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Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2248-2257页

作者： Kuan Li Hao Luo Yuchen Jiang Dejia Tang Hongyan Yang IEEE Shanghai Aerospace Control Technology Institute Shanghai 201109China the Control and Simulation Center School of AstronauticsHarbin Institute of TechnologyHarbin 150006China the Faculty of Information Technology Engineering Research Center of Digital CommunityMinistry of EducationBeijing University of TechnologyBeijing 100124China

This paper presents a subspace identification method for closed-loop systems with unknown deterministic *** deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are *** closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique *** addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

关键词： Bernstein polynomial closed-loop system subspace identification unknown deterministic disturbances

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Influences of Inhomogeneous Spatial Distribution of Global Oceanic Atmospheric Refractivity on LF Groundwave Propagation Delay

Influences of Inhomogeneous Spatial Distribution of Global O...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Wang, Dan-Dan Lu, Qi-Zirui Han, Chao Pu, Yu-Rong Yang, Rui-Hong Bai, Pei-Rui Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao China Xi'an University of Technology Faculty of Automation and Information Engineering Xi'an China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350355581

Atmospheric refractivity affects the propagation delay of low-frequency (LF) groundwave in the realistic ocean environment. In this paper, a parabolic equation (PE) model based on the ERA5 meteorological reanalysis dataset is applied to analyze the influences of inhomogeneous spatial distribution of global oceanic atmospheric refractivity on LF groundwave propagation delay. The global oceanic atmospheric refractivity is derived from the meteorological quantities given by the ERA5 reanalysis dataset. It is found that the global oceanic surface atmospheric refractivity varies systematically with latitude in the range of 290∼300 N-unit to 380∼400 N-unit, which in turn leads to the variation of LF groundwave propagation delay reaching hundredth nanosecond for 1000 km-long propagation path. © 2024 IEEE.

关键词： global oceanic atmospheric refractivity low-frequency (LF) groundwave propagation parabolic equation (PE)

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Fractional order PID control of quadrotor UAV based on SA-PSO algorithm 3

Fractional order PID control of quadrotor UAV based on SA-PS...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Shao, Keyong Liu, Jiaxin Li, Mingxue Department of Control Theory and Control Engineering Faculty of Electrical and Information Engineering Northeast Petroleum University Heilongjiang Daqing China Department of Control Science and Engineering Faculty of Electrical and Information Engineering Northeast Petroleum University Heilongjiang Daqing China Department of Control Engineering Faculty of Electrical and Information Engineering Northeast Petroleum University Heilongjiang Daqing China

ISBN: (纸本)9781510661240

The stability control of quadrotor UAV system is a challenging problem due to its nonlinearity, strong coupling and underactuation characteristics. In this paper, a fractional order PID control strategy (FOPID) is proposed to realize the high-precision control of UAV system by utilizing the multi-degree of freedom characteristic of FOPID, SA-PSO is used to tune the parameters of FOPID. Compared with the standard PSO optimization process, the convergence speed is faster and the accuracy is higher. Simulation results show that the proposed PSO parameter optimization based on SA algorithm makes FOPID controller have better performance than traditional integer order PID controller. © 2022 SPIE.

关键词： Simulink

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Generic model of IoT Edge device for object monitoring via LoRaWAN

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IFAC-PapersOnLine 2024年第9期58卷 160-165页

作者： Peter Peniak Emília Bubeníková Peter Holečko Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Slovakia

This article deals with the generic model for object monitoring and tracking in an industrial environment and manufacturing. We focus on the design of generic IoT Edge device model that enables the integration of various LoRa sensors independently on vendor solutions for common use cases. Our model is based on the MQTT protocol, which is proven for sensor applications and gives needed independency on proprietary protocols. The model was implemented in Node-RED software and subsequently validated with sensors of different vendors to confirm the generic approach and base for independent object tracking solutions. The proposed Edge device model supports independent object tracking, such as object status monitoring, localization and triggered events, giving independent approach for tailored solutions and wide application portfolio based on combined different sensors and devices.

关键词： LoRaWAN GPS sensors monitoring tracking generic model MQTT NodeRED

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Event-Based Adaptive control for MIMO Nonlinear Systems Against Deception Attacks via a Single Parameter Learning Method 7th

Event-Based Adaptive Control for MIMO Nonlinear Systems Aga...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Tian, Yongjie Zhao, Ning Zhao, Xudong College of Control Science and Engineering Bohai University Jinzhou121013 China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Liaoning Dalian116024 China

ISBN: (纸本)9789819733279

This article proposes an adaptive neural network (NN) control scheme for MIMO nonlinear systems with unknown deception attacks. Based on the dynamic surface technology and the backstepping method, the unknown term composed of the sensor attack signals, the error term generated by NN and the disturbance can be regarded as an uncertain parameter. In addition, by combining the adaptive technology with the single parameter learning method (SPLM), the uncertain parameter will be transformed into a linear parameterized form with only one unknown uncertain parameter, thereby significantly reducing the computational complexity in the recursive process. Furthermore, the event-triggered control strategy with dynamic characteristics can be integrated into the control design to reduce the transmission of data. Theoretical analysis demonstrates that the proposed control algorithm ensures boundedness of all signals in the closed-loop system, and the Zeno phenomenon is eliminated. Ultimately, a numerical simulation is employed to validate the effectiveness of the proposed NN adaptive control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Closed loop systems

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Performance of GNSS positioning in PPP mode using MADOCA precise products

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Geodesy and Geodynamics 2024年第6期15卷 642-651页

作者： Brian Bramanto Rachel Theresia Irwan Gumilar Sidik T.Wibowo Geodetic Science Engineeringand Innovation Research GroupFaculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodesy and Geomatics Engineering Study Program Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodetic and Geodynamic Control Network Center Geospatial Information Agency of Indonesia(BIG)Cibinong 16911Indonesia

The Global Navigation Satellite System (GNSS) is widely utilized for accurate *** commonly applied method to obtain precise coordinate estimates is by implementing the relative positioning in network ***,this approach can be complex and ***,The Japan Aerospace Exploration Agency (JAXA) offers freely available satellite orbit and clock correction products called Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA),which can enhance positioning accuracy through the precise point positioning (PPP) *** study focuses on evaluating PPP static mode positioning using MADOCA products and comparing the results with the highly precise relative positioning *** analyzing a network of 20 GNSS stations in Indonesia,we found that the PPP method using MADOCA products provided favorable positioning *** median discrepancies and the corresponding median absolute deviation (MAD) for easting,northing,and up components were estimated as 9±18 mm,10±9 mm,and 3±40 mm,*** results indicate that PPP with MADOCA products can be a reliable alternative for establishing Indonesia's horizontal control networks,particularly for orders 0,1,2,and 3,and for a broad spectrum of geoscience monitoring ***,considerations such as epoch transformations and seismic activities should be taken into account for accurate positioning applications that comply with the definition of the national reference framework.

关键词： Global Navigation Satellite System(GNSS) Precise point positioning(PPP) MADOCA Positioning evaluation

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Synthesizing PET Scans Using Attention GAN Based on MRI Scans and Alzheimer's Disease information 23

Synthesizing PET Scans Using Attention GAN Based on MRI Scan...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Guo, Qing Zhang, Jianjun Liu, Peishun Liu, Xianchang Wang, Qinshuo Tang, Ruichun Faculty of Information Science and Engineering Ocean University of China China Qingdao Center for Disease Control and Prevention China

ISBN: (纸本)9798400708237

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) can facilitate the diagnosis of Alzheimer's disease (AD). But PET data are not always available due to high financial costs and some other reasons. A common research approach to this problem is to filter out incomplete data, which will reduce the sample size. In order to solve the problem of missing PET data, we propose a new attention GAN model AMIGAN, which learns an end-to-end mapping function to generate PET scans from MRI and disease clinical information. We design a 3D attention multi-scale convolutional U-Net generator architecture and implement a pyramidal convolutional attention module PCAM for the generator skip connection process. PCAM can focus on image detail and improve the visual quality of the synthesized PET scans. Moreover, two discriminators are built for image authenticity discrimination and disease information category discrimination respectively, and disease information is integrated into the generated image. In addition, the standard adversarial loss is supplemented with structural similarity loss, reconstruction loss and disease information category loss components, which reduce the reconstruction error and enhance structural consistency across different angles. Experiments show that AMIGAN has better image generation performance than previously proposed methods. © 2023 ACM.

关键词： Generative adversarial networks

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Real-Time UAV Fault Detection and Classification Using Measurement Data from the PADRE Database

Real-Time UAV Fault Detection and Classification Using Measu...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Puchalski, Radoslaw Giernacki, Wojciech Ha, Quang Institute of Robotics and Machine Intelligence University of Technology Faculty of Control Robotics and Electrical Engineering of Poznan Poznan Poland School of Electrical and Data Engineering University of Technology Sydney Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350312072

The growing number of applications for unmanned aerial vehicles operating in close proximity to humans calls for strict safety requirements. To ensure reliability and safety, fast and effective diagnosis of possible drone faults is needed. In this paper, a study on the detection of damage to the propellers of a multirotor using inertial sensors was conducted. Measurement data from the publicly available PADRE repository were used. Frequency features and raw measurement data applicability were tested. Two different approaches to data processing during fault detection and classification were tested. The first is to analyze the signals after collecting the entire set of data needed for treatment by the neural network. The second approach processes the data after every single acquisition of sensor measurements. The performance results and processing time of each solution are recorded for analysis. By effectively selecting parameters of the proposed approaches, the processing time and accuracy of UAV fault classification can be significantly improved, as verified over different classes of propeller defects. Early fault detection is essential for safe operations of multirotor drones. © 2024 IEEE.

关键词： Drones

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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