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

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan 430074 China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China China University of Geosciences Faculty of Engineering Wuhan 430074 China Macau University of Science and Technology School of Computer Science and Engineering Macau Macao Wuhan University Electronic Information School Wuhan 430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： adversarial generative network image fusion Multi-modal fusion semantic awareness

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Miniaturized silicon-based capacitive six-axis force/torque sensor with large range, high sensitivity, and low crosstalk

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Microsystems & Nanoengineering 2024年第6期10卷 133-149页

作者： Renjie Tan Yong Xia Xiangguang Han Linya Huang Wendi Gao Chen Jia Ping Yang Qijing Lin Shujiang Ding Chenying Wang Libo Zhao State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemXi’an Jiaotong University710049 Xi’anChina School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Instrument Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Electronic Materials Research Laboratory Key Laboratory of the Ministry of EducationSchool of Electronic Science and EngineeringXi’an Jiaotong University710049 Xi’anChina Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Engineering Research Center of Energy Storage Materials and Devices of Ministry of Education National Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologySchool of ChemistryXi’an Jiaotong UniversityXi’an 710049China

Miniaturized six-axis force/torque sensors have potential applications in robotic tactile sensing,minimally invasive surgery,and other narrow operating spaces,where currently available commercial sensors cannot meet the requirements because of their large *** this study,a silicon-based capacitive six-axis force/torque sensing chip with a small size of 9.3×9.3×0.98 mm was designed,fabricated,and tested.A sandwich decoupling structure with a symmetrical layered arrangement of S-shaped beams,comb capacitors,and parallel capacitors was employed.A decoupling theory considering eccentricity and nonlinear effects was derived to realize low axial *** proposed S-shaped beams achieved a large measurement range through stress *** results of a coupled multiphysics field finite-element simulation agreed well with those of theoretical *** test results show that the proposed sensing chip can detect six-axis force/torque separately,with all crosstalk errors less than 2.59%*** force and torque measurement ranges can reach as much as 2.5 N and 12.5 N·mm,*** sensing chip also has high sensitivities of 0.52 pF/N and 0.27 pF/(N·mm)for force and torque detection,respectively.

关键词： crosstalk shaped torque

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Optimizing Bounding Box Regression in Complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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2024 China Automation Congress, CAC 2024

作者： Chen, Jun Zhang, Haiyan Yu, Anjun Wang, Yiwei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Jiangxi Ganyue Expressway Co.Ltd. Nanchang China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350368604

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model’s ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1% on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments. © 2024 IEEE.

关键词： Safety devices

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Dynamics Modeling Method of Offshore Drilling Experimental System Based on Double-Layer Stewart Parallel Mechanism 42

Dynamics Modeling Method of Offshore Drilling Experimental S...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Chenxuan Ma, Zhejiaqi Lu, Chengda Du, Sheng Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581543

Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing. A dynamical modeling method for the offshore drilling experimental system built on the double-layer Stewart parallel mechanism is proposed. Firstly, the kinematic and dynamical characteristics of the double-layer Stewart parallel mechanism are combined with the Lagrange method and the virtual work method to establish the dynamics model of the mechanism. Then a parameter identification scheme is designed using a nonlinear gray system estimation method based on the trust-domain reflection algorithm, and the model parameters are identified. The model is downscaled to improve the feasibility of the identification scheme and the accuracy of the identified parameters. The actual experimental system data verify this model's correctness and the model parameters' accuracy. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dynamics

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An Online Education Effect Assessing Method Based on YOLOv8 and Vision Transformer

An Online Education Effect Assessing Method Based on YOLOv8 ...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Bo-Yang Liu, Zhen-Tao Zeng, Xiang-Yu School of Future Technology China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350303759

Due to the rapid growth of online education worldwide, assessing the learning effectiveness of students during online classes has become increasingly challenging for teachers. In this paper, a method of assessing online education effect based on YOLOv8 and Vision Transformer is proposed. Firstly, the drowsiness state of students in an online teaching unconstraint environment is estimated by using YOLOv8, and then the gaze direction of awake students is estimated by using Vision Transformer to assess the students' attention levels during online education. The results from YOLOv8 are determined based on the calculated probabilities of drowsiness or wakefulness. And then gaze estimation method proposed in this paper was compared with state-of-the-art methods on the MPIIFaceGaze and Gaze360 datasets in which the angular errors of gaze estimation are 4.58° and 12.27°, respectively. We conducted experiments and analysis on a self-made dataset, from which the results demonstrate the feasibility of our method in an unconstrained environment. © 2023 IEEE.

关键词： Students

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Different Height Stairs Climbing Pattern Generation for Humanoids

Different Height Stairs Climbing Pattern Generation for Huma...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Yang, Ming Tian, Dingkui Xu, Minjia Li, Feng Sun, Jianquan Shang, Weiwei Wu, Xinyu University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Daqing Vocational College Daqing163255 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9798350327182

Climbing stairs has been an indispensable ability for humanoids. This paper presents a novel and practical problem: climbing stairs of varying heights. In this study, the humanoid is modeled as an inverted pendulum and set the pelvis trajectory on the vertical axis, representing the center of mass (CoM), as a quadratic function. We also use a polynomial to design the ZMP and CoM trajectory on the horizontal plane. To generate the CoM trajectory, the sliding mode controller is utilized to track the reference trajectory. To eliminate chattering of the sliding mode controller, the universal approximation property of the radial basis function (RBF) network is leveraged to approximate the switching law. Finally, simulation experiments are performed in CoppeliaSim, and the biped robot is capable of walking stably and quickly. © 2023 IEEE.

关键词： Stairs

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Surgical Gesture Recognition in Open Surgery Based on 3DCNN and SlowFast 7

Surgical Gesture Recognition in Open Surgery Based on 3DCNN ...

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7th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Men, Yutao Luo, Jian Zhao, Zixian Wu, Hang Luo, Feng Zhang, Guang Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China

ISBN: (纸本)9798350370782

In the field of computer-aided medicine, automated surgical gesture recognition is an important research direction. Its main applications include automated skills assessment, intraoperative guidance, and education and training systems. Current research has focused on robotic surgery, and there is less research on surgical gestures for open surgery. Therefore, in this study, a new simulated open surgery dataset was developed, and a series of surgical gestures for open surgery were defined for closure the abdomen surgery with the cooperation of professional doctors. In addition, this paper uses the classical algorithm 3DCNN and SlowFast model in the field of action recognition to perform offline recognition of surgical gestures for open surgery on the dataset. 3DCNN uses R3D and R(2+1)D models. The experimental results show that on the self-built open surgical dataset, the R3D model has the best surgical gesture recognition results, with an accuracy rate of 90.4%, a precision rate of 90.5%, and a recall rate of 90.0%. © 2024 IEEE.

关键词： Gesture recognition

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A Dual Approach with CycleGANs-based Face Reconstruction and ViT-based Classification

A Dual Approach with CycleGANs-based Face Reconstruction and...

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Laila Ouannes Anouar Ben Khalifa Najoua Essoukri Ben Amara LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse Tunisie Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Facial recognition technology plays a crucial role in various domains, including security, surveillance, and biometric authentication. However, the accuracy of conventional facial recognition systems is significantly compromised in degraded conditions, characterized by head pose variations, occlusions, and variations in lighting. In this study, we propose an innovative approach to address this challenge by integrating advanced deep-learning techniques. Specifically, we leverage Cycle Generative Adversarial Networks (CycleGANs) for facial reconstruction and vision Transformers (ViT) for recognition in degraded conditions. GANs are employed to enhance the quality of degraded facial images by generating high-resolution reconstructions from low-quality inputs. The reconstructed images are then fed into Transformer-based models, which extract discriminative features for robust recognition. To evaluate the effectiveness of our approach, we conducted extensive experiments using publicly available datasets of degraded facial images: the EURECOM Kinect Face Dataset and the IST-EURECOM Light Field Face Database. Our results demonstrate significant improvements in face recognition accuracy compared to traditional methods, particularly in scenarios with low-resolution or occluded faces. Furthermore, we analyze the computational efficiency and scalability of our proposed approach, highlighting its potential for real-world deployment.

关键词： Computer vision Accuracy Image recognition Head Face recognition Lighting Transformers

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Vital Signs Detection Based on Scale-Space Representation and Empirical Wavelet Transform for FMCW Radar

Vital Signs Detection Based on Scale-Space Representation an...

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2022 Chinese Automation Congress, CAC 2022

作者： Wu, Dongchang Xue, Wei Yang, Xinya Li, Tin 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 Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9781665465335

With the development of radar technology, frequency modulated continuous wave (FMCW) radar has been used for non-contact vital signs detection. In order to suppress the environmental noise and interference of breathing harmonics on heartbeat signal, this paper proposes a new vital sign detection method based on scale-space representation (SSR) and empirical wavelet transform (EWT) for the FMCW radar. First, a low-pass filter is set for the high-frequency noise elimination outside the frequency range of vital sign signals. Second, SSR is used to adaptively segment the spectrum of signals to obtain the initial frequency boundaries, and the kurtosis of the spectrum is applied to segment the spectrum again and obtain the final frequency boundaries to further eliminate noise. Third, the empirical wavelet filter bank is constructed by using the determined boundaries and EWT is used to decompose the signals to several components. Finally, according to the frequency ranges of components and the correlation between the components and vital sign signals, the components are selected to reconstruct the breathing and heartbeat signals. The experimental results show that the proposed method achieves a better detection performance than the classical EWT and empirical mode decomposition (EMD). © 2022 IEEE.

关键词： Wavelet transforms

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