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Recognizing knee pathologies by using gait dynamics via kernel principal component analysis and deterministic learning theory

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15535-15543页

作者： Deng, Muqing Feng, Xiaoreng Zeng, Wei Cao, Jiuwen Zhang, Yu Zheng, Tongjia Institute of Information and Control Hangzhou Dianzi University Hangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Orthopaedics and Traumatology Queen Mary Hospital the University of Hong Kong Pok Fu Lam Hong Kong College of Mechanical and Electrical Engineering Longyan University Longyan China Department of Orthopaedic Surgery Guangzhou General Hospital of Guangzhou Military Command Guangzhou China College of Engineering University of Notre Dame Notre DameIN United States

This study concentrates on dynamical motion characteristics of knees during treadmill walking, and describes the application of kernel principal component analysis and deterministic learning theory to the detection of abnormal gait dynamics induced by knee pathologies. First, twelve-dimensional knee joint rotation and translation parameters are measured by a marker-based motion analysis system during treadmill walking. Second, the dimension of the gait parameter signal is reduced based on kernel principal component analysis. Third, gait dynamics underlying time-varying gait parameter signal is captured by using localized radial basis function neural networks through deterministic learning mechanism. This kind of dynamics information represents the frame-to-frame temporal change of kinematics and kinetics modifications during treadmill walking, which is shown to be sensitive to the variance during knee pathologies. Gait patterns are represented as the gait dynamics underlying time-varying gait parameters. The training patters under different knee pathologies further constitute a uniform training dataset, containing all kinds of gait dynamics under different knee pathologies. By comparing the training patterns with a test knee gait pattern to be recognized, a set of recognition errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test pattern. A rapid recognition scheme can be achieved according to the smallest error principle. Experimental results show that encouraging recognition accuracy can be achieved. The proposed method is sensitive for the detection of knee pathologies and is capable of discharge of patients without knee pathologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Errors

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ROBOTIC ARM TRAJECTORY TRACKING control BASED ON RBF NEURAL NETWORK ESTIMATION OF DYNAMIC PARAMETERS

ROBOTIC ARM TRAJECTORY TRACKING CONTROL BASED ON RBF NEURAL ...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Du, Nannan Yan, Liang Wang, Tiantian Bu, Suwan Gerada, Chris Liu, Xiaoshuai Yang, Xuxu Li, Haien School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Tianmushan Laboratory Beihang University Hangzhou310023 China Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom

In robotic arm operations, after planning the desired motion trajectory, accurate and stable trajectory tracking control is essential. However, in actual control, there are issues such as the uncertainty of the dynamic parameters and susceptibility to external disturbances during motion. Addressing these issues, this paper employs RBF neural networks to estimate dynamic parameters and utilizes an adaptive controller to achieve online trajectory tracking. The dynamic approximation capability and adaptability of this method enhance the real-time performance and disturbance rejection of robotic arm trajectory tracking control. Finally, the availability of this method is verified by experiments in the simulation environment. © The Institution of engineering & Technology 2024.

关键词： Disturbance rejection

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Power Loss Analysis of Energy Management System with Model Predictive control in Mae Hong Son Microgrid

Power Loss Analysis of Energy Management System with Model P...

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IEEE International Conference on Power Electronics, Intelligent control and Energy Systems (ICPEICES)

作者： Nattakit Prapanwattana Donlapat Dechawatanapaisal David Banjerdpongchai Abhishek Rajan Dept. of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation for Process Systems Chulalongkorn University Bangkok Thailand Dept. of Electrical Engineering National Institute of Technology Sikkim South Sikkim India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

Energy management system (EMS) is an important tool for energy efficiency and reliability of the power system. The optimal power dispatch of energy resources can be obtained using the nonlinear model predictive control (NMPC). It is formulated as an optimization problem subject to linear and nonlinear constraints. Although the existing system works well under the current conditions, power loss has not been considered. Power loss in transmission lines occurs due to their resistance and a high amount of power flow to loads connecting to the system. The power loss leads to decreased efficiency in the electrical system and reduces the lifespan of equipment because of the high temperature from loss. This paper aims to analyze the power loss in the Mae Hong Son (MHS) microgrid. To achieve the research objectives, we employ the EMS of MHS with NMPC and calculate power loss in transmission lines for a period of 7 days. EMS considers three objective functions, namely, total operating cost, total carbon dioxide emission, and combined economic and emission dispatch and take into account of three seasons consisting of rainy, winter, and summer.

关键词： Resistance Power transmission lines Microgrids Propagation losses Power system reliability Reliability Energy management systems Power generation Load flow Predictive control

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Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping

arXiv

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arXiv 2025年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Digital Futures United Kingdom

In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimization algorithms that exhibit efficient communication among nodes. Our first algorithm presents a simple quantized averaged gradient procedure for distributed optimization, which is shown to converge to a neighborhood of the optimal solution. Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution. It enables nodes to terminate their operation according to predefined performance guarantees. Our third algorithm is tailored to operate in environments where each message consists of only a few bits. It incorporates a novel event-triggered mechanism for adjusting the quantizer basis and quantization level, allowing nodes to collaboratively decide operation termination based on predefined performance criteria. We analyze the three algorithms and establish their linear convergence. Finally, an application on distributed sensor fusion for target localization is used to demonstrate their favorable performance compared to existing algorithms in the literature. © 2025, CC BY.

关键词： Dynamic programming

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Mapping the Optimal Sites for Offshore Wind Power Plants and Green Hydrogen Production: South and Southeast Brazilian Case Study 32

Mapping the Optimal Sites for Offshore Wind Power Plants and...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Paula, Karen De Pumalloclla, Hayro Pourakbari-Kasmaei, Mahdi Melo, Joel D. Falcao, Djalma Federal University of ABC Graduate Program in Energy Santo André Brazil Aalto University Dept. Electrical Engineering and Automation Espoo Finland Alberto Luiz Coimbra Institute for Graduate Studies and Engineering Research Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350399714

The demand for renewable energy solutions has become a global trend due to the current climate change scenario and the increased demand for electricity. Hence, in several countries, the share of renewable sources in the electric matrix has grown with the insertion of new energy sources, such as offshore wind. Brazil presents optimal conditions for the construction of offshore wind farms. The Brazilian 10-year Energy Expansion Plan 2029, in addition to placing this source as an expansion for the coming years, intensely focuses on hydrogen. For this reason, this study aims to assist planners and agents interested in running new construction projects of offshore wind farms with green hydrogen production. Such a study case seeks better use of the available wind resources in the high energy consumption regions of Brazil's South and Southeast, assessing the wind resource considering technical, environmental, social, and economic restrictions to identify the best locations for installing wind farms. As such, it processes the data within a geographic information system, and then a fuzzy logic-based model is used for the joint evaluation of constraints. Results show that Rio de Janeiro and Espírito Santo are the most favorable places. © 2023 IEEE.

关键词： Offshore wind farms

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Structural Design of Permanent Magnet Momentum Sphere for Attitude control of a Small-sized Spacecraft 26

Structural Design of Permanent Magnet Momentum Sphere for At...

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26th International Conference on electrical Machines and Systems, ICEMS 2023

作者： Fang, Zhaowei Guo, Xiwen Zhou, Rui Wang, Qunjing Zhang, Ronglin Anhui University School of Electrical Engineering and Automation Hefei China Anhui University National Engineering Laboratory of Energy-Saving Motor and Control Technology Hefei China Anhui University Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Hefei China State Grid Lu'an Electric Power Supply Company Lu'an China

ISBN: (纸本)9798350317589

With the increasing application of small spacecraft, the momentum actuator, the core component of its attitude control system, has received more and more attention. Most of the traditional momentum actuators are redundantly designed with multiple single-direction momentum structures. The structure is complex and the efficiency is low, which is not conducive to the long-term on-orbit operation of the small spacecraft. Aiming at the above problems, a permanent magnet momentum sphere (PMMS) is proposed. Firstly, the basic structure of PMMS is given, and the mechanism of cogging torque is expounded according to the structure. Then, the model of PMMS is established in ANSYS by using virtual displacement method. According to the design requirements of PMMS, the relevant structural parameters are simulated and analyzed to reduce the influence of the cogging torque, and the optimal parameters that meet the design requirements are obtained. The simulation results confirm the rationality and effectiveness of PMMS design. © 2023 IEEE.

关键词： Permanent magnets

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization 63

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： You, Jiawei Dai, Xinliang Jiang, Yuning Yin, Haoyu Shi, Yuanming Jones, Colin N. ShanghaiTech University School of Information Science and Technology China Epfl Switzerland Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Epfl Automatic Control Laboratory Switzerland Washington University in St. Louis Department of Electrical and Systems Engineering United States

ISBN: (纸本)9798350316339

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Predictive control systems

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Research on material flow measurement of belt conveyor based on orthogonal machine vision 5

Research on material flow measurement of belt conveyor based...

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5th International Conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Wang, Ran Li, Peiyao Wang, Heyang Zeng, Fei School of Mechanical Automation Wuhan University of Science and Technology Wuhan China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781510682863

The accuracy of traditional visual flow measurement is often compromised due to the variation in the particle state of the material being transported by the belt conveyor. To address this issue, this study proposes a material flow measurement algorithm based on orthogonal machine vision. The algorithm begins by identifying the material type in the RR-CNN image, followed by establishing the material equivalent structure and visual measurement structure. Parameters are then calculated based on the movement state of the material. To improve the filtering effect, K-means clustering filtering is employed, along with a seed region growing algorithm that enhances particle shadow characteristics and image edges. Additionally, volume measurement of different filling densities is achieved using a differential grid and three-dimensional micro-elements, enabling the calculation of the mass of the collected material. Experimental results demonstrate clear particle characteristics and edges, resulting in cost savings for material measurement and a visual measurement accuracy exceeding 95%. © 2024 SPIE.

关键词： Belt conveyors

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An Advanced Deep Learning Detection of Rice Plant Diseases Based on Residual Neural Networks 8th

An Advanced Deep Learning Detection of Rice Plant Diseases B...

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8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024

作者： Huong, Nguyen Thanh Lan, Nguyen Dang Dong, Trinh Cong Thanh, Bui Dang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

For countries with the large rice export in the world, taking care of rice plants for them to grow healthily is very necessary. One of the techniques is to detect diseases promptly, at the right time, on the right diseases in order that necessary treatment can be taken to minimize the harmful effects of diseases on rice plants as well as prevent them from breaking out into epidemics. Previously, disease detection was carried out manually, which took a lot of time and labor. Nowadays, automated methods have been increasingly applied for precision agriculture applications. In the article, a method to automatically detect rice diseases based on machine learning, especially deep learning concentrated method is proposed. In this article, our study concentrates on detecting four most common rice diseases: brown spot, hispa, blast and blight. The dataset for training, testing and validation collected and constructed from Kaggle and Google dataset includes approximately 400 images for each type of rice leaf diseases. From the experiment to assess the performance of the proposed learning method, the accuracy can reach approximately 97%. Thereby, the method shows feasibility and reasonableness, with extensively potential usage for practical precision agriculture application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection

Proximity Estimation with Position Adjustment for Autonomous...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Ziyi Zhou Ping Huang Song Zheng Zhezhuang Xu Zixiong Zhuang Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm.

关键词： Wireless communication Accuracy Fluctuations Service robots Estimation Inspection Topology Object recognition Quadrupedal robots Industrial Internet of Things

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