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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Ruizeng Zhou, Wei Liu, Haiou Gong, Jianwei Chen, Huiyan Khajepour, Amir School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computer Science and Informatics Cardiff University Cardiff U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo Canada

Existing terramechanics-based dynamic models for tracked vehicles (TRVs) are widely used in dynamics analysis. However, these models are incompatible with model-based controller design due to their high complexity and computational costs. This study presents a novel and simplified terramechanics-based dynamic model for TRVs that can be used in optimization-based real-time motion controller design. To this end, we approximated the track-ground interactions with an averaged term of the track-ground shear stresses to make the model computationally efficient and linearizable. By introducing the concepts of slip ratio and slip angle in the field of wheeled vehicles, the terramechanics-based dynamic model was finally simplified into a compact and practical single-track dynamic model reducing the demand for precise slip ratio measurements. The single-track model enables us to design an efficient motion control scheme by considering lateral and longitudinal dynamics separately. Finally, the proposed dynamic model was verified and validated under various road conditions using a real TRV. Additionally, the performance of different models was compared in simulation as an example to demonstrate that the proposed model outperforms the existing ones in TRV path-following tasks. IEEE

关键词： Model predictive control

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Research on Polarization Theory Based on Target Polarization Projection 2

Research on Polarization Theory Based on Target Polarization...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： He, Yaomin Yang, Jian Yang, Yimin Yin, Junjun Liu, Chao Academy of Military Sciences Institute of Systems Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Aiming at the information processing in classical polarization theory, such as polarization decomposition, optimal polarization, polarization filtering, polarization detection, and polarization classification, et al, they are of great significance which supported a large number of polarization applications. Although the theory is relatively rich, there are also some deficiencies, i.e., overlapping theories and inconsistent concepts, which have inconvenient effects on the systematic research of polarization theory. In this regard, this paper proposes the concept and mathematical model of "target polarization projection", in order to form two polarization basic theories with target polarization decomposition. It can achieve a series of polarization information processing such as polarization filtering, polarization detection, and polarization classification, thus providing a clear polarization theory framework and laying a theoretical foundation for the application of polarimetric SAR. © 2024 IEEE.

关键词： Wiener filtering

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SMGNN: Semantic Multi-Connected Graph Neural Network for Traffic Flow Prediction

SMGNN: Semantic Multi-Connected Graph Neural Network for Tra...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yao, Xin-Wei Li, Wei-Cai Li, Xiang-Yang Zhang, Xiao-Li Yao, Zhong-Hua Li, Qiang College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Institute for Frontier and Interdisciplinary Sciences Zhejiang University of Technology Hangzhou China School of Computer Science and Technology University of Science and Technology of China Hefei China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Zhejiang Institute of Communications Hangzhou China

ISBN: (纸本)9781665410205

Traffic flow prediction, as one of the problems of spatial correlation analysis of time series, has been extensively studied. The extraction and fusion of effective spatio-temporal features are crucial for achieving high-precision traffic flow prediction. Traditionally, the adjacency graph designed based on the neighboring nodes of real-world road networks has been indispensable for learning spatial features. However, this single connected component graph structure is prone to the phenomenon of over-smoothing, leading to homogenization of the learned spatial feature. Addressing this challenge, this paper proposes a novel Semantic Multi-connected Graph Neural Network (SMGNN) aimed at mitigating the homogeneity of spatial features and effectively modeling spatio-temporal interactions. Firstly, considering the existence of several nodes in large-scale road networks with similar traffic flow variation patterns, we semantically connect these nodes to construct multi-connected semantic spatial graphs (MSSG), replacing the traditionally used neighboring node graph in conventional graph neural networks. Correspondingly, we design a novel graph neural network architecture that cyclically fuses dynamic scale spatio-temporal features from MSSG using an improved Dynamic Spatial Graph Attention (DSGA) module. Secondly, to achieve a more effective representation, we design a Inverted Temporal Attention (ITA) module to supplement static scale temporal features. Furthermore, we introduce a Multi-dimensional and Multi-scale Feature Extraction (MMFE) module to fuse spatio-temporal features at various scales within different receptive fields. Extensive experiments conducted on real-world datasets have verified the effectiveness of our proposed method, significantly outperforming various baseline models. © 2024 IEEE.

关键词： Graph neural networks

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BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition 17th

BiEfficient: Bidirectionally Prompting Vision-Language Model...

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17th Asian Conference on computer Vision, ACCV 2024

作者： He, Haichen Liu, Weibin Xing, Weiwei School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819609079

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have shown great success in various image tasks. However, how to efficiently transfer such powerful VLMs into video domain is still an open problem. Given that full finetuning VLMs for video tasks could be computationally expensive, recent studies turn their focus on parameter-efficient finetuning (PEFT). The great potential of VLMs lies in leveraging the bidirectional semantic connections between the two modalities of vision and language. Nevertheless, most current PEFT methods use the vision-only framework and usually ignore the semantic connections between vision and language. In this paper, we propose a novel method called BiEfficient, which use bidirectional prompting schemes to efficiently transfer the VLM to video recognition task with a small number of tunable parameters: 1) Vision-to-Language: we propose two prompt mechanisms, Pre-Prompt and Post-Prompt, which act before and after the text encoder respectively to generate discriminative video-level text representation for each input video. 2) Language-to-Vision: we propose Word-Guided Visual-Prompt, which enhances the temporal modeling of videos using textual knowledge in an almost parameter-free manner. Experiments on Kinetics-400, UCF-101, HMDB-51 demonstrate that the proposed method can achieve comparable or even better performance to the full finetuning methods with much fewer tunable parameters across closed-set and zero-shot video recognition benchmarks. Code is available here: https://***/WbLiuBJTULab/BiEfficient. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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High-Fidelity Real-Time Radar Modeling with Consideration of False and Missed Alarms for Autonomous Driving Simulation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Huang, Kaibo Deng, Weiwen Wang, Ying Zhao, Rui Ding, Juan School of Transportation Science and Engineering Beihang University Beijing China College of Computer Science and Technology Jilin University Jilin China PanoSim Technology Company Limited Jiaxing Zhejiang China

Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE

关键词： Radar cross section

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No-Reference Point Cloud Quality Assessment via Contextual Point-Wise Deep Learning Network 8th

No-Reference Point Cloud Quality Assessment via Contextual P...

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Wang, Xinyu Liu, Ruijun Wang, Xiaochuan School of Computer Science and Engineering Beijing Technology and Business University Beijing100041 China

ISBN: (纸本)9789819980208

For the processing of point clouds, an accurate assessment of the quality is essential. However, point cloud quality assessment has proven to be a difficult issue, especially when the pristine point clouds are unavailable. Most existing no-reference point cloud quality assessment methods adopt projection-based routes, which inevitably suffer from occlusion and misalignment, resulting in loss of information. Alternatively, this paper proposes a novel no-reference point cloud quality assessment method via a contextual point-wise deep learning network (CPW-Net). Compared with projection-based methods, it reduces information loss by learning features directly from point coordinates and attributes. In particular, CPW-Net utilizes an Offset Attention Feature Encoder (OAFE) module to extract local and contextual features. Experiment results demonstrate that the proposed method overwhelms most publicly available no-reference metrics on SJTU dataset and gains compatible performance in comparison with most full-reference methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Biological Template Protection Based on Multikey Cryptographic Decomposition Features 9

Biological Template Protection Based on Multikey Cryptograph...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Wang, Jing Jiang, Haowen Yang, Yuqi Xin, Rundong University of Science and Technology School of Computer and Communication Engineering School of Civil Engineering Changsha410114 China University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798350383706

As more and more privacy protection mechanisms are introduced, facial recognition is gradually being optimized in terms of homo-morphic computing power. However, certain aspects have been overlooked, such as the reversibility of facial features due to key leakage and the degradation of accuracy when keys are exchanged to update the template library. To address this problem, we propose a feature grouping method that aims to improve the efficiency of ciphertext operation. This approach involves a multi-key encryption strategy for facial features that ensures their irreversibility. Experimental validation supports both the irreversibility and the ability to update within this framework. When rigorously tested, this scheme shows increased security compared to single-key frameworks because part of the key is withheld, making it difficult for attackers to obtain comprehensive facial feature data. In addition, even when using keys of the same length, attackers are faced with increased time expenditure. With regard to the upgradability of systems, experimental findings have shown for the first time that the loss of accuracy associated with homomorphic encryption algorithms is within tolerable limits. It is noteworthy that our proposed method has an even smaller loss in accuracy. When assessing the performance and precision of the algorithm, we achieved an impressive accuracy rate of 97.425% with processing times of 0.302 seconds per face. The benefits of our feature grouping method become more clear as the face template library grows. It outperforms non-grouping methods by more than 4 seconds on nearly 1,000 face libraries. © 2023 IEEE.

关键词： Face recognition

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Secure and Efficient Data Transmission Scheme Based on Physical Mechanism

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computers, Materials & Continua 2023年第5期75卷 3589-3605页

作者： Ping Zhang Haoran Zhu Wenjun Li Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer Science Hunan University of Technology and BusinessChangsha410205China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security *** security technology based on the physicalmechanism has attracted extensive *** to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical *** this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is *** order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same *** analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.

关键词： Data transmission key generation data privacy compressive sensing Internet of Things(IoT)

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