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Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

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Bias-Eliminated Semantic Refinement for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Feng, Liangjun Zhao, Chunhui Li, Xi The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China

When training samples are scarce, the semantic embedding technique, i.e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i.e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning;e.g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive control

arXiv

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arXiv 2023年

作者： Zhang, Baozhe Chen, Xinwei Li, Zhehan Beltrame, Giovanni Xu, Chao Gao, Fei Cao, Yanjun Huzhou Institute of Zhejiang University Huzhou313000 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Chinese University of Hong Kong Shenzhen518172 China Department of Computer Engineering and Software Engineering Poly-technique Montreal Canada

This paper presents a novel solution for UAV control in cooperative multi-robot systems, which can be used in various scenarios such as leader-following, landing on a moving base, or specific relative motion with a target. Unlike classical methods that tackle UAV control in the world frame, we directly control the UAV in the target coordinate frame, without making motion assumptions about the target. In detail, we formulate a nonlinear model predictive controller of a UAV, referred to as the agent, within a non-inertial frame (i.e., the target frame). The system requires the relative states (pose and velocity), the angular velocity and the accelerations of the target, which can be obtained by relative localization methods and ubiquitous MEMS IMU sensors, respectively. This framework eliminates dependencies that are vital in classical solutions, such as accurate state estimation for both the agent and target, prior knowledge of the target motion model, and continuous trajectory re-planning for some complex tasks. We have performed extensive simulations to investigate the control performance with varying motion characteristics of the target. Furthermore, we conducted real robot experiments, employing either simulated relative pose estimation from motion capture systems indoors or directly from our previous relative pose estimation devices outdoors, to validate the applicability and feasibility of the proposed approach. © 2023, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A Traffic Signal control Method Based on Improved Deep Reinforcement Learning*

A Traffic Signal Control Method Based on Improved Deep Reinf...

引用

Chinese Automation Congress (CAC)

作者： Ye Bao-Lin Chen Dong Wu Peng Wu Weimin Lingxi Li Chen Bin School of Information Science and Engineering Jiaxing University Jiaxing China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette USA Jiaxing Key Laboratory of Smart Transportations Jiaxing University Jiaxing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In order to solve the problem that the existing traffic signal control methods usually need to predefine the phase sequence or phase duration, a single intersection traffic signal control method based on improved deep reinforcement learning Double DQN (DDQN) was proposed. Firstly, a traffic signal control model based on deep reinforcement learning DDQN was constructed to optimize the estimated value of action-value function and the iterative process of target value. Secondly, the original one-dimensional action space was discretized into a three-dimensional action space composed of phase selection, phase duration and loss factor, and the next phase and phase green time to be executed were intelligently decided. Finally, a new loss function is designed based on the loss factor, which can dynamically adjust the loss weight of different actions according to the difference of importance. The simulation test is carried out based on the microscopic simulation platform SUMO. The experimental results show that, compared with the baseline method, the proposed method reduces the average waiting time by 28.1%, the average queue length by 29.7%, and the average number of parking vehicles by 18.8%.

关键词： Training Microscopy Roads Neural networks Process control Aerospace electronics Traffic control Deep reinforcement learning Feature extraction Data models

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A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

A Model-based Approach for Indoor Localization Leveraging Si...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yaoxin Duan Rongbin Hu Zexing Liu Yuanyi Zhang Wendi Nie Kam-Yiu Lam Chun Jason Xue Yongli Song Guan Gui Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Computer Science Department City University of Hong Kong Hong Kong PRC Beijing Institute of Computer Technology and Applications China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signal-based indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFi-based localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.

关键词： Location awareness Accuracy Sensitivity Fingerprint recognition Sensors Trajectory Indoor environment Wireless fidelity Monitoring

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Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

Joining Edge-Enabled Metaverse Services with Network Externa...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Xu, Minrui Deng, Ruilong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350333336

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect. © 2023 IEEE.

关键词： Edge computing

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Sinusoidal-Assisted Synchrosqueezing Transform: Algorithms and Biomedical Applications

SSRN

引用

SSRN 2023年

作者： Chen, Tao Zheng, Qian Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Flexolink Technology Co. Ltd. Hangzhou China

Intermittent oscillation signals (IOSs) exist widely in biomedical systems. They are always contaminated by various randomness like noise and artifacts. Compared with traditional time-frequency analysis (TFA) methods, adaptive TFA methods show the significant advantages in separating IOSs. However, these methods are implemented by assuming that the signal is in a weakly noisy environment and have narrow-band limitation. Besides, it is difficult for these methods to alleviate the noise in the gap among time where IOSs happen. Therefore, in this paper, a new method called the sinusoidal-assisted synchrosqueezing transform (SASST) is proposed for separating IOSs in strongly noisy environment. The SASST method locates the IOSs with the aid of a sinusoidal signal. Such a sinusoidal signal fills the energy blank in the gap between time where IOSs happen. This enables that the ridge detection approach, which is the necessary tool for mode retrieval of SST-based methods, focuses on the assisted sinusoidal signal, rather than noise. Thus, the background noise in the gap is suppressed. Moreover, this sinusoidal-assisted framework can be introduced into any SST-based TFA methods. Depending on the characteristics of different biomedical signals, different SST-based methods are chosen to achieve better separation. As a result, SASST is capable of extracting both narrow- and wide-band IOSs in a strongly noisy environment. The effectiveness and advantages of the proposed algorithm are verified by both numerical simulations and real-world cases. Even in a strong noise environment of -10dB, SASST achieve output SNR of 12.66dB and 10.53dB in narrow- and wide-band simulation, respectively. © 2023, The Authors. All rights reserved.

关键词： Medical applications

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

引用

arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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