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IEEE International Conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference Data models

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Nanxin Huang Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models.

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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Adaptive Backstepping Sliding Mode Control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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2023 China Automation Congress, CAC 2023

作者： Li, Zhenjia Cao, Weihua Gan, Chao Wen, Mingming School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Guangzhou Marine Geological Survey Guangzhou510000 China

ISBN: (纸本)9798350303759

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches. © 2023 IEEE.

关键词： Backstepping

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Stability of Nonlinear systems via Hybrid Delayed Impulses

Stability of Nonlinear Systems via Hybrid Delayed Impulses

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第43届中国控制会议

作者： Huannan Zheng Wei Zhu Ya Deng School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper studies the stability problem of nonlinear systems via hybrid delayed impulses,in which stabilizing impulses and destabilizing impulses can occur simultaneously. In order to analyze the hybrid delayed impulses,the average impulsive weight(AIW) is used. Together with average impulsive interval(AII) and average impulsive delay(AID),some Lyapunov-based conditions for globally asymptotically stable(GAS) are established. Results show that delays in impulses have positive effects to the desired performance. The effectiveness of the theoretical results is demonstrated through two numerical examples.

关键词： Stability Nonlinear systems Hybrid Delayed Impulses

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Trajectory set Empowered Hypergraph Transformer for Mobile Sensor Based Traffic Prediction

Trajectory set Empowered Hypergraph Transformer for Mobile S...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanyuan Zhang Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Traffic speed prediction is vital for intelligent transportation systems. However, most existing methods focus on costly static sensors. In contrast, utilizing GPS devices from vehicles as mobile sensors offers a cost-effective means to gather dynamic traffic data. Despite the presence of historical trajectory data, mobile sensor-based traffic prediction remains under-explored. Existing methods often treat trajectories as substitutes for static sensors, missing the full utilization of the spatial-temporal signals within the complete trajectory set. To address this, we propose TrajHGT, a novel trajectory set empowered hypergraph transformer model that captures trafficrelated spatial-temporal features through adaptive attention and fusion mechanisms in both the trajectory hypergraph space and the road graph space. Real dataset experiments demonstrate the superiority of TrajHGT.

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Research on Anti-Swing Strategies for Bridge Cranes

Research on Anti-Swing Strategies for Bridge Cranes

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2024 China Automation Congress, CAC 2024

作者： Wang, Shiqi Hou, Enguang Zheng, Haoyuan Jiang, Yuyi Shen, Zhen Xiong, Gang The College of Rail Transit Shandong Jiaotong University Jinan250399 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266144 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China State Key Laboratory of Multimodal Artifcial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350368604

A study is conducted on the LH5T-13.5M electric crane, focusing on the swing characteristics of the load during steel coil lifting operations and the displacement issue of the crane. Initially, the system model is formulated using the Lagrange dynamic equations, translating the practical challenges into a solvable mathematical framework and deriving the transfer function of interest. Subsequently, the development of three control strategies is elucidated, formulated on the basis of the established model: a conventional PID control system, a fuzzy PID control system, and an adaptive proportional factor fuzzy neural PID controller. These systems are simulated and validated using Simulink. A comparative experimental setup is employed to evaluate performance. The results show that when the step input signal is 1, the maximum absolute load swing Angle of the traditional PID, fuzzy PID and adaptive scale factor fuzzy neural PID controller are 0.185°, 0.054° and 0.049° respectively for the working condition 1 experiment. The percentage overshoot corresponding to the wheelbarrow is 37.1%, 40.0%, and 30.5%, respectively. The findings indicate that the introduced fuzzy neural PID controller, equipped with an adaptive scaling factor, exhibits superior stability and effectiveness in maintaining the crane’s operation within the optimal range of parameters. © 2024 IEEE.

关键词： Bridge cranes

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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L-Type Calcium Channel Modulates Low-Intensity Pulsed Ultrasound-Induced Excitation in Cultured Hippocampal Neurons

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Neuroscience Bulletin 2024年第7期40卷 921-936页

作者： Wen-Yong Fan Yi-Ming Chen Yi-Fan Wang Yu-Qi Wang Jia-Qi Hu Wen-Xu Tang Yi Feng Qian Cheng Lei Xue State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Department of Physiology and Neurobiology School of Life SciencesFudan UniversityShanghai 200438China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education Tongji UniversityShanghai 200070China Center for Rehabilitation Medicine Department of Pain ManagementZhejiang Provincial People's HospitalAffliated People's HospitalHangzhou Medical CollegeHangzhou 310014China Department of Critical Care Medicine Shanghai General HospitalShanghai Jiaotong University School of MedicineShanghai 200080China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China Research Institute of Intelligent Complex Systems Fudan UniversityShanghai 200433China

As a noninvasive technique,ultrasound stimulation is known to modulate neuronal activity both in vitro and in *** latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted ***,the underlying mechanism of low-intensity pulsed ultrasound(LIPUS)-induced neuro-modulation effects is still ***,we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca^(2+)homeostasis in cultured hippocampal *** whole-cell patch clamp recording,we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents(sEPSCs)and also increases the amplitude of sEPSCs in hippocampal *** phenomenon lasts for>10 min after LIPUS *** with Ca^(2+)imaging,we clarified that LIPUS increases the[Ca^(2+)]cyto level by facilitating L-type Ca^(2+)channels(LTCCs).In addition,due to the[Ca^(2+)]cyto elevation by LIPUS exposure,the Ca^(2+)-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein *** work suggests that LIPUS regulates neuronal activity in a Ca^(2+)-dependent manner via *** may also explain the multi-activation effects of LIPUS beyond *** stimulation potentiates spontaneous neuronal activity by increasing Ca^(2+)influx.

关键词： Low-intensity pulsed ultrasound Neuromodulation L-type calcium channel Hippocampal neuron

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Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

Compound Fault Separation and Diagnosis Method Based on FSA-...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781665401159

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method. © 2021 IEEE.

关键词： Failure analysis

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