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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Kang, Tianbo Zhang, Mian Xie, Ruitong Chen, Jiwei Xiang, Hongbiao Wang, Yuhang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China

ISBN: (纸本)9798350301359

Planetary gearboxes (PGs) are the core transmission components of complex rotating machinery. The diverse forms of internal gear faults often complicate diagnosis and maintenance work. To improve the fault diagnosis accuracy of PGs, this paper proposes a method for diagnosing PG faults based on deep autoencoders (DAE) and convolutional neural networks (CNN). The method fully utilizes the deep learning capability of DAE and combines it with the precise recognition ability of CNN for fault features. Through precise learning in the encoding layer, it achieves deep dimensionality reduction of fault features and further learns these features after dimensionality reduction by CNN. Compared to traditional CNN, the method reduces the computation parameters and resource consumption, resulting in higher precision intelligent fault diagnosis. Experimental results demonstrate that DAE-CNN exhibits stronger fault diagnosis ability than traditional networks. © 2023 IEEE.

关键词： component DAE-CNN deep learning fault diagnosis planetary gearboxes

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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design of an electronic device for detection of exhaled gas concentration 5

Design of an electronic device for detection of exhaled gas ...

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2024 5th International Conference on Electrical, Electronic Information and Communication Engineering, EEICE 2024

作者： Fan, Chuanxiu Liu, Xue Yu, Zhichao Du, Changsong Jia, Shengde Liu, Qing Yan, Pengbo Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China Tianjin Chest Hospital Tianjin300384 China

To enable preliminary diagnosis of various diseases in the human body, detecting the concentrations of targeted components in exhaled gases is an effective way without invasive diagnosis. An exhaled gas concentration detection device is proposed in this paper. Firstly, this device employs a high-accuracy analog front end (AFE) for transmitting the concentrations of targeted components to an electronic voltage signal. The core circuit of AFE is a high-precision and low-current constant current source. Secondly, a heating circuit with controllable temperature is also designed to provide an appropriate operating temperature for different types of gas sensor materials. Thirdly, the system applies a microcontroller to directly read multiple-channel gas concentrations and independently control multiple heating circuits. The performance of the device has been validated through simulations. © Published under licence by IOP Publishing Ltd.

关键词： Computer resource management

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Adaptive Neural Tracking Control for Nonlinear Servo System with Unknown Time-Varying Parameters and Uncertainties 42

Adaptive Neural Tracking Control for Nonlinear Servo System ...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Faxiang Gao, Guanbin Na, Jing Wu, Jiande Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581543

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Servomechanisms

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Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion

Automatic Depression Detection Network Based on Facial Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Min Xu, Lingxiang Liu, Lei Wang, Xiaohua Li, Hongbo Yang, Jiaoyun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China Hefei University of Technology National Smart Eldercare International Science and Technology Cooperation Base Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350386226

Artificial intelligence methods offer objectivity and convenience in automatic depression detection, however, current research often neglects the critical role of facial landmarks. This oversight results in insufficient spatial structure information and a lack of detailed local representation, which fails to capture the nuanced semantic information crucial for identifying depression-related clues. To address these issues, we introduce a novel dual-branch network model comprising the Landmark-Image-Landmark Net (LIL Net) and the Global Context Vision Transformer Net (GCVit Net). Through a dual-stream, multiscale, and cross-fusion strategy, LIL Net is designed to extract original facial image features alongside landmark features, prioritizing the detailed semantic information of potential depression clues. LIL Net employs an innovative LIL Attention approach to jointly learn multiscale features from facial landmarks and images, thereby enhancing the model's ability to capture fine-grained depression-related cues. Furthermore, the Multi-scale Feature Fusion (MSFF) module fuses the obtained multiscale features, augmenting the semantic expression of potential depression clues within facial landmarks via attention mechanisms. Meanwhile, the GCVit Net branch network supplements global information by extracting global facial features. Finally, the features from both branches are concatenated to enhance the accuracy of depression degree predictions. Experimental results demonstrate that our model has superior performance in detecting depression compared to existing methods. We release our code at https://***/xlx777/LIL-Net. © 2024 IEEE.

关键词： Artificial intelligence

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An automatic docking method for large-scale sections based on real-time pose measuring and assembly deviation control

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Optoelectronics Letters 2023年第11期19卷 686-692页

作者： QIAO Zhifeng FU Kang LIU Zhenzhong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin300384China

Aiming at the problem of poor accuracy consistency of large sections’docking assembly,an automatic docking method using multiple laser trackers to measure the position and posture of the docking sections in real time was *** the solution of the pose of the docking section,real-time pose measurement of the docking section was realized by establishing a global coordinate system and a coordinate fusion method of three or more laser *** the automatic control of the docking process,the real-time communication protocol and the circular negative feedback control strategy of measurement-adjustment-re-measurement are adopted,and the fully-automated docking of large sections is ***,an experimental verification system was set up,and the docking of the large-scale section reduction models was realized under the requirements of docking accuracy,and the effectiveness of the automatic docking scheme was successfully verified.

关键词： coordinate deviation system

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Advancements in graphene-based composites: A review of the emerging applications in healthcare

Smart Materials in Medicine

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Smart Materials in Medicine 2025年第1期6卷 120-138页

作者： Batool, Farwa Muhammad, Saz Muazzam, Rabia Waqas, Muhammad Ullah, Zia Roy, Shubham Zhang, Yinghe Wang, Ke Guo, Bing Department of Chemistry Lahore Garrison University DHA Phase VI Lahore Pakistan School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology Shenzhen518055 China Government Queen Mary Graduate College Lahore Pakistan

Graphene and related materials are emerging revolutionary materials due to fascinating stimuli-responsive physiochemical properties having widespread applications in developing advanced nano-systems for detection, diagnostics, and therapeutics in biomedical fields. Various materials like metal nanoparticles, polymers, dyes, drugs, and proteins could be combined yielding graphene-based composite materials (GBCMs) to synergistically fulfill different biomedical requirements. The 2D conjugated structure, easy surface functionalization via covalent or van der Waal interactions, and combination with different components for versatile functionalities altogether contribute to the development of GBCM for healthcare applications. This review specifically focuses on GBCMs and discusses specific material properties relevant to their material applications in healthcare. Furthermore, the synthesis, toxicity, and biocompatibility of these GBCMs, and their interactions with biological materials are thoroughly discussed. Importantly, the recent cutting-edge applications in the field of healthcare, including its role in the development of antibacterial, anti-viral, and photothermal therapies, wound healing, drug delivery, neurodegenerative disease therapy, and biosensing based on GBCMs are well-updated. Additionally, the applications of GBCMs in point-of-care devices, wearable electronics, and tissue engineering are well summarized. We hope this review opens up more possibilities to design more innovative healthcare materials derived from graphene. © 2025 The Authors

关键词： Biocompatibility

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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Hierarchical Cooperative Control of Connected Vehicles:From Heterogeneous Parameters to Heterogeneous Structures

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1590-1602页

作者： Manjiang Hu Lingkun Bu Yougang Bian Hongmao Qin Ning Sun Dongpu Cao Zhihua Zhong the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082China Wuxi Intelligent Control Research Institute Hunan UniversityWuxi 214115China the School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving *** this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and *** separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and *** the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback ***-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication *** simulation and field experiment verify the effectiveness of the proposed method.

关键词： Communication delay connected vehicle(CV) heterogeneity string stability vehicle platoon

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A Firmware Vulnerability Detection Method Based on Feature Filtering 21

A Firmware Vulnerability Detection Method Based on Feature F...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Bing Tang, Chongjun Liu, Wenbo Chu, Shihao Dong, Yu ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Dept Zhengzhou China ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Dept Zhengzhou China ZhongYuan University of Technology Dept Zhengzhou China

ISBN: (纸本)9798350329223

Open source code reuse and code cross-platform deployment accelerate the spread of software vulnerabilities, and pose challenges for accurate detection of cross-platform vulnerabilities. The binary vulnerability similarity detection method based on machine learning lacks a fine-grained vulnerability similarity feature filtering mechanism, and cannot solve cross-function vulnerabilities triggered by data dependencies, resulting in a high vulnerability false positive rate in existing solutions, which limits practical applications. Software code vulnerabilities are usually caused by function key statements. Therefore, based on binary function decompilation, we identify the key statement that causes the vulnerability, analyze the cross-function data dependency of the key statements, extract the code similarity features and code vulnerability features of the key statements, and propose a feature filtering-based Binary vulnerability detection method SimFilter. SimFilter calculates the similarity of key statements by comparing the characteristics of function key statements, and infers the similarity of binary function vulnerabilities based on the similarity of key statements. The experimental results show that the vulnerability semantic features extracted by SimFilter are highly interpretable, and the accuracy rate is increased by 10.8% compared with other models. At the same time, we analyzed and evaluated the impact of different similarity features on the accuracy of SimFilter, and gave the best empirical value of the SimFilter model in firmware vulnerability detection. © 2023 IEEE.

关键词： Firmware

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