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Modeling and simulation of vehicle group lane-changing behaviors in upstream segment of ramp areas under a connected vehicle environment

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Smart and Resilient Transportation 2019年第1期1卷 17-29页

作者： Haijian Li Zhufei Huang Lingqiao Qin Shuo Zheng Yanfang Yang Beijing Engineering Research Center of Urban Transport Operation Guarantee Beijing University of Technology Beijing China Traffic Operations and Safety Laboratory University of Wisconsin Madison Madison Wisconsin USA School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport China Academy of Transportation Sciences Beijing China

Purpose The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas. Design/methodology/approach In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM. Findings The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas. Originality/value The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

关键词： A connected vehicle environment Ramp area Upstream segment Vehicle group lane-changing behavior

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Compact evolutionary algorithm based ontology meta-matching 1st

Compact evolutionary algorithm based ontology meta-matching

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017

作者： Xue, Xingsi Liu, Shijian College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China

ISBN: (纸本)9783319707297

Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of ontology entities to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers, so-called ontology meta-matching, is one of the main challenges in ontology matching domain. In recent years, Evolutionary Algorithm (EA) based ontology meta-matching technique has become the state-of-the-art methodology to solve the ontology meta-matching problem, but it suffers from some defects like the slow convergence, premature convergence and the huge memory consumption. To overcome these drawbacks, in this paper, a Compact EA (CEA) based ontology meta-matching technique is proposed, which makes use of a probabilistic representation of the population to perform the optimization process. In particular, we construct an optimal model for the ontology meta-matching problem, propose a problem-specific CEA to optimize the aggregating weights of various matchers, and utilize a Cross Sum Quality Measure (CSQM) to adaptively extract the final alignment. The experimental results show that our approach outperforms other EA based ontology matching techniques and Ontology Alignment Evaluation Initiative (OAEI 2016)’s participants. © Springer International Publishing AG 2018.

关键词： Ontology

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On privacy protection of latent dirichlet allocation model training

arXiv

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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Linear active disturbance rejection control of piezoelectric nanopositioning stage

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Kongzhi Lilun Yu Yingyong/Control Theory and Applications 2018年第11期35卷 1577-1590页

作者： Wei, Wei Xia, Peng-Fei Zuo, Min School of Computer and Information Engineering Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China

In piezoelectric driven nanopositioning systems, hysteresis severely degrades its positioning accuracy. A hysteresis inverse model based positioning approach is able to improve the accuracy. However, due to its large dependence on model, it cannot guarantee satisfied performance when internal and external disturbances exist. To address such problem, hysteresis is taken to be a kind of disturbance, a model independent control, i.e. active disturbance rejection control (ADRC), is designed. An extended state observer (ESO) is used to estimate hysteresis actively and compensate it by control signal in real time to ensure the positioning accuracy. The convergence of ESO and the stability of the closedloop system are analyzed. Relationship between control parameters of ADRC and the response of the system has been discussed. System response, integral of time-multiplied absolute-value of error (ITAE), mean absolute error (MAE) and root mean square error (RMSE) of PI control and ADRC have been compared. Both numerical and experimental results show that ADRC is superior to PI control. It confirms that ADRC can actively estimate hysteresis, internal uncertainties, and external disturbances. Those undesired issues can be cancelled before they reducing the positioning accuracy. Therefore, satisfied performance can be guaranteed. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

关键词： Piezoelectric actuators

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Few-shot deep adversarial learning for video-based person re-identification

arXiv

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arXiv 2019年

作者： Wu, Lin Wang, Yang Yin, Hongzhi Wang, Meng Shao, Ling Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China University of Queensland St Lucia4072 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China Abu Dhabi United Arab Emirates

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Analyzing Health Seeking Behavior of Chinese Residents and Their Influencing Factors Based on CHNS data

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Procedia Computer Science 2019年 162卷 835-841页

作者： Jiayu Xue Xinyue Ren Yuanjie Xu Qianqian Feng School of Economics and Management University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Sino-Danish College University of Chinese Academy of Sciences Beijing China Institutes of Science and Development Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

With the improvement of people’s living standards, Chinese residents have paid more and more attention to health. The development of science and technology has made medical health big data emerge and has gradually become a research hotspot. In our paper, we first adopted Logistic Regression to find out the important variables for health seeking behavior and then we verified that this behavior can be predicted based on machine learning algorithms. By comparing classic Support Vector Machine (SVM) algorithm and improved SVM model with different variables, we finally proposed a SVM model based on SMOTE algorithm that is relatively optimal for predicting health seeking behavior.

关键词： Health seeking behavior Logistic Regression Support Vector Machine SMOTE

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Wang, Ruoyun Chen, Fanxuan Chen, Haoman Lin, Chenxing Shuai, Jincen Wu, Yutong Ma, Lixiang Hu, Xiaoqu Wu, Min Wang, Jin Zhao, Qi Shuai, Jianwei Pan, Jingye Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China Wenzhou Medical University Wenzhou China UCSC Baskin School of Engineering University of California Santa Cruz CA United States Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston MA United States Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China Stony Brook University Stony Brook NY United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging. © 2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Corrigendum to “Rational Protocols and Attacks in Blockchain System”

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Security and Communication Networks 2021年第1期2021卷

作者： Tao Li Yuling Chen Yanli Wang Yilei Wang Minghao Zhao Haojia Zhu Youliang Tian Xiaomei Yu Yixian Yang State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou 550025 *** College of Computer Science and Technology Guizhou University Guiyang Guizhou 550025 *** School of Information Science and Engineering Qufu Normal University Rizhao 276825 *** School of Software Tsinghua University Beijing 100084 *** School of Information Science and Engineering Shandong Normal University Jinan 250014 ***

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Genome Sequences Provide Insights into the Reticulate Origin and Unique Traits of Woody Bamboos

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Molecular Plant 2019年第10期12卷 1353-1365页

作者： Zhen-Hua Guo Peng-Fei Ma Guo-Qian Yang Jin-Yong Hu Yun-Long Liu En-Hua Xia Mi-Cai Zhong Lei Zhao Gui-Ling Sun Yu-Xing Xu You-Jie Zhao Yi-Chi Zhang Yu-Xiao Zhang Xue-Mei Zhang Meng-Yuan Zhou Ying Guo Cen Guo Jing-Xia Liu Xia-Ying Ye Yun-Mei Chen Yang Yang Bin Han Choun-Sea Lin Ying Lu De-Zhu Li Germplasm Bank of Wild Species Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China Key Laboratory for Plant Diversity and Biogeography in East Asia Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China State Key Laboratory of Tea Plant Biology and Utilization Anhui Agricultural UniversityHefeiAnhui 230036China Kunming College of Life Science University of Chinese Academy of SciencesKunmingYunnan 650201China lnstitute of Plant Stress Biology State Key Laboratory of Cotton BiologyDepartment of BiologyHenan UniversityKaifengHenan 475001China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnan 650224China Yunnan Academy of Biodiversity Southwest Forestry UniversityKunmingYunnan 650224China National Center for Gene Research Shanghai Institute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200032China Agricultural Biotechnology Research Center Academia SinicaTaipei College of Fisheries and Life Science Shanghai Ocean UniversityShanghai 201306China

Polyploidization is a major driver of speciation and its importance to plant evolution has been well *** comprise one diploid herbaceous and three polyploid woody lineages,and are members of the only major subfamily in grasses that diversified in forests,with the woody members having a tree-like lignified *** this study,we generated four draft genome assemblies of major bamboo lineages with three different ploidy levels(diploid,tetraploid,and hexaploid).We also constructed a high-density genetic linkage map for a hexaploid species of bamboo,and used a linkage-map-based strategy for genome assembly and identification of subgenomes in *** phylogenomic analyses using a large dataset of syntenic genes with expected copies based on ploidy levels revealed that woody bamboos originated subsequent to the divergence of the herbaceous bamboo lineage,and experienced complex reticulate evolution through three independent allopolyploid events involving four extinct diploid ancestors.A shared but distinct subgenome was identified in all polyploid forms,and the progenitor of this subgenome could have been critical in ancient polyploidizations and the origin of woody *** genetic clues to the unique flowering behavior and woody trait in bamboos were also *** together,our study provides significant insights into ancient reticulate evolution at the subgenome level in the absence of extant donor species,and offers a potential model scenario for broad-scale study of angiosperm origination by allopolyploidization.

关键词： Bambusoideae polyploidization comparative genomics subgenome evolution flowering

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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