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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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An optimized multi-label TSK fuzzy system for emotion recognition of multimodal physiological signals*

An optimized multi-label TSK fuzzy system for emotion recogn...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Yixuan Li Zhongzheng Fu Xinrun He Jian Huang Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan Hubei China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Nowadays, as intelligent detection devices become reachable, how to integrate multimodal physiological signals to identify human emotions becomes a hot topic. However, the corresponding relationship between physiological signals and emotions is often difficult to be modeled mathematically, and too many signals will bring redundant features and reduce the accuracy and efficiency of emotion classification. In order to solve these problems, this paper adopts multi-label TSK fuzzy system to establish fuzzy rules for emotion recognition. Meanwhile, this paper proposes an optimization method of fusing subspace clustering and fuzzy C-means clustering to solve the problem of establishing fuzzy rules for high-dimensional features. We also consider uniform regularization to balance the trigger strength of different fuzzy rules. A dataset named DEAP is used to test the accuracy of our method. The results showed that Multi-label TSK fuzzy system with subspace fuzzy C-means and uniform regularization has a nearly 10% improvement in accuracy compared with the traditional SVM, and it’s also helpful to explore the correlation of different labels such as arousal and valence, which can contribute to the further study of affective computing.

关键词： Support vector machines Emotion recognition Affective computing Correlation Computational modeling Optimization methods Physiology

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Simulation Research on Breast Tumor Model Based on Magnetoacoustic Concentration Tomography of Magnetic Nanoparticles with Magnetic Induction

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Progress In Electromagnetics Research C 2024年 139卷 197-209页

作者： Yan, Xiaoheng Liu, Fangtian Smolik, Waldemar T. Dan, Xinxian Hou, Xiaohan Faculty of Electrical and Control Engineering Liaoning Technology University Huludao125000 China The Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw Poland

Magnetic nanoparticles (MNPs) have been widely investigated as effective drug carriers for targeted tumor therapy. However, the successful application of this technology in the human body requires reliable imaging support. Magnetoacoustic Concentration Tomography of Magnetic Nanoparticles with Magnetic Induction (MACT-MI) is an electromagnetic-ultrasonic coupling imaging technique that holds great promise in improving imaging resolution and providing unique advantages for tumor monitoring and treatment. To evaluate the imaging feasibility of MACT-MI technology for targeted therapy of breast tumors, this study establishes a realistic breast model and takes into account the distribution of magnetic particles within the actual breast tissue environment. A concentration gradient model is introduced, and the finite element method is employed to solve the electromagnetic and sound fields. In addressing the research objective, the forward problem is investigated by analyzing the magnetic force and sound pressure distribution for various tumor sizes and locations, different breast tissues, and both benign and malignant tumors. The results obtained indicate that the magnetoacoustic signal emitted by magnetic particles facilitates accurate mapping of the size and location information of magnetic particles enveloping breast tumors, as well as distinguishing between benign and malignant tumors. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Tomography

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Interpretable Bearing Stage Division based on Shapelet and SVM

Interpretable Bearing Stage Division based on Shapelet and S...

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Data Driven control and Learning Systems (DDCLS)

作者： Xinye Li Xiaoyu Yang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan

The stage division of bearings is of great significance to ensure the safe operation of heavy machinery. By dividing bearing stages, early degradation points can be identified for bearing remaining useful life (RUL) prediction, and the accuracy of RUL prediction can be improved. Although the existing stage division methods can effectively divide the stages of bearings, most of the methods lack interpretability. In this paper, an interpretable rolling bearing stage division method based on shapelet and Support vector machine (SVM) is proposed. Firstly, a Manhattan distance-based shapelet is used to extract the bearing degradation features, which better discriminates the difference of bearing signals in different health states. Then, SVM is utilized to stage the degradation features. Finally, shapelet evolution diagrams are constructed to characterize the rolling bearing vibration signals at different stages, visually interpreting the bearing stage classification results. Based on the experimental study on the PRONOSTIA platform, the effectiveness and interpretability of the proposed method are verified. The results show that the proposed method can effectively classify the bearing health stages and visualize the interpretation of the stage classification results.

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Physics-Informed LSTM Network-Based Nonlinear Model Predictive control

Physics-Informed LSTM Network-Based Nonlinear Model Predicti...

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Data Driven control and Learning Systems (DDCLS)

作者： Yujing Chen Qilin Qui Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Electrical Engineering Wuhan Institute of Technology Wuhan China

To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive control algorithm based on a physics-informed long short-term memory(LSTM) network. Firstly, the neural network incorporating physical information is extended to model the ordinary differential equations with variable initial states and external control quantities, which makes the network adaptable to the control task and makes the training model physically interpretable. Secondly, a network structure with a mixture of fully connected layers and LSTM layers is built by using the good learning ability of LSTM for time-series data, and the loss function is designed according to the system characteristics and prediction requirements. The trained neural network model is then used as an internal prediction model to construct a nonlinear model predictive control algorithm. Finally, taking the continuous stirring reactor system as an example, the method is verified to be able to fit the system model highly and reduce the time to reach the steady state with a small number of samples.

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Aero-Engine Remaining Useful Life Prediction Using A Multi-Dimensional Convolutional Feature Fusion Framework

Aero-Engine Remaining Useful Life Prediction Using A Multi-D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Liangcheng Wang Yang Wang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Hubei

Accurate prediction of aero-engine remaining useful life (RUL) is essential for providing reliable maintenance or alarm decisions. The extraction of degraded features has a great impact on the accuracy of RUL prediction. This paper proposes a feature fusion framework that relies on a multi-dimensional convolutional neural network (MD-CNN). First, the data of each working condition is normalized separately to extract degradation features more effectively. Subsequently, the temporal features are extracted using the one-dimensional convolutional neural network (1D-CNN), while the spatial local features are captured through the utilization of the two-dimensional convolutional neural network (2D-CNN). Finally, the long short-term memory network (LSTM) is utilized as the prediction model to establish the mapping relationship from fusion features to RUL. The result shows that the performance in predicting using the proposed method surpasses that of the existing methods.

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Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

Network Intrusion Detection Based on Graph Neural Network an...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Lina Zhang Yang Wang Minyue Wu Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

Network technology has become an inseparable part of daily life, which is easily attacked by some criminals. Therefore, ensuring the security of network becomes an urgent problem. In the past few years, various deep learning techniques have been put forward for identifying network intrusions. However, the majority of these approaches overlook the non-Euclidean nature of relationships within network intrusion data. In this paper, graph neural network (GNN) is used to learn the relationship between data. The sample balancing module is to address the significant disparity between the number of intrusive samples and normal samples. The results obtained from the experiments indicate that the suggested approach can enhance the effectiveness of the intrusion detection system.

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Trajectory Planning for Minimizing Floating Pedestal Disturbance of Spatial Dual-Arm Robots 43

Trajectory Planning for Minimizing Floating Pedestal Disturb...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Hui Zuo, Xiaozhong Zhang, Wei Gao, Sheng Shenyang Jianzhu University College of Electrical and Control Engineering Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai200120 China

ISBN: (纸本)9789887581581

This paper addresses the trajectory planning problem of a spatial dual-arm robot and proposes a method to minimize the floating pedestal perturbation using a nonlinear planning method. The joint angle of the dual-arm robot is the optimization variable, and the positioning accuracy of the end-effector and the pedestal disturbance are the optimization objective and constraints, respectively. A constrained multi-objective optimization problem is formulated, and the kinematic model of the robot is established by parameterizing the joint trajectory. A penalty factor and a collision detection mechanism are applied to ensure the safety of the planning process. A nonlinear planning method is used to solve the optimization problem and obtain the optimal joint trajectory of the dual-arm robot. Simulation results demonstrate that the proposed method effectively reduces the base disturbance while ensuring the positioning accuracy of the end-effector. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： End effectors

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A Method for Detecting the Reducers’ Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers’ Grease Failure Based o...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Ditong Wang Hong Zhang Ying Zheng Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Real time detection of the lubricating grease’s effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task.

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Deterministic-like data-driven discovery of stochastic differential equations via the Feynman–Kac formalism

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The European Physical Journal Special Topics 2024年 1-21页

作者： Ma, Chaoxiang Huang, Cheng Cheng, Cheng Li, Xiuting The College of Informatics Huazhong Agriculture University and the China–Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan People’s Republic of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Ministry of Education Key Lab of Intelligent Control and Image Processing Wuhan China

This paper develops a data-driven deterministic identification architecture for discovering stochastic differential equations (SDEs) directly from data. The architecture first generates deterministic data for stochastic processes using the Feynman–Kac formula, and gives a parabolic partial differential equation (PDE) associated with the SDE. Then, a sparse regression model is proposed to discover drift and diffusion terms in SDEs using PDE data-driven techniques, where a large candidate library of potential terms only for the drift and diffusion coefficients in SDEs need be constructed. To simultaneously infer the drift and diffusion terms, we proposed a sequential thresholded reweighted least-squares algorithm to solve the constructed sparse regression model. The main advantage of the proposed method is that on the one hand, theoretical and numerical identification results of PDEs can be used for SDEs, on the score, our SDE identification problem is translated into the parameter estimation problem of PDEs, on the other hand, the proposed algorithm is easily executed and can enhance the sparsity and accuracy. Through several classical SDEs and ordinary differential equations, the effectiveness of the proposed data-driven method is demonstrated, and several comparison experiments with state-of-the-art approaches is provided to illustrate the superiority of the developed algorithm.

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