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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Jiaming Li Cheng Ren Jiaxin Xu Yao Wang Hanlin Xu Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection.

关键词： Connectors Point cloud compression Wiring Three-dimensional displays Accuracy Manuals Inspection Pins Artificial intelligence Defect detection

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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Digital Twin Enabled Flight control system Testing: A Physical-Virtual Mapping Experiment

Digital Twin Enabled Flight Control System Testing: A Physic...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Cheng Ren Cailian Chen Shanying Zhu Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Flight control system testing (FCST) is one of the most important testing during aircraft final assembly, however, traditional testing method highly rely on manual labor, resulting in low testing quality and efficiency. Targeting at improving the testing quality and efficiency, in this paper, we apply digtial twin (DT) technology in the FCST process for the first time. A virtual experimental platform named DT-FCST is constructed which is identical to a real wing experimental platform, supporting testing elements management and physical-virtual mapping experiments. The development process of the DT-FCST platform is detailed. Several experiments are conducted to demonstrate the effectiveness of DT technology. Based on motion scripts integrating real-time data from the cockpit, the cockpit control sticks and flight control surfaces are driven to achieve consistency between physical and virtual motions. With the help of DT technology, the entire movement process of control sticks and flight control surfaces are mapped with high fidelity in the DT-FCST platform, greatly enhancing the testing efficiency and monitoring quality.

关键词： system testing Process control Manuals Real-time systems Aircraft Informatics Surface treatment Monitoring Testing Aerospace control

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1571-1583页

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th International Conference on information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 IEEE.

关键词： Image matching

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Temporal and Spatial Changes of Forest Leaf Area Index Based on MODIS: A Case Study of Fujian Province 6th

Temporal and Spatial Changes of Forest Leaf Area Index Based...

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Yu, Hua Zheng, Kaiwen Li, Hui Liang, Fengna Zhang, Jinxin Zhang, Fuquan Ocean College Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811650352

Forest vegetation is an important place for energy conversion and material cycle, and leaf area index (LAI) is an important parameter to describe the process of plant canopy function. In this paper, with the support of computer technology such as VC, the LAI of Fujian Province from 2001 to 2017 was analyzed and studied by using MODIS remote sensing data with 500 m resolution every 8 days. The results showed that the vegetation growth was the most vigorous in the 200–800 m terrain. The evergreen coniferous forest was mainly distributed in the northwest of Fujian Province, while the mixed forest in the interior. The seasonal mean value of LAI for many years reaching 3.5was the highest in the summer and autumn;the second highest was in the spring, with the mean value of LAI reaching 2.0;the lowest was in the winter, with the mean value of LAI reaching 1.5. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conversion

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Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding

arXiv

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arXiv 2024年

作者： Dong, Cunhui Ma, Haichuan Zhang, Haotian Gao, Changsheng Li, Li Liu, Dong The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework – H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework – iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding. Copyright © 2024, The Authors. All rights reserved.

关键词： Image coding

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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A Foreground Feature Embedding Network for Object Detection in Remote Sensing Images 18th

A Foreground Feature Embedding Network for Object Detection...

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Wu, Jiahui Cai, Yuanzheng Wang, Tao Luo, Zhiming Shan, Senhua Li, Zuoyong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Artificial Intelligence Xiamen University Xiamen361005 China Istrong Technology Limited Company Fuzhou350108 China

ISBN: (纸本)9789819996360

Compared with traditional natural images, remote sensing images (RSIs) typically have high resolution. The objects in the images are densely distributed, with heterogeneous orientation and large scale variation, even among objects of the same class. In recent years, object detection algorithms have made great strides in general images, but they are still difficult to meet the challenges that exist in RSIs. Therefore, we propose a foreground feature embedding network (FFE-Net) for object detection in RSIs. To better grasp the object features in RSIs, we design a foreground feature embedding module (FFEM) to learn the foreground features of the object. This is achieved by introducing an additional semantic segmentation branch and embedding the features in the classification and regression branches. Simultaneously, we propose a modified Gaussian function with focal loss (MGFFL) as a way to eliminate the extra background noise from soft labels, making the learned foreground features more robust. Our experimental results on two publicly available remote sensing image datasets, DOTA-v1.0 and HRSC2016, validate the effectiveness of FFE-Net. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

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Annual Conference of Industrial Electronics Society

作者： Yu Wu Bo Yang Dafeng Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy.

关键词： Vibrations Industrial electronics Accuracy Computational modeling Mechanism design Predictive models Logic gates Real-time systems Data communication Industrial Internet of Things

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