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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey university Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning

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A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 661-672页

作者： Dan Zhang Jiabin Hu Jun Cheng Zheng-Guang Wu Huaicheng Yan IEEE Zhejiang university of technology Guangxi Normal university Zhejiang university the East China university of Science and technology

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic ***,a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator *** an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed,so that the priori knowledge of the lumped disturbance is not ***,a new non-singular fast terminal sliding mode(NFTSM) surface is designed by using an arctan function,which helps to overcome the singularity problem and enhance the robustness of the *** on the estimation of the lumped disturbance by the AFTDO,a fixed-time non-singular fast terminal sliding mode controller(FTNFTSMC)is developed to guarantee the trajectory tracking errors converge to zero within a fixed *** settling time is independent of the initial state of the *** addition,the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov ***,the fixed-time NFESM(FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers(SMCs) are *** comparative results confirm that the FTNFTSMC has superior control performance.

关键词： Disturbance observer(DO) fixed-time non-singular sliding mode control robotic manipulator trajectory tracking

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Triphenylphosphine-Assisted Exsolution Engineering on Ruddlesden-Popper Perovskites for Promoting Oxygen Evolution

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Energy & Environmental Materials 2024年第4期7卷 433-441页

作者： Juan Bai Jing Shang Jun Mei Dongchen Qi Ting Liao Ziqi Sun School of Chemistry and Physics Queensland University of Technology Centre for Materials Science Queensland University of Technology School of Materials Science and Engineering Shaanxi University of Science and Technology School of Mechanical Medical and Process Engineering Queensland University of Technology

Metal exsolution engineering has been regarded as a promising strategy for activating intrinsically inert perovskite oxide catalysts toward efficient oxygen evolution *** metal exsolution processes on perovskites are often achieved by using the reducing hydrogen gas; however,this is not effective for the relatively stable phase,such as Ruddlesden-Popper perovskite oxides.

关键词： exsolution oxygen evolution perovskite reconstruction Ruddlesden–Popper

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and technology Wuhan University of Science and Technology School of Future technology South China University of Technology Shenzhen Research Institute of Hunan university School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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A hybrid WUDT-NAFnet for simultaneous source data deblending

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Petroleum Science 2024年第3期21卷 1649-1659页

作者： Chao-Fan Ke Shao-Huan Zu Jun-Xing Cao Xu-Dong Jiang Chao Li Xing-Ye Liu Key Laboratory of Earth Exploration and Information technology of Ministry of Education Chengdu University of Technology State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of Technology College of Geophysics Chengdu University of Technology

Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended ***,deblending algorithms have been developed to separate this ***,deep learning(DL)has been proved its great potential in suppressing the *** most popular DL method employs neural network as a filter to attenuate the blended noise in an iterative estimation and subtraction framework(IESF).However,there are still amplitude distortion and blended noise residual problems,especially when dealing with weak signal submerged in strong *** address these problems,we propose a hybrid WUDT-NAFnet,which contains two *** first network is a wavelet based U-shape deblending transformer network(WUDTnet),incorporated into IESF as a robust regularization term to iteratively separate the blended *** second network is a nonlinear activate free network(NAFnet)designed to recover the event amplitude and further suppress the weak noise residual in *** the hybrid network,the blended noise can be separated purposefully and *** using synthetic and field seismic data demonstrate that the WUDTNAFnet outperforms traditional curvelet transform(CT)based method and the deblending transformer(DT)model in terms of ***,for field applications,the data augmentation method of bicubic interpolation is applied to mitigate the feature difference between synthetic and field ***,the trained network exhibits strong signal preservation ability in numerical field example without requiring additional training.

关键词： Simultaneous-source Deblending Deep learning Transformer

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A FAS approach for stabilization of generalized chained forms: part 1. Discontinuous control laws

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Science China(Information Sciences) 2024年第2期67卷 118-143页

作者： Guang-Ren DUAN Center for Control Theory and Guidance technology Harbin Institute of Technology Center for Control Science and technology Southern University of Science and Technology

In this paper, a type of general nonholonomic systems is proposed, which contains both the Brockett’s two example systems, and their extended n-dimensional chained forms, as special cases. For the stabilization of such systems, a stabilizing controller is proposed based on the fully actuated system(FAS)approach, which is discontinuous at the origin but time-invariant when the open-loop system is time-invariant,and drives the feasible trajectories of the system to the origin exponentially. Furthermore, the proposed FAS approach is also extended to the sub-normal system case and the time-delay system case.

关键词： nonholonomic systems feedback stabilization exponential convergence sub-fully actuated systems time-delay systems

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Image attention transformer network for indoor 3D object detection

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Science China(Technological Sciences) 2024年第7期67卷 2176-2190页

作者： REN KeYan YAN Tong HU ZhaoXin HAN HongGui ZHANG YunLu Faculty of Information technology Beijing University of TechnologyBeijing 100124China

Point clouds and RGB images are both critical data for 3D object detection. While recent multi-modal methods combine them directly and show remarkable performances, they ignore the distinct forms of these two types of data. For mitigating the influence of this intrinsic difference on performance, we propose a novel but effective fusion model named LI-Attention model, which takes both RGB features and point cloud features into consideration and assigns a weight to each RGB feature by attention ***, based on the LI-Attention model, we propose a 3D object detection method called image attention transformer network(IAT-Net) specialized for indoor RGB-D scene. Compared with previous work on multi-modal detection, IAT-Net fuses elaborate RGB features from 2D detection results with point cloud features in attention mechanism, meanwhile generates and refines 3D detection results with transformer model. Extensive experiments demonstrate that our approach outperforms stateof-the-art performance on two widely used benchmarks of indoor 3D object detection, SUN RGB-D and NYU Depth V2, while ablation studies have been provided to analyze the effect of each module. And the source code for the proposed IAT-Net is publicly available at https://***/wisper181/IAT-Net.

关键词： 3D object detection transformer attention mechanism

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A mixed Nash equilibrium solution for visibility-based pursuit-evasion game with multiple obstacles

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Science China(Information Sciences) 2024年第9期67卷 330-332页

作者： Shaoming BU Li LIANG Youqing WANG College of Information Science and technology Beijing University of Chemical Technology

This study primarily investigates a visibility-based pursuitevasion game with multiple obstacles. The pursuer aims to maintain surveillance over the evader as long as possible, while the evader's objective is to escape the pursuer's surveillance as quickly as possible. We use the explicit strategy method to analyze the winning conditions and optimal strategy of the player in the case of corner obstacles and propose a 'Present-Future-Past'(PFP) loop structure to extend the strategy to the case of multiple obstacles.

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Physics-informed deep Koopman operator for Lagrangian dynamic systems

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Science China(Information Sciences) 2024年第9期67卷 146-163页

作者： Xuefeng WANG Yang CAO Shaofeng CHEN Yu KANG Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science University of Science and Technology of China Institute of Advanced technology University of Science and Technology of China

Accurate mechanical system models are crucial for safe and stable control. Unlike linear systems,Lagrangian systems are highly nonlinear and difficult to optimize because of their unknown system *** research thus used deep neural networks to generate linear models of original systems by mapping nonlinear dynamic systems into a linear space with a Koopman observable function encoder. The controller then relies on the Koopman linear model. However, without physical information constraints, ensuring control consistency between the original nonlinear system and the Koopman system is tough, as the learning process of the Koopman observation function is unsupervised. This paper thus proposes a two-stage learning algorithm that uses structural subnetworks to build a physics-informed network topology to simultaneously learn the Koopman observable functions and the system energy representation. In the Koopman matrix learning session, a quadratic-constrained optimization problem is solved to ensure that the Koopman representation satisfies the energy difference matching hard constraint. The proposed energy-preserving deep Lagrangian Koopman(EPDLK) framework effectively represents the dynamics of the Lagrangian system while ensuring control consistency. The effectiveness of EPDLK is compared with those of various Koopman observable function construction methods in multistep prediction and trajectory tracking tasks. EPDLK achieves better control consistency by guaranteeing energy difference matching, which facilitates the application of the control law generated on the Koopman system directly to the original nonlinear Lagrangian system.

关键词： Koopman operator Lagrangian physics-informed nonlinear model predictive control (MPC)

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Optically levitated micro gyroscopes with an MHz rotational vaterite rotor

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Microsystems & Nanoengineering 2024年第3期10卷 219-227页

作者： Kai Zeng Xiangming Xu Yulie Wu Xuezhong Wu Dingbang Xiao College of Intelligence Science and technology National University of Defense TechnologyChangsha 410073China College of Information and Communication National University of Defense TechnologyWuhan 430000China Key Laboratory of Satellite Navigation technology National University of Defense TechnologyChangsha 410073China

The field of levitated optomechanics has experienced significant advancements in manipulating the translational and rotational dynamics of optically levitated particles and exploring their sensing *** concept of using optically levitated particles as gyroscopes to measure angular motion has long been explored but has not yet been proven either theoretically or *** this study,we present the first rotor gyroscope based on optically levitated high-speed rotating *** gyroscope is composed of a micrometer-size ellipsoidal vaterite particle that is driven to rotate at MHz frequencies in a vacuum *** an external angular velocity is input,the optical axis deviates from its initial position,resulting in changes in the frequency and amplitude of the rotational *** analyzing these changes,the angular velocity of the input can be accurately detected,making it the smallest rotor gyroscope in the *** angular rate bias instability of the gyroscope is measured to be 0.08°/s and can be further improved to as low as 10^(−9)°/h theoretically by cooling the motion and increasing the angular moment of the levitated *** work opens a new application paradigm for levitated optomechanical systems and may pave the way for the development of quantum rotor gyroscopes.

关键词： particles rotational angular

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