|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | An ECR-PCR rule for fusion of evidences defined on a non-exclusive framework of discernment显示文摘In the research of uncertain information processing,Dempster-Shafer Theory(DST)provides a framework for dealing with uncertain information,where evidence is defined on a Frame of Discernment(FOD)consisting of mutually exclusive elements.However,the requirement of exclusiveness on FOD sometimes is not satisfied,as shown in Dezert-Smarandache Theory(DSm T),a derivative of DST.In DSm T,the non-exclusiveness is expressed by propositions’intersection and the fusion of evidence is realized through a Proportional Conflict Redistribution(PCR)rule.In order to handle non-exclusive FODs,a new framework called D Number Theory(DNT)has been proposed recently,which quantifies the non-exclusive degree between propositions different from DSm T.In previous studies,an Exclusive Conflict Redistribution(ECR)rule has been designed in DNT to implement the fusion of evidence defined on a non-exclusive FOD,but there are some deficiencies in the ECR rule.In this paper,a new rule called ECR-PCR rule is proposed by combining the ECR and PCR rules to better implement the fusion of evidence defined on a nonexclusive FOD.Within the proposed rule,the definition of conflict utilizes the idea of ECR’s exclusive conflict,and the disposal of conflict is following the idea of PCR’s proportional redistribution.Properties of the ECR-PCR rule are presented.The effectiveness of the proposed new rule is verified through numerical examples and applications,in comparison with other fusion methods. | Xinyang DENG Yebi CUI Wen JIANG | 2022 | Chinese Journal of Aeronautics2022，35，8： | 2 |
| 2 | Assessment of E-Commerce security using AHP and evidential reasoning显示文摘 | Yajuan Zhang Xinyang Deng Daijun Wei Yong Deng | 2011 | Expert Systems With Applications2011，，3： | 2 |
| 3 | Unsupervised content-preserving transformation for optical microscopy显示文摘The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical research.However,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability.Here,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be applied.Through the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content.UTOM shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence labeling.Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities.We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging. | Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai | 2021 | Light(Science & Applications)2021，10，3： | 1 |
| 4 | Assessment of E-Commerce security using AHP and evidential reasoning显示文摘 | Yajuan Zhang Xinyang Deng Daijun Wei Yong Deng | 2011 | Expert Systems With Applications2011，，3： | 1 |
| 5 | Supplier selection using AHP methodology extended by D numbers 显示文摘 | Xinyang Deng Yong Hu Yong Deng Sankaran Mahadevan | 2014 | Expert Systems with Applications2014，41，1： | 1 |
| 6 | Parame- ter estimation based on interval-valued belief structures 显示文摘 | Deng Xinyang Hu Yong Chan F T S | 2015 | European Journal of Operational Research2015，241，2： | 1 |
| 7 | Identifying influential nodes in weighted networks based on evidence theory显示文摘 | Daijun Wei Xinyang Deng Xiaoge Zhang Yong Deng Sankaran Mahadevan | 2013 | Physica A: Statistical Mechanics and its Applications2013，，10： | 1 |
| 8 | Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques显示文摘SARS-CoV-2 infection causes complicated clinical manifestations with variable multi-organ injuries,how-ever,the underlying mechanism,in particular immune responses in different organs,remains elusive.In this study,comprehensive transcriptomic alterations of 14 tissues from rhesus macaque infected with SARS-CoV-2 were analyzed.Compared to normal controls,SARS-CoV-2 infection resulted in dysregulation of genes involving diverse functions in various examined tissues/organs,with drastic transcriptomic changes in cerebral cortex and right ventricle.Intriguingly,cerebral cortex exhibited a hyperinflammatory state evidenced by sig-nificant upregulation of inflammation response-related genes.Meanwhile,expressions of coagulation,angio-genesis and fibrosis factors were also up-regulated in cerebral cortex.Based on our findings,neuropilin 1(NRP1),a receptor of SARS-CoV-2,was significantly elevated in cerebral cortex post infection,accompanied by active immune response releasing inflammatory factors and signal transmission among tissues,which enhanced infection of the central nervous system(CNS)in a positive feedback way,leading to viral encephalitis.Overall,our study depicts a multi-tissue/organ tran-scriptomic landscapes of rhesus macaque with early infection of SARS-CoV-2,and provides important insights into the mechanistic basis for COVID-19-asso-ciated clinical complications. | Chun-Chun Gao Man Li Wei Deng Chun-Hui Ma Yu-Sheng Chen Yong-Qiao Sun Tingfu Du Qian-Lan Liu Wen-Jie Li Bing Zhang Lihong Sun Si-Meng Liu Fengli Li Feifei Qi Yajin Qu Xinyang Ge Jiangning Liu Peng Wang Yamei Niu Zhiyong Liang Yong-Liang Zhao Bo Huang Xiao-Zhong Peng Ying Yang Chuan Qin Wei-Min Tong Yun-Gui Yang | 2022 | Protein & Cell2022，13，12： | 1 |
| 9 | Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework显示文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention. | Yu ZHANG Fanghui HUANG Xinyang DENG Mingda LI Wen JIANG | 2024 | Chinese Journal of Aeronautics2024，37，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员