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Active Magnetic Compensation Based on Parametric Resonance Magnetometer

Journal of Shanghai Jiaotong university(science) 2024年第2期29卷 280-289页

作者：郭阳李绍良黄艺明骆曼箬刘华 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Spacefight Control Technology Shanghai Key Laboratory of Space Intelligent Control TechnologyShanghai Engineer Research Center of InertiaShanghai 201109China

Based on the parametric resonance magnetometer(PRM)theory,this paper establishes an experimen-tal system of *** experimental results are consistent with the theoretical predictions.A PRM has been developed with sensitivity of 0.5 pT/Hz^(1/2),which can detect the magnitude of residual magnetic field;furthermore,a proportion-integration-differentiation(PID)closed-loop magnetic compensation system of the residual magnetic field also has been *** with open-loop compensation,the PID closed-loop compensation reduces the average value of the residual magnetic field in the z-axis direction from 0.0244nT to-0.0023nT,and the mean-square error from 0.2083 nT to 0.0691 *** the same way,the average value of the residual magnetic field in the y-axis direction is reduced from 0.0816nT to-0.0042nT,and the mean-square error from 0.1316nT to 0.0461 *** magnitude of residual magnetic fields in both directions is decreased to the order of picotesla(pT).In addition,based on the signal waveforms of the magnetometer,a method of verifying the effect of magnetic compensation is proposed.

关键词： magnetometer parametric resonance nuclear magnetic resonance gyroscope magnetic compensa-tion proportion-integration-differentiation(PID)

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Research on surface defect detection of aerospace electronic components based on machine vision 3

Research on surface defect detection of aerospace electronic...

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3rd International Conference on Optics and Communication technology, ICOCT 2023

作者： Zhang, Weiwei Pei, Zhonghai Liu, Kairong Lu, Jinglin Shanghai Aerospace Control Technology Institute Shanghai China Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai China

ISBN: (纸本)9781510672529

Aiming at the problems of low detection efficiency and high labor cost for manual aerospace electronic components surface, this paper proposes a surface defect detection method based on machine vision. Firstly, based on the design of the components surface defect detection device, the characteristics of the surface image are analyzed. Then, the image is preprocessed by bilateral filtering algorithm and morphological operation, and the edge is removed while the noise is removed. Finally, the fast adaptive threshold is adopted. Segmentation to achieve defect detection on the components surface. The experimental results show that the detection method has a good detection effect on the surface of the components. The detection rate and false detection rate are 3.2% and3.8%, respectively, which provides a feasible solution for the detection of defects on the surface of aerospace electronic products. Has a certain practical value. © 2023 SPIE.

关键词： Computer vision

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Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 996-1006页

作者： Shenquan Wang Wenchengyu Ji Yulian Jiang Yanzheng Zhu Jian Sun College of Electrical and Electronic Engineering Changchun University of TechnologyChangchun 130012China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Key Laboratory of Complex Systems Intelligent Control and Decision Beijing Institute of TechnologyBeijing 100081China

This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying *** introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic *** developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular ***,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre *** a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay ***,the superiority of our results is illustrated through three numerical examples.

关键词： Equivalent reciprocal combination technique quadratic function convex approximation approach stability timevarying delay

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Edge Gradient Exploration Attack based on Graph Neural Networks 24

Edge Gradient Exploration Attack based on Graph Neural Netwo...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Yang, Shuxin Ge, Ran Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-structured tasks due to their powerful graph representation learning capabilities. By aggregating and transforming information from the neighboring nodes, GNNs explore the structure of the graph and the features of the nodes. However, recent theoretical and empirical analyses have revealed that GNNs are particularly vulnerable to adversarial attacks. Among these, gradient-based attacks have been widely studied for their inherent efficiency and effectiveness. Nevertheless, due to the discrete nature of graph data, gradient-based attacks often result in suboptimal outcomes. Furthermore, the portability of some previously proposed attack algorithms requires further improvement. To address these two issues, we propose a novel Multi-Network Exploratory Attack (MNEA) aimed at enhancing gradient-based graph perturbations and improving their transferability. MNEA consists of two phases: the generation phase and the reconstruction phase. In the generation phase, multiple networks are utilized to generate a variety of candidate edges, preventing the generated perturbed edges from being too targeted toward a single model, which could lead to decreased transferability. The reconstruction phase introduces multiple edges for recombination, with the objective of avoiding misleading information potentially provided by the maximum gradient. Experimental results that are conducted on three publicly available benchmark datasets demonstrate that MNEA significantly outperforms baseline methods in terms of performance. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Kolmogorov-Arnold Networks for Enhancing Gravitational Wave Glitch Classification Performance 24

Kolmogorov-Arnold Networks for Enhancing Gravitational Wave ...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Cai, Yiting Ma, Cunliang Xiao, Wanhai Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Detecting gravitational waves is essential for the success of the LIGO and Virgo experiments, requiring thorough comprehension of how these instruments are affected by environmental and instrumental noise. In particular, the data recorded by the LIGO/Virgo detectors frequently include transient noise events, referred to as"glitches". These glitches can interfere with real gravitational wave signals and, in some cases, their characteristics may resemble those of actual gravitational wave signals, leading to misidentification. Identifying and differentiating these glitches plays a vital role in improving the precision and dependability of gravitational wave observations. LIGO’s initial observation of gravitational waves marked a transformative achievement in expanding our comprehension of the universe. However, because of the detectors’ exceptional sensitivity, they also pick up many other noise sources that can interfere with the observation of gravitational waves. To address this challenge, numerous classification methods have been proposed;however, as model complexity increases, so does the risk of overfitting, requiring more computational resources and resulting in significantly longer training and inference times. The Kolmogorov-Arnold Network (KAN), with its unique high-dimensional mapping and powerful feature extraction capabilities, demonstrates greater robustness and accuracy than traditional methods, thus improving classification performance. In this paper, we explore multi-view classification of gravitational wave glitches using KAN networks, where the linear and convolutional layers of conventional neural networks are replaced with KAN-based structures, and residual connections are incorporated within the KAN modules. © 2024 Copyright held by the owner/author(s).

关键词： Gravitational effects

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Temporal Dynamic Frequency Autoencoder: Enhancing the Performance of Unsupervised Dimensionality Reduction for Gravitational Wave Glitch Clustering 24

Temporal Dynamic Frequency Autoencoder: Enhancing the Perfor...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Xiao, Wanhai Ma, Cunliang Cai, Yiting Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

With the ongoing advancements in gravitational wave detection technology, the results are becoming increasingly precise. However, it is precisely this high precision that has led to the detection of noise signals that should not have been recorded, which are then mixed with real gravitational wave signals. As a result, efficient and accurate algorithms are needed to distinguish between these signals and noise. Past research has mostly relied on fully supervised or semi-supervised learning methods to classify and cluster these glitches, thereby improving the accuracy and reliability of glitch detection. However, neural networks typically require a large amount of training data to perform well, and in future work, it is impractical to manually label a vast number of experimental samples. Additionally, as time progresses, new unlabeled glitches may emerge. To address this issue, we adopted a Temporal Dynamic Frequency Autoencoder (TDFAE) based on a Kolmogorov-Arnold Network (KAN) that integrates Vision Transformers (ViT) and a four-branch encoder. To enhance feature extraction accuracy, we utilized a multi-branch fusion approach with the CLS (Class) token. This innovative unsupervised learning technique focuses on dimensionality reduction and clustering of gravitational wave glitches. Our approach, tested on the GravitySpy O3 dataset, outperforms current semi-supervised learning methods in clustering tasks. Additionally, our work on unsupervised clustering methods for traditional LIGO datasets offers significant potential, representing a key advancement in gravitational wave research. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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control of 2-D Semi-Markov Jump Systems:A View from Mode Generation Mechanism

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 258-260页

作者： Yunzhe Men Jian Sun Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems the School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

Dear Editor,Two-dimensional(2-D) systems have wide applications in image data processing,gas absorption and fluid dynamics analysis [1]-[3].When there exist abrupt changes in 2-D systems,they are usually modeled by 2-D Markov jump systems(MJSs) or 2-D semi-Markov jump systems(SMJSs).This letter investigates the control of 2-D SMJSs based on a novel mode generation mechanism,which could avoid mode ambiguousness phenomenon caused by the evolution of system mode in two different *** criterion that guarantees the almost surely exponential stability of the system is obtained.A thermal process is studied to demonstrate the availability of the proposed method.

关键词： Markov mode system

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Dynamic Modelling of Satellite with High Inertia and High-speed Rotating Payload 3

Dynamic Modelling of Satellite with High Inertia and High-sp...

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3rd International Conference on control Theory and Applications, ICoCTA 2023

作者： Xu, Lijia Li, Guiming Hu, Yong Chen, Bin Beijing Institute of Control Engineering Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing China

ISBN: (纸本)9781839539824

Consider a class of satellite with rotating payload that has high moment of inertia and high rotation speed. In general, the ideal condition of perfect symmetry cannot be satisfied for the rotating payload. It is recognized in this paper that the rotation of the payload will cause the change of the satellite's centre of mass. For this special type of satellite, a general form of dynamics has been established by complex modelling. Based on the mathematical model, the disturbance torques generated by the rotating payload are decomposed into six major categories and analysed in detail. Finally, the results of the analysis are directly verified by mathematical simulations. © The Institution of Engineering & technology 2023.

关键词： Satellites

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Monocular Depth Estimation Algorithm Guided by Global Structure and Local Details 24

Monocular Depth Estimation Algorithm Guided by Global Struct...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Wang, Wenhao Ma, Cunliang Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

In the current encoder-decoder architecture for monocular depth estimation, there are issues with unclear structural details of depth scene objects during the decoder’s upsampling process, as well as insufficient capability to extract contextual information from the scene. To address these problems, this paper proposes a monocular depth estimation algorithm guided by global structure and local details. The algorithm utilizes a pre-trained Swin-Transformer as the encoder to downsample the monocular image and extract multi-scale features from different levels of encoder representations. During the decoder phase, high-level features are fused with low-level features to create feature guidance maps enriched with structural semantic and positional information. These maps are then refined layer by layer with local detail feature maps output from a context aggregation module. This process ensures the model retains more global and local information during the multi-scale feature fusion. Finally, global structure guidance maps and local detail guidance maps from all layers are combined to predict the final depth map. Extensive experimental results on both indoor and outdoor datasets demonstrate the superiority of the proposed model. Compared to the AdaBins algorithm, our approach reduces RMSE by 10.46% and 7.41% on the KITTI and NYU Depth v2 datasets, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Decoding

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Non-Line-of-Sight Identification and Localization Using UWB Based on Deep Learning 24

Non-Line-of-Sight Identification and Localization Using UWB ...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Huang, Kangni Yu, Meng Song, Penghui Long, Keliu Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Due to its characteristics of high precision, high reliability, and low power consumption, UWB technology is widely used in indoor positioning. However, the performance of UWB positioning systems can be affected by issues such as multipath effects and signal attenuation in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. This paper proposes an algorithm for NLOS and LOS state identification and localization using UWB, based on deep learning. First, a recognition model suitable for NLOS and LOS environments is established by analyzing UWB channel models and signal characteristics. Then, a method for identifying NLOS and LOS states is designed using deep learning techniques, with classification accuracy metrics introduced to evaluate model performance. To validate the proposed positioning algorithm, a UWB node positioning system is designed and tested in real environments. Experiments were conducted in two different environments-a basement and a lounge-to verify the positioning performance. Subsequently, the deep learning-based NLOS error identification and mitigation algorithm proposed in this paper was tested. The experiments compared the effects of different deep learning algorithms on NLOS error identification and mitigation. Results show that the positioning algorithm using the Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) model performs best in identifying and mitigating NLOS errors, achieving the highest positioning accuracy. The study results demonstrate that the proposed algorithm not only has theoretical value but also shows great potential for practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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