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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Perceptive self-supervised learning network for noisy image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Li, Bo Zhang, Yanning Zhang, Shichao Zhang, David School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Xi’an710129 China Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To overcome these drawbacks, we propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) in this paper. PSLNet depends on a parallel network to remove noise and watermarks. The upper network uses task decomposition ideas to remove noise and watermarks in sequence. The lower network utilizes the degradation model idea to simultaneously remove noise and watermarks. Specifically, mentioned paired watermark images are obtained in a self-supervised way, and paired noisy images (i.e., noisy and reference images) are obtained in a supervised way. To enhance the clarity of obtained images, interacting two sub-networks and fusing obtained clean images are used to improve the effects of image watermark removal in terms of structural information and pixel enhancement. Taking into texture information account, a mixed loss uses obtained images and features to achieve a robust model of noisy image watermark removal. comprehensive experiments show that our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal. Codes can be obtained at https://***/hellloxiaotian/PSLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion

arXiv

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arXiv 2022年

作者： Meng, Xiangxi Gu, Yuning Pan, Yongsheng Wang, Nizhuan Xue, Peng Lu, Mengkang He, Xuming Zhan, Yiqiang Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China National Engineering Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an China

Multi-modal medical image completion has been extensively applied to alleviate the missing modality issue in a wealth of multi-modal diagnostic tasks. However, for most existing synthesis methods, their inferences of missing modalities can collapse into a deterministic mapping from the available ones, ignoring the uncertainties inherent in the cross-modal relationships. Here, we propose the Unified Multi-Modal Conditional Score-based Generative Model (UMM-CSGM) to take advantage of Score-based Generative Model (SGM) in modeling and stochastically sampling a target probability distribution, and further extend SGM to cross-modal conditional synthesis for various missing-modality configurations in a unified framework. Specifically, UMM-CSGM employs a novel multi-in multi-out Conditional Score Network (mm-CSN) to learn a comprehensive set of cross-modal conditional distributions via conditional diffusion and reverse generation in the complete modality space. In this way, the generation process can be accurately conditioned by all available information, and can fit all possible configurations of missing modalities in a single network. Experiments on BraTS19 dataset show that the UMM-CSGM can more reliably synthesize the heterogeneous enhancement and irregular area in tumor-induced lesions for any missing modalities. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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埋入式传感、图像处理技术和机器学习方法在路面监测与分析中应用的最新研究进展

引用

engineering 2021年第6期 309-334页

作者：侯越李秋晗张晨陆国阳叶周景陈逸涵汪林兵曹丹丹 Beijing Key Laboratory of Traffic Engineering Beijing University of Technology National Center for Materials Service Safety University of Science and Technology Beijing School of Transportation Southeast University Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Department of Civil and Environmental Engineering Virginia Tech

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过... 详细信息

在现代交通系统中,道路作为车辆和行人使用频率最高的民用基础设施之一,其服役状况和使用寿命直接影响通行体验和效率。因此,在路面发生不可逆损伤之前进行路面健康监测和及时养护,对于保障公共交通服务质量以及通行安全至关重要。通过路面结构动力响应监测和路面状况评估可有效表征路面损伤状况。埋入式传感器、图像处理和机器学习是目前常用的三种路面结构动力响应监测技术和分析方法。本文综述了近年来上述三种技术在路面工程中的应用现状,并阐述了这些技术在未来路面工程监测与分析中的发展方向。

关键词：路面监测与分析最新的研究进展埋入式传感器图像处理技术机器学习方法

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Predictability of real temporal networks

arXiv

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arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A comprehensive Review

arXiv

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arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Impact of Stochasticity on Platoon Stability of Car-Following Models for Emissions Estimation

SSRN

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SSRN 2022年

作者： Meng, Dongli Song, Guohua Lu, Hongyu Wu, Yizheng Zhai, Zhiqiang Yu, Lei Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University 3 Shangyuancun Haidian District Beijing100044 China School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive AtlantaGA30332 United States Department of Transportation Studies Texas Southern University 3100 Cleburne Avenue HoustonTX77004 United States Xuchang University Henan Xuchang461000 China

In the application of microscopic traffic simulations for vehicle emissions modeling, the reality and platoon stability of vehicle trajectories derived from the car-following component has been questioned. This study compared the platoon stability of the Gipps car-following model, the full velocity difference model (FVDM), the intelligent driver model (IDM), and the Wiedemann model for emissions modeling, followed by a parameter sensitivity analysis and a stochasticity impact investigation. The results indicated the superior performance of the FVDM and IDM for emissions estimation compared with the Wiedemann model, and the emissions estimation errors were stable along the platoon for the above models. Gipps model generated realistic vehicle dynamics of the first following vehicle, however, the emissions estimation errors increased along the platoon. The parameter calibration failed to improve the platoon stability of the Gipps model, while incorporating the stochastic parameters was proven to reproduce representative platoon trajectories for accurate emissions estimations. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Impact of Stochasticity on Platoon Stability of Car-Following Models for Emissions Estimation

SSRN

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SSRN 2022年

In the application of microscopic traffic simulations for vehicle emissions modeling, the reality and platoon stability of vehicle trajectories derived from the car-following component has been questioned. This study compared the platoon stability of the Gipps car-following model, the full velocity difference model (FVDM), the intelligent driver model (IDM), and the Wiedemann model for emissions modeling, followed by a parameter sensitivity analysis and a stochasticity impact investigation. The results indicated the superior performance of the FVDM and IDM for emissions estimation compared with the Wiedemann model, and the emissions estimation errors were stable along the platoon for the above models. Gipps model generated realistic vehicle dynamics of the first following vehicle, however, the emissions estimation errors increased along the platoon. This study found that it is difficult to improve the platoon stability of the Gipps model by parameter calibration. While taking into account the driving behavior heterogeneity and the human factors, the Gipps model was modified by incorporating the stochastic parameters to reproduce representative platoon trajectories for accurate emissions estimations. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Unsupervised evaluation of airspace complexity based on kernel principal component analysis

引用

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2019年第8期40卷

作者： Zhang, Zhuxi Zhu, Xi Zhu, Shaochuan Zhang, Mingyuan Du, Wenbo National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beihang University Beijing100083 China Unit 32751 Force of PLA China Research Institute of Frontier Science Beihang University Beijing100083 China School of Transportation Science and Engineering Beihang University Beijing100083 China School of Electronic and Information Engineering Beihang University Beijing100083 China

Airspace complexity evaluation is a key means to measure the airspace operational situation and the controller workload, providing the basis for the operation optimization. Its accurate evaluation is a challenging problem in the aviation domain due to numerous influencing factors, the complex correlations between factors, and the high difficulty of collecting labelled samples. This paper proposes an unsupervised evaluation method for airspace complexity. Firstly, the kernel principal component analysis is utilized to mine the nonlinear correlations in different sample dimensions, and extract several principal components in which the airspace complexity information is maximized. Furthermore, the principal component clustering which can be customized according to user requirements is designed. The proposed method achieves accurate complexity evaluation capacity under the unsupervised condition, providing effective technical support for air traffic management like airspace configuration and traffic management. © 2019, Press of Chinese Journal of Aeronautics. All right reserved.

关键词： Principal component analysis

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Structure and Evolution of Urban Heavy Truck Mobility Networks

SSRN

引用

SSRN 2022年

作者： Yang, Yitao Jia, Bin Liu, Erjian Yan, Xiao-Yong de Bok, Michiel Tavasszy, Lóránt A. Gao, Ziyou Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Department of Transport & Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Stevinweg1 Delft2628 CN Netherlands Instituto de Física Interdisciplinar y Sistemas Complejos IFISC CSIC UIB Palma de Mallorca07122 Spain

Revealing the structural properties and understanding the evolutionary mechanisms of the urban heavy truck mobility network (UHTMN) provide insights in assessment of freight policies to manage and regulate the urban freight system, and are of vital importance for improving the livability and sustainability of cities. Although massive urban heavy truck mobility data become available in recent years, in-depth studies on the structure and evolution of UHTMN are still lacking. Here we use massive urban heavy truck GPS data in China to construct the UHTMN and reveal its a wide range of structure properties. We further develop an evolving network model that simultaneously considers weight, space and system element duplication. Our model reproduces the observed structure properties of UHTMN and helps us understand its underlying evolutionary mechanisms. Our model also provides new perspectives for modeling the evolution of many other real-world networks, such as citation networks and air transportation networks. © 2022, The Authors. All rights reserved.

关键词： Complex networks

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