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A novel wearable device integrating ECG and PCG for cardiac health monitoring

Microsystems & Nanoengineering 2025年第1期11卷 173-185页

作者： Junbin Zang Qi An Bo Li Zhidong Zhang Libo Gao Chenyang Xue College of Information Engineering Shanxi College of TechnologyShuozhou 036000China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education North University of China030051 TaiyuanChina Department of Mechanical and Electrical Engineering Xiamen University361102 XiamenChina

The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection *** methods,often costly,bulky,and prone to subjectivity,fall short of meeting the need for daily *** and portable wearable monitoring devices have emerged as a promising research *** study introduces a wearable system that integrates electrocardiogram(ECG)and phonocardiogram(PCG)*** ingeniously pairing a contact-type PZT heart sound sensing structure with ECG electrodes,the system achieves the acquisition of high-quality ECG and PCG ***,the signal-to-noise ratios(SNR)for ECG and PCG signals were measured at 44.13 dB and 30.04 dB,respectively,demonstrating the system’s remarkable stability across varying *** collected signals were subsequently utilized to derive crucial feature values,including electromechanical delay(EMD),left ventricular ejection time(LVET),and pre-ejection period(PEP).Furthermore,we collected a dataset comprising 40 cases of ECG and PCG signals,enabling a comparative analysis of these three feature parameters between healthy individuals and coronary heart disease *** research endeavor presents a significant step forward in the realm of early,non-invasive,and intelligent monitoring of cardiovascular diseases,offering hope for earlier detection and more effective management of these life-threatening conditions.

关键词： wearable device portable wearable monitoring devices phonocardiogram wearable system electromechanical delay cardiovascular diseases electrocardiogram cardiac health monitoring

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SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5724-5737页

作者： Yin, Yufeng Liu, Xiaoyan Zhang, Zichao Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University Quanzhou362006 China

Multi-view stereo aims to recover the 3D model of a scene from a set of images. However, low-textured areas in the scene have always been a challenge in 3D reconstruction. In this work, we propose a segmentation-guided multi-scale anchor deformation patch multi-view stereo. Specifically, we use the Segment Anything Model to distinguish different instances in the scene, and propose an anchor deformation strategy guided by segmentation to adaptively generate a multi-scale anchor patch, so that the depth can be refined from coarse to fine scales. To propagate a better hypothesis for low-textured areas where photometric consistency is unreliable, we propose a non-local adaptive propagation scheme by using the segmented mask as a propagation domain. In order to reduce the interference of illumination on reconstruction completeness, we propose an outlier depth cost refinement guided by reliable points that improves the performance of depth estimation in unevenly illuminated areas. As a result, our method achieves state-of-the-art performance among traditional methods and exhibits better generalization capabilities on the ETH3D, Tanks and Temples, and DTU datasets. © 1991-2012 IEEE.

关键词： Image segmentation

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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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Experimental study on effects of gas flow rate on soot characteristics in diffusion flames coupled with plasma

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Science China(Technological Sciences) 2024年第2期67卷 475-485页

作者： QI DanDan CHEN MingXiao TU Xin LIU Dong MIIT Key Laboratory of Thermal Control of Electronic Equipment School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjing 210094China Advanced Combustion Laboratory School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK

This study examined the evolution of morphology and nanostructure of soot particles from the plasma-flame interaction for various gas flow *** current study used both optical diagnostic and sampling methods to explore the soot production and combustion *** particles were characterized at the same positions downstream from the flame zone by thermophoretic sampling and transmission electron ***,X-ray diffraction analysis,and thermogravimetric analysis were performed to study the nanostructure and oxidation reactivity of soot.A reduction in soot concentration was found with the plasma addition,which illustrated an inhibition effect of plasma on soot *** increased gas flow rate promoted soot concentration since a growing number of carbons participated in the combustion *** on the gas flow rate(carbon content)variation and plasma activation,either liquid-like soot material with irregularly shaped protrusions or chain-like structure,or a mixture of both,were generated from the diffusion *** soot produced by plasma-flame interaction also demonstrated a high correlation between nanostructure and *** soot from lower carbon content with plasma activation had a shorter fringe length and larger fringe tortuosity related to higher oxidation *** the contrary,soot from the highest carbon content without plasma-flame interaction exhibited prevalent fullerene-like nanostructures with evident large or small shells and also had a higher carbonization degree resulting in lower oxidation reactivity.

关键词： soot characteristics gas flow rate plasma diffusion flames

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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Distributed formation control of multiple aerial vehicles based on guidance route

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Chinese Journal of Aeronautics 2023年第3期36卷 368-381页

作者： Jinyong CHEN Rui ZHOU Guibin SUN Qingwei LI Ning ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007China AVIC Xi’an Flight Automatic Control Research Institute Xi’an 710076China

Formation control of fixed-wing aerial vehicles is an important yet rarely addressed problem because of their complex dynamics and various motion constraints,such as nonholonomic and velocity *** guidance-route-based strategy has been demonstrated to be applicable to fixed-wing ***,it requires a global coordinator and there exists control lag,due to its own *** this reason,this paper presents a fully distributed guidance-route-based formation approach to address the aforementioned ***,a hop-count scheme is introduced to achieve distributed implementation,in which each aircraft chooses a neighbor with the minimum hop-count as a reference to generate its guidance route using only local ***,the model predictive control algorithm is employed to eliminate the control lag and achieve precise formation shape *** addition,the stall protection and collision avoidance are also ***,three numerical simulations demonstrate that our proposed approach can implement precise formation shape control of fixed-wing aircraft in a fully distributed manner.

关键词： Formation shape control Fixed-wing aircraft Guidance route Hop-count estimation Model predictive control

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Adaptive Impedance control of Upper Limb Exoskeleton Robot Based on Human Motion Data

Adaptive Impedance Control of Upper Limb Exoskeleton Robot B...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Zhai, Zhifei Liu, Keping Liu, Guang Yu, Changlin Sun, Zhongbo Changchun University of Technology Department of Control Science and Engineering Changchun China Jilin Engineering Normal University Department of Electrical and Information Engineering Changchun China

ISBN: (纸本)9798331504755

The upper limb exoskeleton robot provides an effective treatment method for patients with upper limb functional impairments. A humanoid impedance learning controller (HILC) was designed based on upper limb motion data from humans, which enhances the effectiveness of repetitive rehabilitation training based on human motion mechanisms. Based on human kinematic structures and healthy upper limb motion trajectories, a bionic robot dynamic model was developed, which served as the foundation for designing a humanoid robot control system. To address inaccuracies in the robot's dynamic model, differential adaptive estimation is utilized to compensate for model uncertainties, thereby enhancing accuracy. Additionally, the integration of impedance learning control (ILC) and feedforward control (FFC) within the system reduced the time required to reach a stable state, decreased tracking errors compared to traditional controllers, and improved system stability. Finally, experimental simulations based on ideal parameterized upper limb motion data were conducted to validate the controller's performance. The results demonstrated that the control system could accurately track the ideal parameterized upper limb motion trajectories, with the shoulder and elbow joint trajectories of the exoskeleton robot closely resembling those of a healthy human. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage

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Science China(Technological Sciences) 2023年第5期66卷 1397-1407页

作者： WANG YiFan ZHOU MiaoLei SHEN ChuanLiang CAO WenJing HUANG XiaoLiang Department of Control Science and Engineering Jilin UniversityChangchun130022China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun130022China Department of Engineering and Applied Sciences Sophia UniversityTokyo102-8554Japan Department of Electrical Engineering Chalmers University of TechnologyGothenburg41296Sweden

Piezo-actuated stage is a core component in micro-nano manufacturing ***,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking *** study proposes a direct adaptive control(DAC)method to realize high precision *** proposed controller is designed by a time delay recursive neural *** with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis ***,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller ***,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this ***,the performance of the HNN estimator can be exhibited visually through the modeling *** proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC *** stability of the control system is ***,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.

关键词： piezo-actuated stage direct adaptive control time delay recursive neural network hopfield neural network estimator

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