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BP-STFNet: A Hybrid Time-Frequency Domain Neural Network for Blood Pressure Estimation from Multi-channel BCG Signals

BP-STFNet: A Hybrid Time-Frequency Domain Neural Network for...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kaige Huai Zhicheng Zhou Hong Sun Xianchao Zhang Institute of Advanced Technology University of Science and Technology of China Hefei China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Continuous blood pressure (BP) monitoring is crucial for cardiovascular care but is often constrained by financial concerns. Continuous BP estimation based on ballistocardiogram (BCG) signals offers a non-invasive and cost-effective alternative. Traditional approaches to BP estimation from BCG signals have been hampered by single-channel noise interference, high costs of multi-signal acquisition, and insufficient feature capture. This paper introduces BP-STFNet, a novel deep learning framework that transcends these limitations by leveraging a unique fusion of time-frequency domain information. BP-STFNet enhances input signal quality and ensures robustness against physical movement artifacts. Our custom-designed parallel gated dilated convolution architecture, along with Squeeze-and-Excitation ResNet and Bidirectional Gated Recurrent Unit (Bi-GRU) module, extract detailed features and capture dynamic temporal patterns. The framework achieves a Mean Absolute Error (MAE) of 4.08 mmHg for systolic and 2.12 mmHg for diastolic pressure measurements, demonstrating highly competitive results for systolic pressure estimation and state-of-the-art performance for diastolic pressure prediction. The promising results achieved with BP-STFNet suggest its potential as a viable solution for both continuous and real-time BP monitoring, paving the way for improved cardiovascular health management.

关键词： Time-frequency analysis Neural networks Estimation Medical services Logic gates Feature extraction Blood pressure

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A Novel Transfer Learning Method for Robot Bearing Fault Diagnosis Based on Deep Convolutional Residual Wasserstein Adversarial Network 16th

A Novel Transfer Learning Method for Robot Bearing Fault Dia...

引用

16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Pan, Bing Xiong, Xin Hu, Hailiang He, Jun Yang, Shixi The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Hangzhou Steam Turbine Power Group Co. Ltd. Hangzhou China

ISBN: (纸本)9789819965007

In the process of robot bearing fault diagnosis based on data-driven, transfer learning is an effective method to solve the lack of labeled data, and data distribution difference between source domain data and target domain data will lead to domain drifts under variable working conditions. In this paper, a robot bearing fault diagnosis method based on deep convolutional residual Wasserstein adversarial network (DCRWAN) is proposed to reduce domain offsets and improve the robot bearing fault diagnosis accuracy. In the proposed method, a deep convolutional residual network is constructed to extract the transferable features of source domain and target domain. Then, a domain discrimination module is constructed and the domain adversarial training is carried out to minimize the distribution discrepancy of cross-domain by the Wasserstein loss function. At the same time, a domain distribution discrepancy metrics module is applied to estimate the difference of features distribution between two domains, and further fine-tuning the network parameters to obtain domain-invariant and class-separable features for the classification. The average accuracy of the two transfer tasks using this method is 95.65% and 93.75%, respectively, and the results show that the method effectively reduces the domain differences caused by varying working conditions and improves the fault diagnosis accuracy of robot bearings under variable working conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Failure analysis

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On-Chip Wideband Bandpass Filter with High Frequency Selectivity and Low Insertion Loss Based on GaAs Integrated Passive Device technology

On-Chip Wideband Bandpass Filter with High Frequency Selecti...

引用

IEEE International Wireless Symposium (IWS)

作者： Jiancheng Dong Xu Zhu Tao Yang Yi Nie Yong Wang Nanhu Laboratory Research Center of Advanced RF Chips and Systems Jiaxing China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing China University of Electronic Science and Technology of China Chengdu China

In this paper, an on-chip wideband bandpass filter (BPF) with high frequency selectivity and low insertion loss is proposed using gallium arsenide (GaAs) integrated passive device (IPD) technology. Multiple transmission zeros can be provided by the capacitors loaded on the series and parallel LC resonators for the proposed design. Comparing with the state-of-the-art on-chip wideband BPFs, the proposed design has the advantages of high frequency selectivity and low loss. The final filter centered at 1.93 GHz with the 3-dB fractional bandwidth of 64.2% is demonstrated, which exhibits the excellent shape factor (30-dB BW/3-dB BW) of 1.28 and the minimum insertion loss of 1.17 dB.

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Stabilization of Stochastic Linear systems via Path-dependent Feedback Control

Stabilization of Stochastic Linear Systems via Path-dependen...

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Industrial Cyber-Physical systems (ICPS)

作者： Ruizhe Yu Xiaofeng Zong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

This paper aims to investigate the stabilization problem of stochastic linear system via path-dependent state-feedback control. For the given stochastic linear system, a novel feedback control is designed with the path-dependent information of the system states, and the control gains are determined by the stochastic algebraic Riccati equation. To prove that path-dependent control can drive the stochastic linear system to be exponentially stable, a novel Lyapunov function is proposed. Combined with the general theory on stability of stochastic system, it is shown that stochastic system will be stabilized in mean-square via path-dependent control.

关键词： Linear systems Wireless sensor networks Stochastic systems Riccati equations Filling Feedback control Synchronization

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Robust Delay-Dependent Stability for Uncertain Linear systems with Time-Varying Delay

Robust Delay-Dependent Stability for Uncertain Linear System...

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Annual Conference of Industrial Electronics Society

作者： Dong-Shuai Chen Yong He Xing-Chen Shangguan School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper investigates the problem of stability analysis for the uncertain linear systems with time-varying delay. Firstly, an uncertain linear system model considering time-varying delay is established. Then based on the Lyapunov-Krasovskii functional (LKF) method, a novel robust delay-dependent stability criterion is proposed, which is benefited by a new augmented LKF with more effective time-delay information and the use of a tighter integral inequality to estimate functional derivative. The stability criterion obtained is less conservative. At last, a numerical example shows the superiority and effectiveness that the method used in this paper.

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Road Underground Target Detection in Ground Penetrating Radar Images Based on YOLO Models

Road Underground Target Detection in Ground Penetrating Rada...

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Chinese Automation Congress (CAC)

作者： Ting Li Wei Xue Xinya Yang Dongchang Wu Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models.

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A Magnetically Steerable and Automatically Propulsion Guidewire Robot System for Vascular Interventional Surgery

A Magnetically Steerable and Automatically Propulsion Guidew...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Fu, Shixiong Zhang, Shanxiu Yin, Meng Wang, Shu Li, Dong Wu, Xinyu Du, Shiwei Xu, Tiantian Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China South China Hospital of Shenzhen University Department of Neurosurgery Shenzhen518111 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Magnetically soft continuum robot with automatic navigation capability has great potential to improve the treatment conventional vascular interventional surgery, which exists many problems such as long procedure time and high radiation for doctors to manipulate the passive guidewire. This paper proposes a novel guidewire robot system with magnetically steering and automatically propulsion ability. Actuated by an external magnetic field, the system is able to greatly increase the autonomy of conventional guidewires through the efficient coordination of steering and propulsion. We model the kinematics of autonomous propulsion and magnetically actuation steering separately. Then, the steering performance of the guidewire robot is validated under different magnetic field magnitudes and directions. Finally, two scenario application experiments are implemented to demonstrate the automatic navigation capability of the proposed guidewire robot in a simulated clinical environment. The proposed guidewire robot system provides an effective solution for advancing the automation of vascular interventional surgery. © 2022 IEEE.

关键词： Propulsion

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Landslide Sensitivity Mapping Based on One-Dimensional Residual Convolutional Neural Networks

Landslide Sensitivity Mapping Based on One-Dimensional Resid...

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2022 Chinese Automation Congress, CAC 2022

作者： Ke, Yougang Lu, Chengda Wu, Min Yu, Wanke Niu, Ruiqing School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

ISBN: (纸本)9781665465335

Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extraction. This paper proposes a one-dimensional residual convolution neural network (1DRCNN), which takes Yunyang County, Chongqing City, China, located in the Three Gorges Reservoir area as the research area. Extracting 12 evaluation factors from multi-source remote sensing data and building a training set. The spatial probability of landslide occurrence is quantitatively predicted by the proposed model, and finally, the landslide sensitivity mapping is generated. Compared with the common machine learning models SVM and logistic regression, the results show that the AUC (area under curve) and accuracy of 1D-RCNN are 0.9860 and 93.38%, respectively, which proving that this method is effective and can provide a reference for disaster prevention and reduction. © 2022 IEEE.

关键词： Landslides

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FPGA Implementation of a Sensorless RFOC Strategy for the Delta Inverter fed Induction Motor Drive

FPGA Implementation of a Sensorless RFOC Strategy for the De...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Asma Ben Rhouma Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS - Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie

This paper deals with the fields programmable gate array (FPGA) implementation of a sensorless rotor flux oriented control (RFOC) in a reduced delta inverter fed induction motor drive. In fact, this work represents the cost-effectiveness and the compactness of the electric motor drive control within the consideration of two major reductions: (i) the half reduction of the inverter feeding the electric motor when the reduced delta inverter is used, (ii) the elimination of the speed encoder which allows the decrease of the cost and the volume of the system control. The sensorless RFOC strategy is considered in this paper with a speed estimation based on the application of the MRAS algorithm. The implementation of this strategy on the FPGA virtex 5 board is made using the Xilinx system generator programming tools (XSG). To execute this implementation, a hardware co-simulation test is performed. The performances of this test are approved with the validity of the good agreement between the co-simulation and the XSG simulation results.

关键词： Induction motor drives Simulation Estimation Rotors Tools Inverters Hardware

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3D CNN for Human Action Recognition

3D CNN for Human Action Recognition

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Sameh Neili Boualia Najoua Essoukri Ben Amara Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie

Recognizing different human actions from still images or videos is an important research area in the computer vision and artificial intelligence domains. It represents a key step for a wide range of applications including: human-computer interaction, ambient assisted living, intelligent driving and video surveillance. However, unless the many research works being involved, there are still many challenges ahead including: the high changes in human body shapes, clothing and viewpoint changes and the conditions of system acquisition (illumination variations, occlusions, etc.). With the emergence of new deep learning techniques, many approaches are recently proposed for Human Action Recognition (HAR). Compared with conventional machine learning methods, deep learning techniques have more powerful learning ability. The most wide-spread deep learning approach is the Convolutional Neural Network (CNN/ConvNets). It has shown remarkable achievements due to its precision and robustness. As a branch of neural network, 3D CNN is a relatively new technique in the field of deep learning. In this paper, we propose a HAR approach based on a 3D CNN model. We apply the developed model to recognize human actions of KTH and J-HMDB datasets, and we achieve state of the art performance in comparison to baseline methods.

关键词： Deep learning Training Solid modeling Three-dimensional displays Shape Tools Video surveillance

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