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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation

arXiv

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arXiv 2023年

作者： Chen, Ziyang Pan, Yongsheng Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China The School of Biomedical and Engineering ShanghaiTech University Shanghai201210 China

Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be deployed successfully to different healthcare centers. The difficulties mainly comes from the domain shift issue, i.e., the fundus images collected at these centers usually vary greatly in the tone, contrast, and brightness. To address this issue, in this paper, we propose a novel unsupervised domain adaptation (UDA) method called Reconstruction-driven Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation backbone for simultaneous edge detection and region prediction and design three modules to alleviate the domain gap. The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way. It also uses a style-consistency constraint to force the network to retain more domain-invariant information. The low-level feature refinement (LFR) module employs input-specific dynamic convolutions to suppress the domain-variant information in the obtained low-level features. The prediction-map alignment (PMA) module elaborates the entropy-driven adversarial learning to encourage the network to generate source-like boundaries and regions. We evaluated our RDR-Net against state-of-the-art solutions on four public fundus image datasets. Our results indicate that RDR-Net is superior to competing models in both segmentation performance and generalization ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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On the Role of Non-Localities in Fundamental Diagram Estimation

arXiv

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arXiv 2023年

作者： Liu, Jing Zheng, Fangfang Yu, Boxi Jabari, Saif School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Western Hi-tech Zone Sichuan Chengdu611756 China Division of Engineering New York University Abu Dhabi Saadiyat Island PO Box 129188 Abu Dhabi United Arab Emirates

We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities results in a clear classification of speed-density data into stationary and non-stationary points, namely, acceleration and deceleration regimes and their separating boundary. The separating boundary represents a locus of stationary traffic states, i.e., the fundamental diagram. To fit fundamental diagrams, we develop an enhanced cross entropy minimization method that honors equilibrium traffic physics. We illustrate the effectiveness of our proposed approach by comparing it with the traditional approach that uses local speed-density states and least squares estimation. Our experiments show that the separating boundary in our approach is invariant to varying trajectory samples within the same spatio-temporal region, providing further evidence that the separating boundary is indeed a locus of stationary traffic states. © 2023, CC BY.

关键词： Entropy

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Do Electric Vehicles Lead to More Severe Crashes? A Doubly Robust-Based Causal Inference Approach

SSRN

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SSRN 2025年

作者： Zhai, Guocong Xie, Kun Yang, Di Yang, Hong School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University NorfolkVA23529 United States Department of Transportation & Urban Infrastructure Studies Morgan State University BaltimoreMD21251 United States Department of Electrical & Computer Engineering Old Dominion University NorfolkVA23529 United States Singapore

Electric vehicles (EVs) possess distinct physical characteristics compared to conventional vehicles, including greater mass, larger torque, higher acceleration rates, and a lower center of gravity. However, the safety implications of these differences remain underexplored in existing literature. This study examines the relationship between vehicle type and crash injury severity by employing a rigorous causal inference framework. We analyzed seven years of police-reported crash data (2017-2023) from Virginia, USA, treating EV crashes as the treatment group and conventional vehicle crashes as the control group. To address potential confounding issues in this high-dimensional covariate space, we implement a doubly robust estimator that integrates Bayesian Additive Regression Trees for matching-based estimation (inverse probability of treatment weighting) with logistic regression for outcome modeling. This approach provides reliable causal estimates even if one component is incorrectly specified. Our findings reveal that EV crashes are associated with a 14% reduction in the odds of severe injury compared to conventional vehicle crashes, with consistent estimates across methodological approaches. This study advances knowledge on EV safety and contributes methodologically by developing a doubly robust framework for binary safety outcomes through log-transformed causal odds ratios. From a practical perspective, our findings can guide transportation agencies in developing EV-related regulations, inform insurance providers in designing premium structures for EVs, and help consumers make informed choices on EV adoptions. © 2025, The Authors. All rights reserved.

关键词： Vehicle safety

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中国双碳目标下流程制造业生命周期工程评价方法及应用（英文）

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engineering 2024年第9期 87-99页

作者：李明阳高峰聂祚仁孙博学刘宇龚先政 National Engineering Laboratory for Industrial Big-data Application Technology College of Material Science and Engineering Beijing University of Technology

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

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NSGA II for Configuration Optimization of Cold Chain Logistic Network with Fully Shared Facilities and Equipments

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1497-1516页

作者： Yu, Qu-Bo An, Le-Yao Yu, Qing-Feng Liu, Xiao-Wei Pu, Fang-Zhu Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China New Corridor Logistics Group Co. Ltd. of Sichuan Port and Shipping Investment Chengdu610000 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China

In the face of multiple issues such as unreasonable allocation of cold chain logistics resources, environmental pollution, and energy crises, better resource integration, optimization, and upgrading are needed. One of the methods to address this is fully shared facilities and equipment of cold chain logistics network. This paper proposes the construction of a cold chain logistics resource sharing platform, which includes five main entities: supply nodes, demand nodes, demand node logistics companies, 4PL and 3PLs, aiming to achieve timely response to cold chain logistics demands. A unified optimization method for facility and equipment configuration throughout the cold chain logistics process is selected. A dual-objective programming model is constructed to minimize logistics companies’ operational costs and carbon emissions. The optimization is performed on various aspects such as the types and quantities of facility and equipment configurations. To solve this dual-objective programming model, the NSGA-II intelligent algorithm is employed. The results show that compared to the sharing of single types of cold chain logistics resources such as shared delivery vehicles or shared cold storage, full sharing is an effective approach to reducing logistics companies’ operational costs, carbon emissions, and idle cold chain logistics resources. It optimizes the internal allocation of cold chain logistics resources within the cold chain logistics network. © 2023.

关键词： Carbon

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A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

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Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

arXiv

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arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

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The State-of-the-Art Review on applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and analysis

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engineering 2021年第6期7卷 845-856页

作者： Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye Yihan Chen Linbing Wang Dandan Cao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China School of Transportation Southeast UniversityNanjing 211189China Department of Civil and Environmental Engineering Virginia TechBlacksburgVA 24061USA

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and *** service quality and service life are of great importance for civil engineers as they directly affect the regular service for the ***,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation *** pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface *** technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning *** review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

关键词： Pavement monitoring and analysis The state-of-the-art review Intrusive sensing Image processing techniques Machine learning methods

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Modeling Offline Knowledge Evolution Effect for Online Knowledge Tracing

Modeling Offline Knowledge Evolution Effect for Online Knowl...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaonan Zeng Shuanghong Shen Zhenya Huang Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Hefei Comprehensive National Science Center School of Computer Science and Technology Hefei Normal University & Institute of Artificial Intelligence Hefei China State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China) iFLYTEK Co. Ltd Hefei China

Knowledge Tracing (KT) focuses on tracing stu-dents' evolving knowledge state in the process of question-answering, which has been widely applied to online learning systems. Existing KT methods leverage students' online question-answering logs to infer their knowledge state and predict their subsequent performance. However, these methods only focus on students' online behaviors without considering the fact that students' knowledge also evolves offline. Specifically, students have plenty of offline learning actions that also make changes in their knowledge state, which is called the offline knowledge evolution effect in this paper. However, these offline actions are not recorded by online learning systems, therefore leading to unreliable knowledge tracing results. To alleviate this problem, we propose a novel model named offline-aware knowledge tracing (OKT) that considers students' offline knowledge evolution for KT. In O KT, we introduce a novel concept named offline trait to represent student's offline learning action preference. Based on the preference, the model evaluates offline knowledge evolution, and apply the evolution to generate a new knowledge state for students' future performance prediction. To the end, we conduct experiments on three real-world benchmark datasets and demonstrate that OKT achieves state-of-the-art performance. Besides, the experimental results indicate that considering offline actions can help interpret the KT results.

关键词： Learning systems Predictive models Benchmark testing Behavioral sciences

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