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A Domain Generalization Method for E-nose Cross-platform Identification of Ignitable Liquids

IEEE Sensors Journal 2025年

作者： Song, Tian-Shu Hou, Hui-Rang Chen, Zhen-Peng Ding, Hui-Hang Meng, Qing-Hao Tianjin University Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China

Electronic nose (e-nose) can carry out odor fingerprinting and identification, exhibiting several advantages such as speed, low cost and portability. The e-nose technology allows objective, accurate and real-time detection and recognition of ignitable liquids (ILs), which provide technical support for fire cause determination and fire scene reproduction. Metal oxide semiconductor (MOS) gas sensors have certain technical limitations, and multiple e-nose platforms assembled using the same sensor array will produce different output responses when sampling the same gas. Manual data labeling and repetitive training of models involve a huge workload. This study proposes a novel domain generalization method to mitigate this problem. During the training process, we assign pseudo-domain labels iteratively for the e-nose response signals obtained from multiple source domains by clustering. This approach avoids the situation where multiple potential domains can exist for the same e-nose. The highlights of our approach are as follows: 1) The training paradigm of traditional domain generalization methods that require domain labels is not used;2) Pseudo-domain labels are generated for the samples according to each cycle iteration during training;3) The feature extractor is trained to learn domain-invariant features by adversarial learning based on pseudo-domain labels;4) The statistics of each hidden layer of the neural network are used to cluster the e-nose signals and generate pseudo-domain labels from them. Five e-noses, each with 16 identical sensor channels, are used to recognize four types of ILs. Our method achieves an average cross-platform recognition accuracy of 85.2%. © 2001-2012 IEEE.

关键词： Deep neural networks

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Dual model biosensor integrated with peroxidase-like activity and self-assembly for uric acid detection

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Journal of Materials Chemistry B 2025年 2025 May 27页

作者： Liang, Dingyitai Ding, Ziqi Ding, Yushu Tang, Wenxuan Yang, Shouzhi Xu, Xiaoyu Wang, Yuning Qian, Kun State Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering and Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200030 China

Uric acid (UA), the final product of purine metabolism, is a crucial biomarker for gout diagnostics and highly related to various metabolic diseases. Precise detection of UA levels in serum and urine enables disease diagnosis and guides treatment. Combining the advantages of colorimetry and laser desorption/ionization mass spectrometry (LDI-MS), we developed a dual-model biosensor based on hollow Cu2O@Au nanocubes (h-Cu2O@Au NCs) for UA detection. The h-Cu2O@Au NCs demonstrated excellent peroxidase (POD)-like activity and were used to rapidly detect UA by colorimetric assay, with a linear range of 0.05-2 mM and limit of detection (LOD) of 35.71 μM. Moreover, the h-Cu2O@Au NCs achieved enrichment and detection of UA via the liquid-liquid interface self-assembly-assisted LDI-MS, with a linear range of 0.01-0.5 mM, LOD of 15.6 μM, and reproducibility of 2O@Au NCs achieved UA detection in serum samples by colorimetry assay and in urine samples by LDI-MS, obtaining results consistent with the commercial UA assay kit (72-511 μM for serum, R2 = 0.956 and 2-9 mM for urine, R2 = 0.876), presenting potential in the rapid and sensitive detection of UA in clinic. © 2025 The Royal Society of Chemistry.

关键词： Mass spectrometry

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Observability Analysis and Reduced-Order Observer Design for a Super-Coiled Polymer-Driven Robotic Eye

Observability Analysis and Reduced-Order Observer Design for...

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IEEE Conference on Decision and Control

作者： Sunil Kumar Rajendran Qi Wei Ningshi Yao Feitian Zhang Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Bioengineering George Mason University Fairfax VA USA Department of Advanced Manufacturing and Robotics and the State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

With the aid of a robotic eye platform, several ocular motor disorders such as strabismus can be studied by ophthalmologists and biomedical researchers to better under-stand the biomechanisms of the human eye. Our previous work modeled a 2-DOF robotic eye driven by Super-Coiled Polymer (SCP) artificial muscles and presented a Deep Deterministic Policy Gradient (DDPG) learning-based controller. The control policy requires access to the full system states that include the orientation of the robotic eye and temperature changes of the SCPs. While the angular orientations of the robotic eye can be determined using embedded sensors or image-processing of the visual feed, it is quite laborious and expensive to measure the temperatures of the slender SCP muscles without affecting robot dynamics. To address this problem, this paper designs a linear reduced-order state observer based on the linearization of the nonlinear dynamics of the robotic eye. The linearized model is analytically shown to be fully observable along any trajectory within the operation range. To quantify the local observability of the dynamical system, local unobservability indices and local estimation condition numbers are determined along trajectories of the system states. The performance of the designed observer is tested through simulation in both open-loop and closed-loop foveation control of the robotic eye.

关键词： Temperature sensors Temperature measurement Visualization Observers Robot sensing systems Trajectory Nonlinear dynamical systems

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Correction to: Stereo reconstruction from microscopic images for computer-assisted ophthalmic surgery

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International journal of computer assisted radiology and surgery 2024年 2024 Nov 26页

作者： Rebekka Peter Sofia Moreira Eleonora Tagliabue Matthias Hillenbrand Rita G Nunes Franziska Mathis-Ullrich Carl Zeiss AG Oberkochen Germany. rebekka.peter@***. Laboratory for Surgical Planning and Robotic Cognition (SPARC) Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. rebekka.peter@***. Carl Zeiss AG Oberkochen Germany. Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal. Laboratory for Surgical Planning and Robotic Cognition (SPARC) Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany.

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Development in the application of laser-induced breakdown spectroscopy in recent years:A review

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Frontiers of physics 2021年第2期16卷 45-69页

作者： Lian-Bo Guo Deng Zhang Lan-Xiang Sun Shun-Chun Yao Lei Zhang Zhen-Zhen Wang Qian-Qian Wang Hong-Bin Ding Yuan Lu Zong-Yu Hou Zhe Wang Wuhan National Laboratory for Optoelectronics(WNLO) Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesSheyang 110016China School of Electric Power South China University of TechnologyGuangzhou 510640China State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Laser SpectroscopyShanxi UniversityTaiyuan 030006China State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong UniversityXi'an 710049China Key Laboratory of Photonic Information Technology School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China School of Physics Dalian University of TechnologyNo.2 Linggong RoadGanjingzi DistrictDalian 116024China Optics and Optoelectronics Laboratory Ocean University of ChinaQingdao 266100China State Key Lab of Power Systems Department of Energy and Power EngineeringTsinghua UniversityBeijing 100084China

Laser-induced breakdown spectroscopy(LIBS)has been widely studied due to its unique advantages such as remote sensing,real-time multi-elemental detection and none-to-little *** the efforts of researchers around the world,LIBS has been developed by leaps and ***,in recent years,more and more Chinese LIBS researchers have put tremendous energy in promoting LIBS *** is worth mentioning that the application of LIBS in a specific field has its special application background and technical difficulties,therefore it may develop in different stages.A review summarizing the current development status of LIBS in various fields would be helpful for the development of LIBS technology as well as its applications especially for Chinese LIBS community since most of the researchers in this field work in *** the present work,we summarized the research status and latest progress of main research groups in coal,metallurgy,and water,*** on the current research status,the challenges and opportunities of LIBS were evaluated,and suggestions were made to further promote LIBS applications.

关键词： laser-induced breakdown spectroscopy application

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science The Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model’s performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis.

关键词： Deep learning Systematics Image analysis Market research Skin Convolutional neural networks Lesions Global communication Medical diagnostic imaging Skin cancer

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Infrastructure and city ontologies

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2022年第2期176卷 43-52页

作者： Varga, Liz McMillan, Lauren Hallett, Stephen Russell, Tom Smith, Luke Truckell, Ian Postnikov, Andrey Rodger, Sunil Vizcaino, Noel Perkins, Bethan Matthews, Brian Lomax, Nik Department of Civil Environmental and Geomatic Engineering Infrastructure Systems Institute University College London London United Kingdom School of Water Energy and Environment Cranfield University Cranfield United Kingdom Environmental Change Institute Oxford University Oxford United Kingdom Software Engineering Department De la Rue Team Valley Trading Estate Gateshead United Kingdom Lincoln Agri-Robotics University of Lincoln Lincoln United Kingdom Zentrum Klinische Studien Universitätsklinikum Freiburg Freiburg Germany Scientific Computing Department Science and Technology Facilities Council Uk Research and Innovation Rutherford Appleton Laboratory Harwell Campus Didcot United Kingdom School of Geography University of Leeds Leeds United Kingdom

The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use of cases that rely on real-world integration of disparate systems, the need for semantic congruence across boundaries and the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems, but they are particularly apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated, undermining the potential for integration to solve societal challenges such as net zero, resilience to climate change, equity and affordability. This paper reports on findings of a literature review on infrastructure and city ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to the literature and provide avenues for further research on (a) belief systems that underpin non-top-level ontologies and the potential for interference from them, (b) the need for a small number of top-level ontologies and translation mechanisms between them and (c) clarity on the role of standards and information systems in the adaptability and quality of data sets using ontologies. A gap is also identified in the extent that ontologies can support more complex automated coupling and data transformation when dealing with different scales. © 2023 ICE Publishing: All rights reserved.

关键词： Ontology

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Admittance Control based Hemiplegic Exoskeleton Motion Control

Admittance Control based Hemiplegic Exoskeleton Motion Contr...

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

作者： He, Bailin Zhou, Xin Huai, Yaping Liu, Jiaqing Li, Pengbo Wang, Can Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In this paper, a method based on admittance control is developed to improve patients' comfort wearing an exoskeleton robot for rehabilitation training. In our previous work, we designed a modular and convenient hemiplegic exoskeleton robot. The mechanical structure will cause stiffness when it comes to contact with the human body in practical applications. We introduced admittance control to improve the flexibility of the exoskeleton robot and improve the subjects' comfort. This paper considers coupling of model between the exoskeleton and the human body as the human-computer interaction force of the system at the main human-computer interaction point. Measuring the interaction force between exoskeleton members and limbs, the work uses the virtual admittance model to correct the system's inherent characteristics and studies the control method's stability with Lyapunov's theory. The experiment collected the subject movement information which was converted the movement information of the exoskeleton. Human-computer interaction forces in the thigh and calf were measured while the exoskeleton assisted the subject's movement. The experimental results show that adding the admittance controller reduces the human-computer interaction force from 5g to 2g and 6g to 3g, respectively. © 2022 IEEE.

关键词： Human computer interaction

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An LMI-based Composite Nonlinear Controller Design for Robust Stabilization of a Knee Rehabilitation Exoskeleton Robot

An LMI-based Composite Nonlinear Controller Design for Robus...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia RISC Lab Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a novel robust control strategy specifically designed for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot, focusing on position control. Our approach addresses several key challenges, including state constraints, parameter uncertainties, solid and viscous frictions, and external disturbances. We suggest a composite controller that combines a nonlinear controller with a linear state feedback controller in order to successfully address these issues. By utilizing a quadratic Lyapunov function, we derive the expression of the nonlinear control law and establish the Linear Matrix Inequality (LMI) conditions for computing the matrix gain of the linear controller, ensuring robust stabilization of the exoskeleton robot to the desired position. These conditions are derived through the application of advanced mathematical techniques such as the matrix inversion lemma, the Young inequality, the Schur complement, and the S-procedure lemma. Numerical results show the effectiveness of our suggested control law, demonstrating the continuous convergence of the intended angular position to the real one, even when external disturbances and uncertainties are present. Overall, our research not only presents a novel methodology for robust control but also delivers promising results that underscore the efficacy of our approach in achieving stable and reliable performance for 1-DoF knee exoskeleton robots.

关键词： Robust control Uncertain systems State feedback Uncertainty Exoskeletons Position control Solids Linear matrix inequalities Reliability Robots

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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