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Proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023

作者： Li, Yiying Wang, Bo Zhang, Lijun Liu, Lei Fan, Huijin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819958467

This paper proposes a real-time integrated energy system (IES) management approach which aims at promoting overall energy efficiency, increasing renewable energy penetration, and smoothing load fluctuation. The electric vehicles (EVs) charging scheduling is incorporated into the IES management, where the uncertain arrivals and departures of multiple EVs are considered as a stochastic but flexible load to the IES. Furthermore, towards the carbon neutralization target, a carbon emissions trading mechanism is introduced into the IES management to incentivize the system to operate in an eco-friendlier manner. To tackle the computational complexity induced by the stochastic and intermittent nature of the renewable energy sources and EVs load, the scheduling of the IES is realized in a neural network based real-time manner, driven by a deep reinforcement learning approach that guarantees safe training and operation. The case study verifies the effectiveness of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A review of underground transport infrastructure monitoring in CCS: technology and Engineering Practice

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Process Safety and Environmental Protection 2024年 190卷 726-745页

作者： Peng, Haoyan Xu, Zhao-Dong Lu, Hongfang Xi, Dongmin Xia, Zhiheng Yang, Cen Wang, Bohong China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures Southeast University Nanjing 210096 China National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control Zhejiang Ocean University Zhoushan 316022 China

Carbon capture and storage (CCS) plays a vital transitional role in carbon sinks, which is an international research hotspot. CO2 pipelines and injection wells are the most common transport methods for transporting CO... 详细信息

Carbon capture and storage (CCS) plays a vital transitional role in carbon sinks, which is an international research hotspot. CO₂ pipelines and injection wells are the most common transport methods for transporting CO₂ from the capture site to the storage space, which are underground transport infrastructure. Despite the global significance of underground transport infrastructure of CCS, research on its monitoring remains limited, and there is a scarcity of relevant engineering specifications due to the under development of CCS. This paper proposes a three-level monitoring framework based on the characteristics of the monitoring sensing technology, and summarizes various monitoring methods, engineering practice of monitoring, current challenges and future development trends of underground transport infrastructure of CCS based on the structural characteristics, failure characteristics of underground transport infrastructure of CCS. Through the summary of existing engineering projects and academic research, there is a more comprehensive and in-depth understanding of the current and future monitoring technologies of underground transport infrastructure of CCS, which will contribute to enhancing process safety and disaster prevention and control in CCS operations. © 2024

关键词： CO2 injection well CO2 pipeline Engineering practice Monitoring technology

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Prescribed performance following control of mobile rehabilitation robot based on human walking intention estimation

Prescribed performance following control of mobile rehabilit...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Meiping Zhang Yifei Guo Qingyang Yan Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

For the elderly, disabled and hemiplegic patients who have mild weakness of lower extremity muscle strength but can still walk independently, a mobile rehabilitation robot accompanied by the user side is designed to meet their needs of daily assistance and rehabilitation training. In order to accurately estimate human walking intention, a human walking intention estimation algorithm based on Kinect vision sensor and ellipsoid set member filter is proposed. In order to solve the problem of limited detection range of the vision sensor, a robot following control algorithm with prescribed performance is proposed, which not only guarantees that the robot will smoothly follow the user, but also keeps the robot in a relative position where the user can comfortably grasp the armrest without collision risk. Simulation and experimental results verify the effectiveness of the proposed intention estimation algorithm and the following control algorithm.

关键词： Legged locomotion Training Estimation Vision sensors Filtering algorithms Robot sensing systems Assistive robots

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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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Covid-19 Epidemic Trend Prediction Based on CNN-StackBiLSTM 11

Covid-19 Epidemic Trend Prediction Based on CNN-StackBiLSTM

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Li, Zhenyu Wang, Yang Wang, Yanwei Zheng, Ying Su, Housheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology China-Belt and Road Joint Laboratory On Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Hubei Wuhan430073 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In late 2019, the novel coronavirus (COVID-19) became a major health hazard around the world. Recently, the COVID-19 has spread widely in most countries and regions and the number of infected people continues to grow rapidly. Therefore, it is essential to research the development trend of the epidemic. The prediction of the number of infections and deaths is critical and helpful for developing health and epidemic prevention strategies and even curbing the epidemic. In this paper, a one-dimensional convolutional neural network combined with the stacked long-short-term-memory network model (CNN-StackBiLSTM) is proposed for the time-series prediction of cumulative cases and daily new cases. The local feature is extracted by CNN. The stacked BiLSTM captures the deeper characteristics of the time-series data. By combining the two networks, the proposed method simultaneously considers the information of temporal and spatial domains and can achieve accurate prediction results. Examples in Taiwan and Italy demonstrate the effectiveness of the proposed method. The proposed method is compared with LSTM, BiLSTM, and GRU. The mean absolute error, mean square error, R2 score, and root mean square error are calculated to quantificationally measure the different models. The results indicate the proposed method performs well in the prediction of both new daily confirmed cases and cumulative confirmed cases. © 2022 IEEE.

关键词： COVID-19

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A Prototypical Networks-based Multi-task Model for Few-shot Fault Diagnosis 11

A Prototypical Networks-based Multi-task Model for Few-shot ...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Huo, Zhihao Yang, Xiaoyu Yang, Tao Fan, Huijin Su, Housheng Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China-Belt and Road Joint Laboratory On Measurement and Control Technology Wuhan430074 China School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Due to the powerful classification performance, Prototypical Networks is widely used in few-shot fault diagnosis. However, insufficient feature extraction ability and negligence of inter-class distance in the Prototypical Networks may lead to the decrease of diagnosis accuracy. To solve the above problems, a Prototypical Networks-based multi-task model (PNBMTM) is proposed in this paper. Firstly, a convolutional autoencoder is built based on the feature extraction network of the Prototypical Networks, in which the optimization of the reconstruction loss is taken as a new task to obtain more efficient features. Secondly, a channel attention module (CAM) is designed to be applied to each layer of the encoder, which focuses on important features and suppresses useless features. Thirdly, relative distance loss is introduced while considering the effect of intra-class distance and inter-class distance to make inter-class distance larger and intra-class distance smaller, which helps to improve the quality of the features. And the optimization of relative distance loss is consequently taken as a new task to constitute a multi-task fault diagnosis model. Finally, a mechanical fault simulation experiment platform is built to verify the validity of the proposed model. The experimental results show that the proposed model has superior performance compared with other models. © 2022 IEEE.

关键词： Failure analysis

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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 Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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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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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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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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