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A Seamless Pedestrian Localization System Based on GNSS/IμUWB/MAP Integration

IEEE Internet of Things Journal 2025年第11期12卷 16653-16667页

作者： Zhang, Shuning Tang, Hongjing Chen, Liangming Gao, Yufeng Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Pedestrian localization has broad applications. However, achieving seamless positioning in complex environments remains a significant challenge due to factors like environmental occlusion and indoor-outdoor transitions. This paper proposes a combined localization framework using Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and MAP data to address this issue. In the framework, a three-step GNSS localization method is proposed to enhance accuracy and stability by eliminating Non-Line-of-Sight (NLOS) effects and filtering anomalies. Inertial Navigation System (INS) and Pedestrian Dead Reckoning (PDR) models are integrated to improve local precision and stability. UWB, known for its superior localization capabilities, has been integrated into some smartphones and is promising for large-scale indoor applications. The paper presents a gradient descent-based optimal anchor deployment algorithm and localization methods for varying anchor quantities. Additionally, a non-stationary Markov model fuses map data for trajectory prediction and correction. Finally, anomaly detection filtering and low-pass filtering are applied to the corrected trajectories, yielding high accuracy and stability. Experiments on the Honor V20 and iQOO U3 smartphones showed significant improvements compared to the baseline algorithm. Outdoor Root Mean Square Error (RMSE) was reduced by 44.4% (10.83m) for the Honor V20 and 40.0% (10.77m) for the iQOO U3. Additionally, outdoor Standard Deviation (STD) improved by 43.9% (4.80m) and 43.5% (5.50m), respectively. In transitional areas, RMSE improved by 4.61% (10.77m) and 19.6% (4.76m), respectively. Indoor RMSE also showed improvements, with the Honor V20 reducing it by 8.00% (3.79m) and the iQOO U3 by 5.78% (3.91m). © 2025 IEEE.

关键词： Location awareness Global navigation satellite system Accuracy Pedestrians Filtering Sensors Prediction algorithms Predictive models Smart phones Kalman filters

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Fabrication of Zn-0.5Mn-0.05 Mg Micro-Tube with Suitable Strength and Ductility for Vascular Stent Application

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Acta Metallurgica Sinica(English Letters) 2025年第2期38卷 327-337页

作者： Dongfang Lou Mingda Zhang Yuping Ren Hongxiao Li Gaowu Qin Key Laboratory for Anisotropy and Texture of Materials(Ministry of Education) School of Materials Science and EngineeringNortheastern UniversityShenyang 110819China Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China Research Center for Metallic Wires Northeastern UniversityShenyang 110819China State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China

Biodegradable Zn alloys are a hot topic in the biodegradable vascular stent materials,the study on preparing Zn alloys microtubes for vascular stent is still insufficient and none of the tubes could well meet the mechanical requirement by *** this study,we fabricate Zn-0.5Mn-0.05 Mg microtube with outer diameter of 3.5 mm and wall thickness of 0.2 mm by five passes three-roller *** mechanical properties of the microtube are 277±2.9 MPa,330±3.3 MPa and 39.8±5.25%for the yield tensile strength(YTS),ultimate tensile strength(UTS)and break elongation,respectively,which well satisfies the requirements of the vascular *** leading factors to the variations of the mechanical property are texture evolution,grain size refinement and microstructure uniformity during the rolling process,and the main deformation mechanisms are twinning and dislocation *** a result of the dynamic recrystallization,the grain size decreases obviously and the microstructure gets more uniform as the rolling pass *** texture gradually changes from a basal texture<0110>‖ED(extrusion direction)for the as-extruded tube blank to deformation texture<0001>‖RD(rolling direction)and non-basal texture<0221>‖RD for the 5-pass rolled microtube.

关键词： Zn alloys Microtube Microstructure Mechanical property Three-roller rolling

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AMA-Net: Adaptive Masking Attention Network for Agricultural Crop Classification From UAV Images

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Sampled-Data Consensus for Multiagent Systems With Open Topology and Packet Loss

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3130-3142页

作者： Zhu, Haitao Lu, Jianquan Liu, Yang Zhang, Lingzhong Southeast University School of Mathematics Nanjing210096 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application the School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Hangzhou School of Automation Hangzhou311231 China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu215500 China

This article studies a more realistic issue in multiagent systems (MASs) named open topology, where the network scale tends to be variable as agents may join, leave, or be replaced. In order to reveal the influence that the variation of network scale brings on the dynamic behavior of MASs, a novel transition process containing impulse is provided. Then by applying the Halanay-like inequality, the distributed sampled-data control is designed to ensure the consensus property of the MASs under open topology. It is shown that the derived results can degenerate into the consensus criteria for general MASs with fixed-scale topology. Also, the potential packet loss phenomenon existing in controller is considered, which is integrated into the consensus performance via the average packet loss interval (APLI) condition, thereby guaranteeing the robustness of consensus performance against packet loss. Moreover, within the framework of open topology, it is proved that the admissible packet loss for MASs can be estimated in an average sense. At the end, the availability of this article is demonstrated by discussing a numerical example. © 2013 IEEE.

关键词： Network topology

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An Enhanced Model-Free Predictive Current control for PMSMs Incorporating Discrete Space Vector Modulation 19th

An Enhanced Model-Free Predictive Current Control for PMSMs ...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yang, Yuhui Qin, Qing Qin, Yingmei Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9789819613823

Model-free predictive current control (MFPCC) has excellent parameter robustness owing to unused parameters of the controller. However, the method has the current gradient update stagnation problem due to the limited availability of only 7 voltage vectors, which results in a high current/torque ripple. To address this shortcoming, an enhanced MFPCC for permanent magnet synchronous machines (PMSMs) incorporating discrete space vector modulation (DSVM) is proposed in this paper. First, the basic voltage vectors are expanded from 7 to 37 by DSVM. Then, a novel current gradient updating method is developed to effectively reduce the current/torque ripple caused by current gradient updating stagnation. Finally, a candidate vector preselection method is designed to simplify the calculation process of current gradient updating. The effectiveness of the proposed method is verified on a 2 kW PMSM driving experimental platform. The experimental results show that the THD is reduced from 25.5% to 7.8% while the dq-axes current ripple is respectively reduced from 5.1 A and 5.8 A to 2.2A and 1.5 A. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Design and Performance Test of an H-shaped Bionic Piezoelectric Robot Based on the Standing Wave Principle

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Journal of Bionic Engineering 2025年第2期22卷 608-625页

作者： Ying Li Chaofeng Li Zhiwei Wu Binbin Zhu Jinghu Tang School of Mechanical Engineering and Automation Northeastern UniversityShenyang110819People’s Republic of China Key Laboratory of Vibration and Control of Aero-propulsion Systems Northeastern UniversityShenyang110819People’s Republic of China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao999078People’s Republic of China

In this paper,inspired by the running motion gait of a cheetah,an H-shaped bionic piezoelectric robot(H-BPR)based on the standing wave principle is proposed and *** piezoelectric robot realizes linear motion,turning motion,and turning motion with different radi by the voltage differential driving method.A prototype with a weight of 38 g and dimensions of 150×80×31 mm^(3) was ***,the dynamics and kinematics of the piezoelectric robot were analyzed to obtain the trajectory of a point at the end of the piezoelectric robot *** motion principle of the piezo-electric robot was analyzed,and then the piezoelectric robot's modal analysis and harmonic response analysis were carried out using finite element analysis ***,an experimental setup was built to verify the effectiveness and high efficiency of the robot's motion,and the effects of frequency,voltage,load,and height of the driving leg on the robot's motion performance were *** performance test results show that the piezoelectric robot has a maximum veloc-ity of 66.79 mm/s at an excitation voltage of 320 V and a load capacity of 55 *** addition,the H-BPR with unequal drive legs has better climbing performance,and the obtained conclusions are informative for selecting leg heights for piezoelectric robots.

关键词： Piezoelectric Standing wave principle Bionic Differential driving method Robot

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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MFDFormer: A Unified Multiscale Frequency Domain MetaFormer Framework for EEG-based Chronic Pain Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 16511-16524页

作者： Qiu, Shihan Xie, Kan Yang, Junjie Yang, Qiyu Zhang, Bo Gu, Yuhong Xie, Feng Qiu, Rongjian Xie, Shengli Bai, Wenfang Guangdong University of Technology School of Automation China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology Guangzhou510006 China School of Automation Guangdong University of Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Guangzhou510006 China Guangzhou510080 China

Wearable electroencephalogram (EEG) devices have shown great potential in enabling real-Time monitoring of subtle changes in brain activity, providing new possibilities for the assessment and management of chronic pain. However, recognizing pain-related biomarkers from EEG data remains a complex, multi-Task challenge. Most existing research focuses on single-Task approaches and rarely addresses this issue within a unified framework. In this paper, we propose a novel deep neural network (DNN) model called multiscale frequency domain MetaFormer (MFDFormer), which is designed to simultaneously predict the presence, type, and intensity of chronic pain. The proposed MFDFormer comprises two primary sub-networks: multiscale feature extractor (MFE) and frequency domain MetaFormer (FDFormer) encoder. The MFE extracts diverse EEG features through convolutions with different kernel sizes, while a self-Attention mechanism is integrated into MFE to emphasize the importance of inter-dependency among these features. The FDFormer encoder refines the output MFE features using causal convolutions to capture local patterns and projects them into a higher-dimensional representation domain. Additionally, it incorporates spatial and temporal frequency domain learners in parallel to effectively capture the spatial-Temporal information of EEG data. Based on the publicly available BFCP dataset, the proposed MFDFormer demonstrates superior performance over state-of-The-Art algorithms, achieving accuracies of 97.59%, 96.02%, and 83.48% in pain or non-pain state classification (P/NSC), pain type classification (PTC), and pain intensity classification (PIC) tasks, respectively. This paper proposes a unified, end-To-end DNN-based framework for multi-Task chronic pain recognition, providing a reliable solution with the potential to advance pain diagnosis and management in IoT and smart wearable applications. © 2014 IEEE.

关键词： Electroencephalography

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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