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A collisional-radiative model for atmospheric-pressure low-temperature air discharges

Chinese Journal of Aeronautics 2025年第4期38卷 55-69页

作者： Ximing ZHU Lu WANG Wei CUI Bangdou HUANG Yun WU Min JIA Yang ZHAO Bowen ZHENG Ji LI Desheng ZHOU Daren YU School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China National Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering UniversityXi’an 710038China Beijing International S&T Cooperation Base for Plasma Science and Energy Conversion Institute of Electrical EngineeringChinese Academy of SciencesBeijing 100190China

Plasma-assisted combustion technology has been a hot spot in aero-engines *** electron density is a key discharge parameter related to the *** latter has been considered playing an important role in the above applications bythe kinetic *** this work,an atmospheric pressure air plasma collisional-radiative model con-sidering the excited states of atomic nitrogen and oxygen is built based on previous widely kineticinvestigations of molecules and radicals,as well as their excited *** excited states,especiallythe atomic nitrogen and oxygen states were less investigated in previous *** emission inten-sity distributions from the model have a good agreement with those measured in the glide arcplasma with two discharge modes,as well as the microwave *** on the kinetics of molec-ular and atomic emitting states,the line-ratio method is presented to determine the electron *** N_(2)(337 nm)/O(844 nm)and N_(2)(337 nm)/NO(γ)line ratios are used for the glide arc plasma andmicrowave plasma torch,***,the kinetics of the excited states involved with twoline-ratios are also investigated in the two types of *** with the atmospheric pres-sure actinometry method,the kinetic effect of the plasma-assisted combustion can be revealed quan-titatively in the future.

关键词： CR model Optical emission spectrum Air atmosphere plasma Electron density Glide arc plasma ignitor

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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science China(Information sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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A double-layer radiative cooling coating that utilizes the refractive index difference between layers to achieve extremely high solar reflectivity

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science China(Technological sciences) 2024年第10期67卷 3182-3189页

作者： WANG FuQiang LI ChunZhe YANG ZhenNing XIE WeiXin LI Xiang XU ZengHui YAN YuYing CHENG ZiMing School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of New Energy Harbin Institute of Technology at WeihaiWeihai 264209China China Construction Eco-Environmental Protection Technology Co. Ltd.Suzhou 215123China Faculty of Engineering University of NottinghamNottingham NG72RDUK

Passive daytime radiative cooling(PDRC)technology has great potential in reducing cooling energy *** order to further improve the spectral performance of PDRC coatings,current researchers mostly focus on the selection and size design of functional particles,while ignoring the optical properties enhancement effect caused by the interlayer *** this study,based on the principle that the refractive index difference between layers enhanced the backscattering performance of the solar spectrum,we proposed and manufactured a double-layer PDRC coating with polyvinylidene difluoride(PVDF)as the filmforming material in the upper layer and polydimethylsiloxane(PDMS)as the film-forming material in the lower layer,both filled with Al_(2)O_(3) and SiO_(2) *** double-layer PDRC coating exhibited excellent spectral performance that a high solar reflectivity of 98%and an emissivity of 0.95 at the“atmospheric window”*** comparison,the solar spectrum reflectivity of the single-layer PDRC coatings based on PVDF and PDMS of the same thickness was 95%and 94.7%,*** tests showed that the PDRC coating achieved a temperature decrease of up to 7.1℃ under direct sunlight at noon *** addition,the PDRC coating had excellent weather resistance,water resistance,and other basic *** article opens up a new idea and provides methodological guidance for the design of double-layer PDRC coatings.

关键词： double-layer radiative cooling coating spectral modulation interlayer refractive index solar energy radiative transfer

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An improved method for classifying depth-based human actions using self-adaptive evolutionary technique

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 157-173页

作者： Pareek, Preksha Thakkar, Ankit Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

Automatic Human Action Recognition (HAR) using RGB-D (Red, Green, Blue, and Depth) videos captivated a lot of attention in the pattern classification field due to low-cost depth cameras. Feature extraction in action recognition is an important aspect. As compared to Depth Motion Maps (DMM), Depth Motion Maps–Local Binary Pattern (DMM–LBP) provides compact representation of features. After extracting features using DMM–LBP, Principal Component Analysis (PCA) is used for dimensionality reduction. For classification task, randomly generated input weights of Extreme Learning Machine (ELM) can lead to non-optimal results. Therefore, in this paper, we have used an improved learning algorithm named Self-adaptive Differential Evolution (SaDE) ELM for action classification. In the proposed approach, DMM–LBP is used for feature extraction and SaDE–ELM is used for action classification. To evaluate strength of the proposed approach, experiments are performed on four public datasets, namely, MSRAction3D, MSRDaily Activity3D, UTD–MHAD, and MSRGesture3D. The proposed approach gives better accuracy compared to existing approaches Kernel ELM (KELM), l2-Collaborative Representation Classifier (l2-CRC), and Probabilistic CRC (ProCRC) methods. We have also presented statistical significance of results in the paper. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Local binary pattern

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Multiscale damage and failure behavior of SiCf/SiC composites with initial defects

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Ceramics International 2025年第14期51卷 19366-19381页

作者： Wang, Ben Wang, Jialin Lyu, Qihui Guo, Zaoyang Research Institute of Interdisciplinary Science & School of Materials Science and Engineering Dongguan University of Technology Guangdong Dongguan523808 China School of Science Harbin Institute of Technology Guangdong Shenzhen518055 China

A multiple damage mechanisms-based multiscale model is developed and implemented within a multiscale simulation framework, which incorporates the statistical distributions of SiC fiber strength and SiC matrix microcrack size to capture the mechanical response and defect-induced nonlinear behavior of SiCf/SiC composites. The micro- and mesoscale representative volume element (RVE) models accounting for the initial defects, are established to simulate the key deformation and damage evolution at the fiber bundle and material levels, and whose predictions are in good agreement with the experimental results for SiCf/SiC minicomposites and 2D SiCf/SiC composites. Based on the validated multiscale models, the effects of the multiscale components and defects on the failure envelopes of 2D SiCf/SiC composites under combined loadings are studied. Subsequently, a unified failure criterion with minimal non-physical parameters is formulated. This multiscale modeling strategy provides an effective way to predict the strength of SiCf/SiC composite structures related to the micro-mesoscopic characteristics. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Ceramic matrix composites

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Enhancing patient-independent detection of freezing of gait in Parkinson’s disease with deep adversarial network

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Neural Computing and Applications 2025年 1-21页

作者： Fahad, Md Shah Ranjan, Ashish Kumar, Gautam Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science and Engineering C. V. Raman Global University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India

Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, traumas, and psychological repercussions, significantly impacting the patient’s quality of life. The use of accelerometer data derived from sensors put on a patient’s body to detect FoG has previously been proposed using convolutional neural network (CNN)-based deep learning algorithms. Here, we combine a CNN + Long short-term Memory (LSTM)-attention model to detect FoG episodes—a first for the detection of FoG using the accelerometer data. CNN facilitates automatic feature extraction from the accelerometer data itself. The output from the CNN is fed to the LSTM network, which is known for capturing sequential information. Further, the attention mechanism introduces relative focus on individual sub-sequences during the training of the LSTM network. The proposed model is made patient independent using adversarial training. The proposed model achieved improvements of +7.60% (without adversarial training) and + 8.36% (with adversarial training) in the sensitivity values over state of the art. This is obtained with little or no compromise in specificity values. The corresponding improvements in accuracy values are +3.81 and +2.23%, respectively. These findings imply that the proposed method can detect FoG gaits satisfactorily and can be effective in achieving accurate monitoring and gait assistance for Parkinson’s disease patients during daily life and rehabilitation therapy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Accelerometers

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computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma Khan Chowdhury Tanoy Debnath Anichur Rahman Md.Saikat Islam Khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult *** Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal *** existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and *** research efficiently focuses on Sign Language Recognition to overcome previous *** importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our ***,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,***“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.For“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,F1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%***,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,F1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static Feature extraction Convolution2D Fully connected layer Dropout

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QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Active learning Online learning Malicious traffic Concept drift Data imbalance

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Failure modes determination and load-bearing capacity evaluation of concrete columns under seismic loads by ANNs

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Soft Computing 2024年第13-14期28卷 8361-8377页

作者： Hao, Meijing Chang, Wei Xu, Chonghao School of Architecture and Civil Engineering Harbin University of Science and Technology Harbin150080 China Key Lab of Structures Dynamic Behavior and Control Ministry of Education Harbin Institute of Technology Harbin150090 China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of Technology Harbin150090 China School of Civil Engineering Harbin Institute of Technology 202 Haihe Road Nangang District Harbin China

The failure modes of concrete columns can be applied to judge the ductility of concrete columns under seismic loads. In addition, the load-bearing capacity of concrete columns was an important parameter in evaluating the seismic performance of concrete columns under seismic loads. However, it was difficult to determine the failure modes and evaluate the load-bearing capacity of concrete columns under seismic loads with the application of high-strength concrete. In this paper, two ANNs, including a genetic algorithm optimization-based support vector machine (GA-SVM) and an ANFIS, were developed to determine the failure modes of concrete columns under seismic loads. The main investigated parameters, including specimens' shear span ratio, the compressive strength of concrete, the yield strength and volumetric ratio of stirrups, and the yield strength and ratio of longitudinal reinforcement, were applied as input variables to the models. The proposed models were trained and tested using 247 data sets from previous studies. The proposed models had high reliability and predictability to determine the failure modes of concrete columns under seismic loads. Furthermore, an ANFIS network was developed to evaluate the load-bearing capacity of concrete columns, which had high reliability and predictability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Failure modes

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