performance.The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach.We have compared the CNNs,and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%,5.19%,and 5.10%under overall accuracy(OA),Macro F1-score,and Micro F1-score,respectively.Especially for DenseNet161,the OA is improved to 98.14%.Our proposed method demonstrates its effectiveness in improving classi fication accuracy and opens a new solution to reduce denoising time-consuming for large datasets. | Lantian Hu Ruixiang Guo Sifan Li Jing Cao Qian Liu | 2023 | Journal of Innovative Optical Health Sciences2023，16，6： | 0 |
| 5 | Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects显示文摘Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various applications.With the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature learning.This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years.Specifically,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation perspectives.For FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are summarized.In terms of training,we discuss ideas for enhancing model transferability.In terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed methods.We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology.Finally,based on the above comparisons and discussions,we present research prospects and future directions. | Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou | 2023 | Machine Intelligence Research2023，20，3： | 0 |
| 6 | Construction of the Chinese Veteran Clinical Research (CVCR) Platform for the assessment of non-communicable diseases显示文摘 | Tan Jiping Li Nan Gao Jing Guo Yuhe Hu Wei Yang Jinsheng Yu Baocheng Yu Jianmin Du Wei Zhang Wenjun Cui Lianqi Wang Qingsong Xia Xiangnan Li Jianjun Zhou Peiyi Zhang Baohe Liu Zhiying Zhang Shaogang Sun Lanying Liu Nan Deng Ruixiang Dai Wenguang Yi Fang Chen Wenjun Zhang Yongqing Xue Shenwu Cui Bo Zhao Yiming Wang Luning | 2014 | Chinese Medical Journal2014，，3： | 3 |
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