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18th International Conference on Computer science and Education, ICCSE 2023

作者： Yu, Li Wu, Ming-Wei Cen, Gang Xi, Yong-Xin Yan, Xin Qiu, Tian-Hao School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789819707904

Artificial intelligence (AI) is advancing rapidly and finding its way into various industries. Universities are placing a high priority on incorporating AI into professional education. However, for students who are not majoring in AI, applying AI technology to projects can be challenging. To foster the development of application-oriented talents in the AI era, this article presents an illustrative example of project-based learning that focuses on the use of AI tools for assisted education in the context of blind navigation vehicles. The project specifically concentrates on core AI applications, such as pedestrian detection using Raspberry Pi and line tracking for blind navigation. Voice broadcasting is employed to facilitate interaction with visually impaired individuals. The project is carried out in teams, with an emphasis on utilizing user-friendly AI tools for project-based education and practical experimentation. By integrating theory and practice, students have the opportunity to enhance their practical skills, deepen their understanding of AI, strengthen teamwork, improve communication skills, and foster innovative thinking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Unknown Input Estimation and FDI Attack Detection for Autonomous Vehicles 13

Unknown Input Estimation and FDI Attack Detection for Autono...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Ma, Mengfan Luo, Shijian Guo, Shenghui School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9798350361674

For autonomous vehicles that contain both unknown inputs and attack signals, this paper focuses on addressing the challenges associated with estimating unknown factors and detecting potential attacks. First, a dynamic model of an autonomous vehicle is proposed and combined with a lane keeping model, a model of an autonomous vehicle containing both unknown input signals and attack signals is constructed. Then the autonomous vehicle state and unknown input signals are estimated by designing a proportional integral observer. Also, an attack detection method for autonomous driving vehicles is proposed. Moreover, the viability of the proposed approach is verified through a simulation analysis conducted on real autonomous vehicles. © 2024 IEEE.

关键词： Autonomous vehicles

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ODFKG-A: Object Detection Fused with Knowledge Graph Based on Attention Mechanism 7

ODFKG-A: Object Detection Fused with Knowledge Graph Based o...

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7th International Conference on Computer information science and Application technology, CISAT 2024

作者： Fan, Yujiao Xu, Jie Yang, Fan Xie, Linbing Miao, Ke Wu, Zelei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350375107

In recent years, object detection technology has been widely applied in fields such as autonomous driving, underwater navigation, and security monitoring. Although modern object detectors can accurately represent the features of each region, recognizing occluded or small objects in large-scale multi-category scenarios remains challenging. This paper proposes Object Detection Fused with Knowledge Graph Based on Attention Mechanism (ODFKG-A). The method utilizes attention mechanism to emphasize important inter-channel relationships, enriching image feature information, and leverages knowledge graph for reasoning to further enhance features for each category. This model achieves detection accuracies of 80.8% on the VOC2007 test set and 56% on the COCO2017 validation set. These results demonstrate that combining a knowledge graph with an attention mechanism can effectively improve the performance of object detectors in complex scenarios. © 2024 IEEE.

关键词： Knowledge graph

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Emotion-oriented Cross-modal Prompting and Alignment for Human-centric Emotional Video Captioning

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IEEE Transactions on Multimedia 2025年 27卷 3766-3780页

作者： Wang, Yu Liu, Yuanyuan Zhou, Shunping Huang, Yuxuan Tang, Chang Zhou, Wujie Chen, Zhe China University of Geosciences School of Computer Science School of Geography and Information Engineering Wuhan430074 China China University of Geosciences School of Computer Science Wuhan430074 China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310018 China La Trobe University Cisco-La Trobe Centre for AI and IoT School of Computing Engineering and Mathematical Sciences BundooraVIC3086 Australia

Human-centric Emotional Video Captioning (H-EVC) aims to generate fine-grained, emotion-related sentences for human-based videos, enhancing the understanding of human emotions and facilitating human-computer emotional interaction. However, existing video captioning methods primarily focus on overall event content, often overlooking sufficient subtle emotional clues and interactions in videos. As a result, the generated captions frequently lack emotional information. To address this, we propose a novel Emotion-oriented Cross-modal Prompting and Alignment (ECPA) approach for large foundation models to enhance H-EVC accuracy by effectively modeling fine-grained visual-textual emotion clues and interactions. Using large foundation models, our ECPA introduces two learnable prompting strategies: visual emotion prompting (VEP) and textual emotion prompting (TEP), as well as an emotion-oriented cross-modal alignment (ECA) module. In VEP, we develop two-level learnable visual prompts, i.e., emotion recognition (ER)-level and action unit (AU)-level prompting, to assist pre-trained vision-language foundation models to attend to both coarse and fine emotion-related visual information in videos. In TEP, we correspondingly devise two-level learnable textual prompts, i.e., sentence-level emotional tokens, and word-level masked tokens, for obtaining both whole and local textual prompt representations related to emotions. To further facilitate the interaction and alignment of visual-textual emotion prompt representations, our ECA introduces another two levels of emotion-oriented prompt alignment learning mechanisms: the ER-sentence level and the AU-word level alignment losses. Both enhance the model's ability to capture and integrate both global and local cross-modal emotion semantics, thereby enabling the generation of fine-grained emotional linguistic descriptions in video captioning. Extensive experiments not only demonstrate that our ECPA outperforms existing state-of-the-art ap

关键词： Semantics

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Robust Metric Localization in Autonomous Driving via Doppler Compensation With Single-Chip Radar

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 491-502页

作者： Gao, Pengen Zhang, Shengkai Wang, Wei Lu, Chris Xiaoxuan Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Wuhan University of Technology School of Information Engineering Wuhan430074 China The University of Edinburgh School of Informatics EdinburghEH8 9AB United Kingdom

Metric localization is vital to autonomous driving where it corrects cumulative errors in a long-term run. Such errors are inevitable in real scenarios where GPS signals or some other drift-free exteroceptive measurements are not available, e.g., when an automobile goes through a tunnel. Using FMCW-based mmWave radars is an attractive metric localization technique with improved robustness as RF signals can traverse small particles in harsh weather conditions like snowing, foggy, and storming, but it faces a fundamental challenge of Doppler distortion. Existing works take spatial constraints to mitigate the Doppler distortion of point clouds from mechanical radars with limited accuracy. Modern single-chip mmWave radars that provide dynamic estimates, i.e., radial velocities, bring new opportunities to develop more accurate approaches. This paper presents DC-Loc++, a robust metric localization framework by compensating Doppler distortions using a single-chip mmWave radar. It consists of an explicit velocity-assisted Doppler compensation module for each radar sub-map, an uncertainty-aware metric registration algorithm, and a failure recovery method that validates measurement constraints to generate a more confident pose graph for optimizing vehicle poses. Extensive experiments on both nuScenes dataset and a synthetic CARLA dataset show the effectiveness of DC-Loc++, achieving 99.2% success rate and more than 20.0%, 30.2% error reductions in terms of translation and rotation estimates, respectively, compared with existing approaches. © 2000-2011 IEEE.

关键词： Radar measurement

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PNSS: Unknown Face Presentation Attack Detection with Pseudo Negative Sample Synthesis

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Computers, Materials & Continua 2025年第5期83卷 3097-3112页

作者： Hongyang Wang Yichen Shi Jun Feng Zitong Yu Zhuofu Tao School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang050043China Shijiazhuang Key Laboratory of Artificial Intelligence Shijiazhuang Tiedao UniversityShijiazhuang050043China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Computer Science and Technology Great Bay UniversityDongguan523808China School of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Face Presentation Attack Detection(fPAD)plays a vital role in securing face recognition systems against various presentation *** supervised learning-based methods demonstrate effectiveness,they are prone to overfitting to known attack types and struggle to generalize to novel attack *** studies have explored formulating fPAD as an anomaly detection problem or one-class classification task,enabling the training of generalized models for unknown attack ***,conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown *** address this challenge,we propose a novel framework focusing on unknown attack detection using exclusively bonafide facial data during *** core innovation lies in our pseudo-negative sample synthesis(PNSS)strategy,which facilitates learning of compact decision boundaries between bonafide faces and potential attack ***,PNSS generates synthetic negative samples within low-likelihood regions of the bonafide feature space to represent diverse unknown attack *** overcome the inherent imbalance between positive and synthetic negative samples during iterative training,we implement a dual-loss mechanism combining focal loss for classification optimization with pairwise confusion loss as a *** architecture effectively mitigates model bias towards bonafide samples while maintaining discriminative *** evaluations across three benchmark datasets validate the framework’s superior ***,our PNSS achieves 8%–18% average classification error rate(ACER)reduction compared with state-of-the-art one-class fPAD methods in cross-dataset evaluations on Idiap Replay-Attack and MSU-MFSD datasets.

关键词： Face presentation attack detection pseudo negative sample anomaly detection one-class classification

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Low PAPR FBMC-OQAM System Based on Data Mapping and DFT Spreading

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IEEE Internet of Things Journal 2025年第12期12卷 22311-22324页

作者： Li, Liming Zhang, Jiliang Ding, Liqin Wang, Yang Zhang, Haining NingboTech University School of Information Science and Engineering Ningbo315100 China Harbin Institute of Technology Department of Electronic and Information Engineering Shenzhen518055 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nankai University College of Software Tianjin300350 China

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is considered as a promising waveform for Internet of Things (IoT) due to its capability to mitigate interference among asynchronous users. However, as a multicarrier technique, the FBMC-OQAM signal also has a high peak-to-average power ratio (PAPR), which is detrimental to low-power transmission. To address this issue, this paper proposes a novel discrete Fourier transform (DFT)-spread scheme to exhaustively reduce the PAPR of the FBMC-OQAM signal. In this scheme, the transmitting data symbols are first mapped with a conjugate symmetry rule and then coded by the DFT. Using this method, the preprocessing step in FBMC-OQAM that involves separating the real and imaginary parts of the quadrature amplitude modulation (QAM) symbols can be avoided. Compared with existing DFT-spread FBMC-OQAM schemes, the proposed scheme demonstrates better PAPR performance while maintaining equivalent spectral efficiency. Additionally, the computational complexity of the proposed scheme experiences only a slight increase, thereby preserving a relatively low computational burden. Numerical simulation results validate the effectiveness of the proposed scheme. Furthermore, the effect of the prototype filter on the PAPR is investigated, and a trade-off exists between the PAPR and out-of-band performance. © 2014 IEEE.

关键词： Quadrature amplitude modulation

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Adjustable patch and feature prior token-based transformer for weakly supervised semantic segmentation

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International Journal of Computers and Applications 2024年第7期46卷 445-454页

作者： Li, Linjuan Zhang, Haoxue Xie, Gang Bai, Yanhong School of Electronic and Information Engineering Taiyuan University of Science and Technology Taiyuan China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China

Weakly supervised semantic segmentation is a challenging task, utilizing only low-cost weak supervision to produce pixel-level predictions. Existing transformer-based methods for weakly supervised semantic segmentation have some limitations: (1) Fixing patch size might destroy the structured semantics, which is unfriendly to objects of different scales, and (2) Ignoring the prior features when using multi-head attention mechanisms might lead to inaccurate segmentation localization. To tackle these issues, we proposed an effective transformer framework coupled with the adjustable patch and prior feature tokens, termed as APFPformer, in which an adjustable patch module is developed to split the image according to the area of the salient object for preserving the structured semantics in patches. A prior feature token module is devised to exploit the edge and texture information as prior tokens, ensuring the gain of discriminative representation. Additionally, a single-stage scheme is applied to reduce the computation and rapid segmentation process. Our experiments demonstrate the superiority of our approach over early methods, gaining competitive mean Intersection-over-Union scores of 67.9% on the PASCAL VOC2012 dataset and 40.2% on the MS COCO dataset. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantic Segmentation

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Research on Multi-protocol Wireless Adaptation Automatic Test System 24

Research on Multi-protocol Wireless Adaptation Automatic Tes...

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2024 International Conference on Computing, Machine Learning and Data science, CMLDS 2024

作者： Zhou, Yun Yu, Xiaoyu Liu, Tao School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400716393

With the continuous deepening of industrial automation, automatic test systems play an increasingly important role in the communications field, software industry, artificial intelligence and other fields. Traditional automatic test systems are mainly based on wired connections, which are easily subject to various limitations. In many scenarios, there are problems such as difficult wiring, incompatible interfaces, long development cycles, and poor scalability. Wireless communication is an effective way to solve this series of problems, however, different wireless communication technologies are suitable for different scenarios, and it is difficult to use one protocol to adapt to most measurement scenarios. To solve this problem, our project team developed a universal wireless adaptation terminal that can perform wireless adaptation to test instrument terminals using different protocols and different interfaces, expand their wireless communication capabilities, and be compatible with a variety of Wireless communication protocols. © 2024 ACM.

关键词： software testing

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Design of modified model of intelligent assembly digital twins based on optical fiber sensor network

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Digital Communications and Networks 2024年第5期10卷 1542-1552页

作者： Zhichao Liu Jinhua Yang Juan Wang Lin Yue School of Opto-Electronic Engineering Changchun University of Science and TechnologyChangchun130000JilinChina Key Laboratory of Optoelectric Measurement and Optical Information Transmission Technology of Ministry of Education Changchun University of Science and TechnologyChangchun130000JilinChina Department of Actuarial Science and Business Analytics Macquarie UniversityNSW2109Australia

Intelligent assembly of large-scale,complex structures using an intelligent manufacturing platform represents the future development direction for industrial *** large-scale structural assembly processes,several bottleneck problems occur in the existing auxiliary assembly ***,the traditional LiDARbased assembly technology is often limited by the openness of the manufacturing environment,in which there are blind spots,and continuous online assembly adjustment thus cannot be ***,for assembly of large structures,a single-station LiDAR system cannot achieve complete coverage,which means that a multi-station combination method must be used to acquire the complete three-dimensional data;many more data errors are caused by the transfer between stations than by the measurement accuracy of a single station,which means that the overall system's measurement and adjustment errors are increased ***,because of the large numbers of structural components contained in a large assembly,the accumulated errors may lead to assembly interference,but the LiDAR-assisted assembly process does not have a feedback perception capability,and thus assembly component loss can easily be caused when assembly interference ***,this paper proposes to combine an optical fiber sensor network with digital twin technology,which will allow the test data from the assembly entity state in the real world to be applied to the"twin"model in the virtual world and thus solve the problems with test openness and data *** problem of station and perception feedback is also addressed and represents the main innovation of this *** system uses an optical fiber sensor network as a flexible sensing medium to monitor the strain field distribution within a complex area in real time,and then completes real-time parameter adjustment of the virtual assembly based on the distributed *** areas include areas that are laser-unreachable,areas wi

关键词： Digital twins Intelligent manufacturing Intelligent assembly Optical fiber sensor network Assembly condition monitoring algorithm

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