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Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Enhancing the performance of CaTiO3-GDC heterostructure electrolytes in LT-CFCs

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Ceramics International 2025年第15期51卷 20131-20142页

作者： Li, Dongchen Cui, Yingying Hong, Xiaofang Shah, M.A.K. Yousaf Mushtaq, Naveed Rauf, Sajid Arshad, Naila Irshad, Sultan Alodhayb, Abdullah N. Yousaf, Muhammad Lu, Yuzheng Department of Electrical and Automation Shandong Labor Vocational and Technical College Jinan250300 China Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and Environment Southeast University No.2 Si Pai Lou Nanjing210096 China College of Mechatronics and Control Engineering Shenzhen University Guangdong Province 518000 China School of Physics Electronics and Intelligent Manufacturing Huaihua University Hunan Huaihua418008 China College of Electronics and Information Engineering Shenzhen University Guangdong Province 518000 China Department of Physics and Astronomy College of Science King Saud University Riyadh11451 Saudi Arabia College of Chemistry and Environmental Engineering Shenzhen University Guangdong Province Shenzhen518055 China School of Electronic Engineering Nanjing Xiaozhuang University Nanjing211171 China

Designing ceramic fuel cells (CFCs) with fast ionic conduction and superior electrochemical performance at low temperatures is critical for advancing hydrogen applications. Here, we developed a heterostructure electrolyte by combining the ionic conductor Gd0.1Ce0.9O2 (GDC) with the semiconductor material CaTiO3 (CTO) to shape into the composite heterostructure (semiconductor ionic heterostructure) CTO-GDC. The designed heterostructure with different fermi levels established the depletion zone due to the redistribution of charges at the interface, leading to enhancement of the ionic conduction;meanwhile, the established built-in electric field suppresses the electronic conduction speed of the ion's transportation at the interface of the constructed heterostructure (CTO-GDC). The formation of heterostructure introduces a number of oxygen vacancies, as confirmed via x-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR), benefiting the transportation of ions in the composite heterostructure. The prepared composite heterostructure shows the highest power output of 810 mW/cm2 at a low operational temperature of 520 °C. The CTO-GDC shows high ionic conductivity of 0.17 S cm−1 at 520 °C and low activation energy of 0.785 eV at 520-420 °C. The composite containing a 50 % molar ratio of GDC and CTO demonstrated exceptional performance by successfully inhibiting electron transit, improving ionic conductivity, and achieving outstanding power output. The enhanced ionic conductivity with low activation energy of composite heterostructure suggests that designing a heterostructure seems an efficient approach to fuel cell application. © 2025

关键词： Paramagnetic resonance

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

Binary system modes of matrix-coupled multidimensional Kuramoto oscillators

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arXiv 2023年

作者： Wang, Chongzhi Shao, Haibin Li, Dewei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Synchronization and desynchronization are the two ends on the spectrum of emergent phenomena that somehow often coexist in biological, neuronal, and physical networks. However, previous studies essentially regard their coexistence as a partition of the network units: those that are in relative synchrony and those that are not. In real-world systems, desynchrony bears subtler divisions because the interacting units are high-dimensional, like fish schooling with varying speeds in a circle, synchrony and desynchrony may occur to the different dimensions of the population. In this work, we show with an ensemble of multidimensional Kuramoto oscillators, that this property is generalizable to arbitrary dimensions: for a d dimensional population, there exist 2d system modes where each dimension is either synchronized or desynchronized, represented by a set of almost binary order parameters. Such phenomena are induced by a matrix coupling mechanism that goes beyond the conventional scalar-valued coupling by capturing the inter-dimensional dependence amongst multidimensional individuals, which arises naturally from physical, sociological and engineering systems. As verified by our theory, the property of the coupling matrix thoroughly affects the emergent system modes and the phase transitions toward them. By numerically demonstrating that these system modes are interchangeable through matrix manipulation, we also observe explosive synchronization/desynchronization that is induced without the conditions that are previously deemed essential. Our discovery provides theoretical analogy to the cerebral activity where the resting state and the activated state coexist unihemispherically, it also evokes a new possibility of information storage in oscillatory neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Synchronization

Demand-Side Joint Electricity and Carbon Trading Mechanism

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IEEE Transactions on Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 14-25页

作者： Hua, Haochen Chen, Xingying Gan, Lei Sun, Jiaxiang Dong, Nanqing Liu, Di Qin, Zhaoming Li, Kang Hu, Shiyan College of Energy and Electrical Engineering Hohai University Nanjing211100 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Automation Tsinghua University Beijing100084 China Automatic Control Laboratory at Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users' optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method. © 2023 IEEE.

关键词： Real time systems

Synchronization Stability of Grid-Tied VSC With control Limiters: Models, Solutions, and Boundaries

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IEEE Transactions on Energy Conversion 2024年

作者： Wu, Tianhao Jiang, Qirong Liu, Feng Huang, Meng Xie, Xiaorong State Grid Corporation of China National Power Dispatch and Control Center Beijing100031 China Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing100084 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China

Existing studies on large-signal synchronization stability of the grid-following voltage-source converters (VSCs) have been focused on the phase-locked loop (PLL). Saturation nonlinearities introduced by current or voltage limiters, which can emerge during large disturbances, could greatly complicate the instability mechanism of PLL-synchronized converters. However, limited attention has been paid to such saturation-induced instability issues in prior work. This paper investigates the large-signal synchronization stability of grid-tied VSCs with full consideration to two typical kinds of saturation nonlinearities in the VSC's control system: the current hard limiter and the voltage circular limiter. The conditions under which the VSC may enter a saturated state are revealed. The impacts of hard and circular limiters on the system transient dynamics are analyzed separately. A unified differential equation (UDE) model that combines both kinds of limiters is proposed for quantitative analysis of synchronization stability. Subsequently, the averaging-theory-based analysis approximately solves the UDE model and provides the analytical large-signal stability boundaries of the system. The impacts of various factors, including operating conditions, PLL parameters, limiter thresholds, and active/reactive current control strategies, on the synchronization stability are investigated based on the proposed model. The effectiveness of theoretical analyses is validated via time-domain simulation and control hardware-in-the-loop tests. © 1986-2012 IEEE.

关键词： Phase locked loops

Generalized Predictive Analysis of Reactions in Paper Devices Via Graph Neural Networks

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SSRN

SSRN 2024年

作者： Sun, Hao Pan, Yihan Dong, Hui Liu, Canfeng Yang, Jintian Tao, Yihui Jia, Yuan School of Mechanical Engineering and Automation Fuzhou University Fuzhou China School of Mechatronics Engineering Harbin Institute of Technology Harbin China Fuzhou University China Department of Automation Control and System Engineering University of Sheffield Sheffield United Kingdom Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5%, 94.3% and 99.0% by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models

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arXiv 2024年

作者： Yao, Yifei He, Wentao Gu, Chenyu Du, Jiaheng Tan, Fuwei Zhu, Zhen Lu, Junguo All authors are with Machine Vision and Autonomous System Laboratory Department of Automation School of Electrical Information and Electronic Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologies, yet these still require significant human intervention. This paper introduces an end-to-end framework for training and deploying RL policies, guided by Large Language Models (LLMs), and evaluates its effectiveness on bipedal robots. The framework consists of three interconnected modules: an LLM-guided reward function design module, an RL training module leveraging prior work, and a sim-to-real homomorphic evaluation module. This design significantly reduces the need for human input by utilizing only essential simulation and deployment platforms, with the option to incorporate human-engineered strategies and historical data. We detail the construction of these modules, their advantages over traditional approaches, and demonstrate the framework’s capability to autonomously develop and refine controlling strategies for bipedal robot locomotion, showcasing its potential to operate independently of human intervention. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

A Fault Detection Method for Power Transmission Lines Using Aerial Images

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A Fault Detection Method for Power Transmission Lines Using ...

International Conference on Unmanned Aircraft systems (ICUAS)

作者： Yulong Zhang Shaowei Cao Lingxia Mu Xianghong Xue Jing Xin Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing School of Automation and Information Engineering Xi'an University of Technology Xi'an Shaanxi China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

It is necessary to regularly detect faults to maintain the safety and stability of power lines. Insulators are one of the important electrical components in high-voltage transmission lines. It is extremely necessary to check the working status of insulators regularly. Traditional manual inspection is inefficient because it requires a significant amount of labor costs. In this paper, a method for detecting insulators' missing defect based on aerial images is proposed to address the issue by unmanned aerial vehicle (UAV). Firstly, the improved Faster R-CNN (region-based convolutional neural network) is used to identify and locate insulators in aerial images. Secondly, the U-Net image segmentation network segments insulators from the images. The adaptive threshold segmentation method completely separates the insulator from the background. Then the binary image of the insulator is obtained. Finally, the binary image is converted into a fault curve which is used for determining the missing insulators based on the distribution of the fault curve. By using collected insulator datasets on a 330kV overhead transmission line using a DJI M300 UAV platform and an onboard H20T camera/sensor, the detection accuracy of glass insulators is as high as 0.98 with the proposed algorithm. The positioning accuracy of the proposed algorithm is also higher than other algorithms. This method has high detection accuracy for missing defects in insulators. The experimental results show that compared with similar algorithms, this method has higher accuracy and efficiency.

关键词： Image segmentation Accuracy Power transmission lines Glass Manuals Power system stability Insulators