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arXiv 2024年

作者： Wu, Xin Huang, Zhanchao Wang, Li Chanussot, Jocelyn Tian, Jiaojiao The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Spatial Data Mining and Information Sharing The Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China The National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Remote Sensing Technology Institute German Aerospace Center Weßling82205 Germany

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in crowded environments and are unable to identify obscured objects. To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities. Based on these datasets, we propose a multimodal collaboration network for dense and occluded vehicle detection, MuDet for short. MuDet hierarchically enhances the completeness of discriminable information within and across modalities and differentiates between simple and complex samples. MuDet includes three main modules: Unimodal Feature Hierarchical Enhancement (Uni-Enh), Multimodal Cross Learning (Mul-Lea), and Hard-easy Discriminative (He-Dis) Pattern. Uni-Enh and Mul-Lea enhance the features within each modality and facilitate the cross-integration of features from two heterogeneous modalities. He-Dis effectively separates densely occluded vehicle targets with significant intra-class differences and minimal inter-class differences by defining and thresholding confidence values, thereby suppressing the complex background. Experimental results on two re-labeled multimodal benchmark datasets, the 4K-SAI-LCS dataset, and the ISPRS Potsdam dataset, demonstrate the robustness and generalization of the MuDet. The codes of this work are available openly at https://***/Shank2358/MuDet. © 2024, CC BY-NC-SA.

关键词： Remote sensing

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SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

arXiv

引用

arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Liu, Hongyan Lu, Zhiwu He, Jun Du, Xiaoyong Key Laboratory of Data Engineering Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Time Synchronization for 5G and TSN Integrated Networking

引用

IEEE Journal on Selected Areas in Communications 2025年

作者： Wang, Zixiao Li, Zonghui Long, Cheng Zheng, Yiming Ai, Bo Song, Xiaoyu Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Portland State University Department of Electrical and Computer Engineering PortlandOR United States

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP) is promoting the integration of 5th Generation Mobile Communication Technology (5G) and Time-Sensitive Networking (TSN). Time synchronization is essential for deterministic data transmission. Based on the 3GPP's vision of the 5G and TSN integrated networking with interoperability, we improve the time synchronization of TSN to conquer the multi-gNB competition, re-transmission, and mobility problems for the integrated 5G time synchronization. We implemented the improvement mechanisms and systematically validated the performance of 5G+TSN time synchronization. Based on the simulation in 500m x 500m industrial environments, the improved time synchronization achieved a precision of 1 microsecond with interoperability between 5G nodes and TSN nodes. © 1983-2012 IEEE.

关键词： Industrial robots

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Covert beamforming design for intelligent reflecting surface assisted IoT networks

arXiv

引用

arXiv 2021年

作者： Ma, Shuai Zhang, Yunqi Li, Hang Sun, Junchang Shi, Jia Zhang, Han Shen, Chao Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Data-driven Information System Laboratory Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

In this paper, we consider covert beamforming design for intelligent reflecting surface (IRS) assisted Internet of Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the later case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of Kullback-Leibler divergence to develop the robust beamformer based on a relaxation, S-Lemma and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design. © 2021, CC BY.

关键词： Beamforming

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PWG-IDS: An intrusion detection model for solving class imbalance in IIoT networks using generative adversarial networks

arXiv

引用

arXiv 2021年

作者： Zhang, Lei Jiang, Shuaimin Shen, Xiajiong Gupta, Brij B. Tian, Zhihong Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng47500 China Institute of Data and Knowledge Engineering Henan University Kaifeng47500 China School of Computer and Information Engineering Henan University Kaifeng47500 China National Institute of Technology Kurukshetra India Asia University Taichung Taiwan The Department of Cyberspace Insitute of Advanced Technology GuangZhou University GuangZhou510006 China

With the continuous development of industrial IoT (IIoT) technology, network security is becoming more and more important. And intrusion detection is an important part of its security. However, since the amount of attack traffic is very small compared to normal traffic, this imbalance makes intrusion detection in it very difficult. To address this imbalance, an intrusion detection system called pretraining Wasserstein generative adversarial network intrusion detection system (PWG-IDS) is proposed in this paper. This system is divided into two main modules: 1) In this module, we introduce the pretraining mechanism in the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) for the first time, firstly using the normal network traffic to train the WGAN-GP, and then inputting the imbalance data into the pre-trained WGAN-GP to retrain and generate the final required data. 2) Intrusion detection module: We use LightGBM as the classification algorithm to detect attack traffic in IIoT networks. The experimental results show that our proposed PWG-IDS outperforms other models, with F1-scores of 99% and 89% on the 2 datasets, respectively. And the pretraining mechanism we proposed can also be widely used in other GANs, providing a new way of thinking for the training of GANs. Copyright © 2021, The Authors. All rights reserved.

关键词： Intrusion detection

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AMGFormer: A multilevel GNSS recording autoencoder for agricultural machinery trajectory operation mode identification

引用

Computers and Electronics in Agriculture 2025年 237卷

作者： Pan, Chenyu Zhai, Weixin School of Science China University of Mining and Technology Beijing 100083 China College of Information and Electrical Engineering China Agricultural University Beijing 100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural Affairs Beijing 100083 China

Leveraging the spatiotemporal features contained in massive amounts of trajectory data to identify agricultural machinery operation modes and assigning corresponding semantic labels to each unknown trajectory point are important basic tasks in research on spatiotemporal trajectory processing in the agricultural field. However, existing methods ignore the intrapoint feature regulation and fail to fully explore the correlation and dependency between agricultural machinery trajectories. To overcome the above defects, we propose a tailored identification architecture named AMGFormer to model various long-range dependencies in end-to-end agricultural machinery trajectory processing. First, to explore the potential information in agricultural machinery trajectories fully, we propose a data enhancement scheme based on the statistical distribution enriching the expression of trajectories. Second, we developed a multilevel GNSS recording autoencoder (MGRA) for agricultural machinery trajectories,which consists of an information-aware context module and a semi-adaptive spatial aggregation context module to aggregate features across spatial dimensions in a dual manner between and within points of agricultural machinery trajectories to extract contextual information. Finally, we design a dynamic interaction module to cross-model the complex information flows from different modules and filter out irrelevant contexts between various modalities, thereby achieving comprehensive and discriminative feature representation. To evaluate the effectiveness of our model, we conducted experiments on agricultural trajectory samples provided by the key laboratory of Agricultural Machinery Operation Monitoring and Big data Application, Ministry of Agriculture and Rural Affairs, and compared our results with those of advanced agricultural machinery trajectory operation mode identification methods. Our model achieved 91.30% accuracy and a 91.28% F1 score, which is 4.57% higher accuracy and a 4.6

关键词： Agricultural machinery trajectory operation mode identification data enhancement Dynamic interaction module Information awareness context Semi-adaptive spatial aggregation context

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Recovering Accurate Labeling Information from Partially Valid data for Effective Multi-Label Learning

arXiv

引用

arXiv 2020年

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Sci & Information Engineering Hefei University of Technology China

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Joint item recommendation and attribute inference: An adaptive graph convolutional network approach

arXiv

引用

arXiv 2020年

作者： Wu, Le Yang, Yonghui Zhang, Kun Hong, Richang Fu, Yanjie Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology College of Engineering and Computer Science University of Central Florida

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users’ (items’) characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user (item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user (item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks. To this end, in this paper, we define these two tasks in an attributed user-item bipartite graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference. The key idea of AGCN is to iteratively perform two parts: 1) Learning graph embedding parameters with previously learned approximated attribute values to facilitate two tasks;2) Sending the approximated updated attribute values back to the attributed graph for better graph embedding learning. Therefore, AGCN could adaptively adjust the graph embedding learning parameters by incorporating both the given attributes and the estimated attribute values, in order to provide weakly supervised information to refine the two tasks. Extensive experimental results on three real-world datasets clearly show the effectiveness of the proposed model. Copyright © 2020, The Authors. All rights reserved.

关键词： Collaborative filtering

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A theoretic study of a distance-based regression model

引用

Science China Mathematics 2019年第5期62卷 979-998页

作者： Jialu Li Wei Zhang Sanguo Zhang Qizhai Li School of Mathematics and Statistics Beijing Institute of Technology Biostatistics and Bioinformatics Branch National Institute of Child Health and Human Development School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences LSC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences

The distance-based regression model has many applications in analysis of multivariate response regression in various ?elds, such as ecology, genomics, genetics, human microbiomics, and neuroimaging. It yields a pseudo F test statistic that assesses the relation between the distance(dissimilarity) of the subjects and the predictors of interest. Despite its popularity in recent decades, the statistical properties of the pseudo F test statistic have not been revealed to our knowledge. This study derives the asymptotic properties of the pseudo F test statistic using spectral decomposition under the matrix normal assumption, when the utilized dissimilarity measure is the Euclidean or Mahalanobis distance. The pseudo F test statistic with the Euclidean distance has the same distribution as the quotient of two Chi-squared-type mixtures. The denominator and numerator of the quotient are approximated using a random variable of the form ξχ_d^2+ η, and the approximate error bound is given. The pseudo F test statistic with the Mahalanobis distance follows an F *** simulation studies, the approximated distribution well matched the "exact" distribution obtained by the permutation procedure. The obtained distribution was further validated on H1N1 in?uenza data, aging human brain data, and embryonic imprint data.

关键词： distance-based regression Euclidean pseudo F test statistic Mahalanobis

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An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir 16th

An Improved Whale Optimization Algorithm and Its Application...

引用

16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Lü, Ji-Xiang Yan, Li-Jun Pan, Tien-Szu Chu, Shu-Chuan Pan, Jeng-Shyang He, Xian-Kang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Shenzhen Institute of Information Technology Shenzhen518172 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University Sturt Rd Bedford ParkSA5042 Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350108 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved;secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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