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2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023

作者： Jin, Zhichao Lu, Kai Yang, Nan Hu, Peng Fei Yang, Donglai Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (纸本)9798350358971

A decoupling method based on parasitic dielectric resonators (DRs) is proposed for compact multiple-input multiple-output (MIMO) antenna systems. Because a DR can scatter electromagnetic waves strongly at its resonance frequencies, it can serve as an effective decoupling structure. In this work, this concept is applied to a dual-port monopole antenna system. The two monopoles are separated 20mm (0.235 λ0), with an original isolation level of 8 dB. By introducing a pair of cylindrical DRs (with a material relative permittivity of 7.9), the isolation level between the two monopoles can be improved by 12-25 dB from 3.4-3.6 GHz. Simulation results show that an overlapping bandwidth (|S11| © 2023 IEEE.

关键词： Decoupling dielectric resonators (DRs) multiple-input and multiple-output (MIMO)

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haoran Xu, Zhezhuang Zhu, Yuhang Lin, Ye College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface. © 2024 IEEE.

关键词： Discoloration

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A Probabilistic Topology Inference Method for Networked Dynamical System via Single Excitation 63

A Probabilistic Topology Inference Method for Networked Dyna...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Jiao, Qing Li, Yushan He, Jianping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Dept. of Automation Shanghai China

ISBN: (纸本)9798350316339

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Network topology

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Vision Based Deflection Angle Measurement of Flight control Surfaces in Aircraft Testing 22

Vision Based Deflection Angle Measurement of Flight Control ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Xu, Jiaxin Ren, Cheng Chen, Cailian Ma, Yehan Guan, Xinping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798331527471

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05◦ © 2024 IEEE.

关键词： Mesh generation

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Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Skewed Pins Detection of Aviation Connectors Based on Multi-...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Li, Jiaming Ren, Cheng Xu, Jiaxin Wang, Yao Xu, Hanlin Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798331507886

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. © 2024 IEEE.

关键词： defect detection machine vision multi-modal cooperative perception skewed pin

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Cluster Consensus Problem on Time-varying Higher-Order Interaction Networks 63

Cluster Consensus Problem on Time-varying Higher-Order Inter...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wei, Haoyu Pan, Lulu Shao, Haibin Li, Dewei Yu, Wenbin Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

This paper examines the cluster consensus problem of higher-order interaction networks on switching networks. The matrix-valued inter-agent coupling mechanism is employed to capture the higher-order interactions amongst neighboring agents. Specifically, for a higher-order interaction network with both positive definite and positive semi-definite coupling matrices, we examine the scenario in which the nullspace of positive semi-definite coupling matrices is constructed by a set of orthogonal basis vectors. From the perspective of the lower-order component graph -the projection of network structure onto the direction of each orthogonal basis-results on the explicit characterization of cluster consensus of higher-order interaction networks are provided. Moreover, for time-varying higher-order interaction networks, necessary and sufficient conditions are provided to ensure that a time-varying network shares the same steady-state values as the integral time-invariant network. Simulation results are provided to demonstrate the theory. © 2024 IEEE.

关键词： Time varying systems

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Ying Chen, Kang Xu, Zhezhuang Lin, Ye Zeng, Qingbo College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement. © 2024 IEEE.

关键词： Contrastive Learning

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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Semi-supervised learning with GAN for Semantic Segmentation of Wood Defect image

Semi-supervised learning with GAN for Semantic Segmentation ...

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2024 China Automation Congress, CAC 2024

作者： Zheng, Kunxin Zheng, Song Xu, Zhezhuang Lin, Ye Zhu, Yuhang College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data. © 2024 IEEE.

关键词： Generative adversarial networks

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Pointer Meter Reading under Noise Based on Virtual Data Generation

Pointer Meter Reading under Noise Based on Virtual Data Gene...

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2024 China Automation Congress, CAC 2024

作者： Zhu, Jinyang Xu, Zhezhuang Lin, Ye College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accuracy of meter readings. Additionally, the meter images and labels under noise are hard to obtain. To address these challenges, We propose a method for pointer meter reading under noise based on virtual data generation. Firstly, We utilize Unity to construct an industrial meter scene, which generates the images of meter under noise and theri labels. Secondly, we develop a U-Net network with an ASPP module, which expands the receptive field of network and enhances the ability of segmentation network to capture global semantic information under noise. Finally, the angular method is used to calculate the meter readings. Experimental results show that this method can effectively read meters under noise in outdoor environments. © 2024 IEEE.

关键词： Virtual addresses

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