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Development of an atmospheric fluorocarbon plasma jet generated by pulse modulated microwave discharge

Plasma science and technology 2023年第11期25卷 91-100页

作者：陈思乐任鑫柳陈兆权徐笑娟程涛李平张冠军卢新培 School of Electrical and Information Engineering Anhui University of TechnologyMa’anshan 243032People’s Republic of China School of Electrical and Information Engineering Anhui University of Science and TechnologyHuainan 232001People’s Republic of China State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical EngineeringXi’an Jiaotong UniversityXi’an 710049People’s Republic of China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and TechnologyWuhan 430074People’s Republic of China

Atmospheric fluorocarbon plasma plays an important role in the surface modification of insulating materials like *** existing fluorocarbon plasma is usually generated by dielectric barrier discharge,which has a low concentration of reactive species and may cause insufficient surface *** work attempts to develop an atmospheric fluorocarbon plasma jet using a coaxial transmission line resonator by microwave discharge with locally enhanced electric field and high *** gas temperature is reduced by pulse modulation *** kinds of working gases,pure CF_(4),Ar/CF_(4)and He/CF_(4),are utilized to generate the atmospheric microwave fluorocarbon plasma *** discharge images,optical emission spectra,electron densities and gas temperatures are studied *** results show that the Ar/CF_(4)plasma jet has the best comprehensive performance,such as strong discharge intensity and controllable gas *** electron density of the Ar/CF_4plasma jet has a magnitude of 10~(20)m^(-3),indicating a higher density than that of the frequently used dielectric barrier *** the other conditions unchanged,the gas temperature at the end of the Ar/CF_(4)plasma jet can be reduced from 410.2 to 347.3 K by decreasing the duty cycle of the modulated pulse from 0.5 to ***,the microwave Ar/CF_(4)plasma jet is considered to be a promising fluorocarbon plasma source for surface fluorination of polymers.

关键词： microwave discharge atmospheric pressure plasma jet fluorocarbon plasma(Some figures may appear in colour only in the online journal)

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High-frequency enhanced ultrafast compressed active photography

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Opto-Electronic Advances 2025年第1期8卷 32-43页

作者： Yizhao Meng Yu Lu Pengfei Zhang Yi Liu Fei Yin Lin Kai Qing Yang Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame ***,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame ***,a high-frequency enhanced compressed active photography(H-CAP)is *** uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling *** sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image *** superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,*** applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.

关键词： ultrafast compressed imaging high-frequency enhanced sampling spectral-temporal transform transient processes high-fidelity reconstruction

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A Secure Parameter Self-Tuning Control Scheme for Vehicle Platoons under DoS Attacks 13

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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13th IEEE International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Han, Qiaoni Cheng, Guowen Tang, Zhenning Liu, Chang School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (纸本)9798350315196

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security. © 2023 IEEE.

关键词： Fuzzy control

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Data quantity governance for machine learning in materials science

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National science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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LOWER LIMB GAIT RECOGNITION METHOD BASED ON KNOWLEDGE DISTILLATION 10

LOWER LIMB GAIT RECOGNITION METHOD BASED ON KNOWLEDGE DISTIL...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wang, Mengdi Wang, Shaohong Ma, Chao Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology Beijing China Beijing Key Lab of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology Beijing China

ISBN: (纸本)9781837242672

The need for assistive devices such as lower limb exoskeletons is steadily growing, making quick and precise gait recognition essential for optimal operation of these apparatuses. The knowledge distillation method was applied to gait recognition in this work. First, we took participant lower limb gait data, which we then resampled with step frequency taken into account. Next, multi-layer perceptron (MLP) with distinct layers were modeled by educators and students. The process of knowledge distillation was used to improve the accuracy of participant activity recognition by transferring knowledge from the sophisticated teacher model to the lightweight student model. On the training set, the teacher model's accuracy average was 96.4%. Following the process of knowledge distillation, the accuracy of the student model was 95.48%, representing a 1.12% improvement compared to the non-distilled model, with only 0.53M parameters and shorter training duration. The results demonstrate that knowledge distillation can improve the recognition speed and stability of gait recognition models, enabling more accurate and reliable results within the same patient population, thus providing more effective support for medical diagnosis and rehabilitation treatment. © The Institution of engineering & technology 2024.

关键词： Exoskeleton (Robotics)

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A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： Quanbo Ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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Computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

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IEEE Transactions on Image Processing 2023年 32卷 5283-5295页

作者： Liu, Meiqin Xu, Chenming Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods. The code is available at https://***/ruhig6/JNMR. © 1992-2012 IEEE.

关键词： Interpolation

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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Improved vision-only localization method for mobile robots in indoor environments

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Autonomous Intelligent systems 2024年第1期4卷 153-165页

作者： Gang Huang Liangzhu Lu Yifan Zhang Gangfu Cao Zhe Zhou School of Automobile and Traffic Engineering Wuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and ScienceXiangyang 441053China

To solve the problem of mobile robots needing to adjust their pose for accurate operation after reaching the target point in the indoor environment,a localization method based on scene modeling and recognition has been ***,the offline scene model is created by both handcrafted feature and semantic ***,the scene recognition and location calculation are performed online based on the offline scene *** improve the accuracy of recognition and location calculation,this paper proposes a method that integrates both semantic features matching and handcrafted features *** on the results of scene recognition,the accurate location is obtained through metric calculation with 3D *** experimental results show that the accuracy of scene recognition is over 90%,and the average localization error is less than 1 *** results demonstrate that the localization has a better performance after using the proposed improved method.

关键词： Deep learning Mobile robot Scene recognition Visual localization

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