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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Zhao, Haoli Li, Zhenni Chen, Ci Wang, Lujia Xie, Kan Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

Obtaining high-accuracy vehicle localization is essential for safe navigation in automated vehicles, however, complex urban environments result in inaccurate positioning because of multipath and non-line-of-sight errors, making precise positioning in complex environments an important and unsolved problem. Different from conventional model-based approaches which require rigid assumptions on sensors and noise models, data-driven artificial intelligent approaches can provide new solutions for Global Navigation Satellite System (GNSS) positioning problems in multipath environments. Recent learning-based works employ the generalization ability of the deep neural network to model these complex errors in urban environments, and provide effective positioning corrections for rough GNSS localizations by learning from a vehicle trajectory-based Reinforcement Learning (RL) environment. However, existing RL-based approaches employ insufficient features as input, and cannot take advantage of the vehicle trajectory *** tackle these issues, we propose a novel Deep Reinforcement Learning-based (DRL) positioning correction framework with fused features considering the vehicle trajectory information and GNSS features in the multi-input *** help the agent learn the correction policy efficiently with comprehensive inputs, we develop a new positioning correction RL environment with multi-input observations, which augments GNSS device readings of different satellites as complementary to the vehicle trajectory, and moreover employ a correction advantage reward to help the agent learn an efficient policy in positioning correction. To fully utilize comprehensive multi-input observations in the proposed environment, we employ the LSTM module to separately extract features to fuse brief states from input time series data. Finally, to address the long-term positioning accuracy, we construct the learning model based on actor-critic DRL structure with the cumulative reward

关键词： Statistical tests

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MineLoRa: Markov Transition Fields and Adaptive Neural Networks for LoRa Device Radio Frequency Fingerprint Identification

MineLoRa: Markov Transition Fields and Adaptive Neural Netwo...

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

作者： Zhu, Biaokai Jia, Yupeng Wang, Zhonghao Wen, Jiawei Liu, Xinle Zhao, Shijia Xu, Qiongyue Song, Jie Li, Ping Liu, Sanman Li, Feng Zhao, Jumin Shanxi Police College Taiyuan University of Technology Taiyuan China Shanxi Police College Cyber Security Department Taiyuan China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer Science and Technology Anhui University Hefei China Taiyuan University of Technology Taiyuan China College of Information and Computer Taiyuan University of Technology Jinzhong China

ISBN: (纸本)9798331507886

With the development of LPWAN technology, LoRa, one of the typical representatives, is widely used in various communication scenarios. At the same time, it is also faced with problems such as difficulty in identifying the terminal equipment and the easy mistake and confusion. The industry has widely valued LoRa RF fingerprint recognition technology, which has become a security focus that cannot be ignored. Most recent research on terminal recognition has been carried out by multi-class feature fusion. Still, it needs help with high computational complexity, significant noise and other external interference factors, and harrowing feature extraction. We propose a time-frequency feature analysis method based on GAF and MTF. The time-domain features are transformed into two-dimensional images using the Gram Angle field and Markov transfer field. The spectral graph has both time-domain and frequency-domain characteristics, and the feature probability distribution in the images is intuitively analyzed. Finally, we design a deep neural network model based on CNN improvement. It can accurately extract and classify the features contained in multi-Time frequency. Our experiments show that a 97% recognition rate can be achieved in an indoor environment, and a 90% recognition accuracy can be achieved in an outdoor environment in time. © 2024 IEEE.

关键词： Frequency domain analysis

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Low-noise Microwave Signal Generation by Heterodyning Two Narrow-linewidth Self-injection Locked Lasers

Low-noise Microwave Signal Generation by Heterodyning Two Na...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Fan, Yihao Siyu, E. Guo, Yuyao Li, Xinhang Lu, Liangjun Wang, Shuxiao Cai, Yan Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Shanghai Industrial μTechnology Research Institute Shanghai201800 China

We realize a hybrid integrated self-injection locking laser (SIL) with an ultra-narrow intrinsic linewidth of 5 Hz. With heterodyne synthesis using a pair of SILs, a microwave signal with a 4-kHz linewidth is achieved... 详细信息

关键词： Linewidth

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Service-Based Event Penetration from IoT Sensors to Businesses: a Case Study

Service-Based Event Penetration from IoT Sensors to Business...

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Wang, Guiling Fang, Jun Wang, Jing Yu, Jian Zhang, Liang Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

By leveraging IoT Big Data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach. © 2022 IEEE.

关键词： Internet of things

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Stability of Nonlinear systems via Hybrid Delayed Impulses

Stability of Nonlinear Systems via Hybrid Delayed Impulses

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第43届中国控制会议

作者： Huannan Zheng Wei Zhu Ya Deng School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper studies the stability problem of nonlinear systems via hybrid delayed impulses,in which stabilizing impulses and destabilizing impulses can occur simultaneously. In order to analyze the hybrid delayed impulses,the average impulsive weight(AIW) is used. Together with average impulsive interval(AII) and average impulsive delay(AID),some Lyapunov-based conditions for globally asymptotically stable(GAS) are established. Results show that delays in impulses have positive effects to the desired performance. The effectiveness of the theoretical results is demonstrated through two numerical examples.

关键词： Stability Nonlinear systems Hybrid Delayed Impulses

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TILTING MANHOLE COVER: A NONLINEAR SPRING-MASS SYSTEM

TILTING MANHOLE COVER: A NONLINEAR SPRING-MASS SYSTEM

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ASME 2024 Pressure Vessels and Piping Conference, PVP 2024

作者： van de Meulenhof, Niels F. Tijsseling, Arris S. Vasconcelos, Jose G. Hou, Qingzhi Zafer, Bozkuş Department of Mathematics and Computer Science Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Department of Civil Engineering Auburn University AuburnAL36849 United States State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin300350 China Hydraulics Laboratory Department of Civil Engineering Middle East Technical University Ankara06800 Turkey

ISBN: (纸本)9780791888490

Manhole covers are potential "dancers". They may leave their resting state and start "dancing". They may hover, move up and down, tilt, rotate, bounce, make noise, flip over, or even fly up into the air. In general, their motion looks chaotic, probably due to the nonlinear dynamics governing the system. The authors have previously derived basic models of dancing manhole covers covering the translational vertical motion of free covers and the rotational motion of hinged covers. In the current contribution the basic model is extended with tilting (without hinge) and bouncing behavior. Some fundamental problems and assumptions are discussed. Preliminary numerical results are shown together with 3D visualizations. Scientific curiosity into a mysterious phenomenon has been the motivation for this study. The obtained equations governing the manhole cover’s motion may serve as boundary conditions in hydraulic-pneumatic models of sewer-manhole systems (think of geysering and ventilation). Copyright © 2024 by ASME.

关键词： Fluid structure interaction

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Human–Robot Coordination Control for Sit-to-Stand Assistance in Hemiparetic Patients With Supernumerary Robotic Leg

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IEEE Transactions on Automation science and Engineering 2025年 22卷 16591-16602页

作者： Jie Zuo Jun Huo Xiling Xiao Yanzhao Zhang Jian Huang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Hubei Key Laboratory of Brain-Inspired Intelligent Systems and the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China Department of Rehabilitation Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiparetic patients validate the algorithm’s effectiveness during STS. The results validate substantial reductions in STS time (39.54%) and muscle activity (28.01%), highlighting the efficacy of the proposed algorithm-controlled SRL support for hemiparetic individuals. Note to Practitioners—To prevent misalignment between the SRL and natural limb movements, which can impose additional strain on the body, this study combines the principles of joint bio-mimicry to develop a personalized human-robot coupling model, and also propose an sEMG-based adaptive impedance control algorithm. Our research fills a gap in the nuanced analysis of robot-assisted support effects on both the affected and unaffected sides of hemiparetic patients, while also possessing the potential for transfer or extension to other types of rehabilitation robotic platforms. Although the SRL shows promising results, it is important to note the limitations of a small sample size and the need for further exploration of the needs of diverse patient po

关键词： Robots Robot kinematics Limbs Impedance Legged locomotion Adaptation models Couplings Muscles Biological system modeling Automation

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Self-adjustable hyper-graphs for video pose estimation based on spatial-temporal subspace construction

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science China(Information sciences) 2022年第3期65卷 260-262页

作者： Jizhou MA Shuai LI Hong QIN Aimin HAO Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science Stony Brook University

Dear editor,In recent years many supervised video pose estimation methods have achieved growing successes based on well-labeled training datasets. Nonetheless, when facing roughly-labeled training data, it still remains challenging to intrinsically encode the video contents' spatial-temporal coherency for robust video pose *** researches aimed to directly improve and refine the existing confidence maps by combining the spatial-temporal structure models [1, 2]. Li et al.

关键词： Video Pose Estimation Self-adjustable Hyper-graph Spatial-temporal Subspace Exploration Maximum Matching Subspace Operator Action Pattern

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VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos 39

VidEvent: A Large Dataset for Understanding Dynamic Evolutio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Baoyu Su, Qile Zhu, Shoutai Liang, Yuchen Tong, Chao School of Computer Science and Engineering Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

ISBN: (纸本)157735897X

Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos. To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data. We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements. Our analysis of VidEvent and the baseline models highlights the dataset’s potential to advance video event understanding and encourages the exploration of innovative algorithms and models. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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