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IEEE International Conference on Image Processing

作者： Jun Chen Wei Yu Xin Tian Jun Huang Jiayi Ma 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 Electronic Information School Wuhan University Wuhan China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

As a vital technology in the field of image processing, numerous visible and infrared fusion methods have emerged with the aim of generating a single image containing salient targets and abundant details. However, owing to the influence of target motion or camera shake, source images may suffer from motion blur, resulting in blurred edges and unrecognizable objects. To address this issue, this paper bridges the gap between deblurring and fusion tasks and proposes a joint motion deblurring and fusion network for visible and infrared image (MDbFusion). On the one hand, we innovatively merge motion deblurring task into network design, which effectively ensures the capacity of MDbFusion to process images in the extreme condition. On the other hand, an Adaptive Weight Module (AWM) is designed to calculate contribution between visible and infrared features, which solves the channel contrast between two tasks, greatly reducing the complexity of network. Extensive experiments demonstrate that the MDb-Fusion outperforms state-of-the-art (SOTA) fusion algorithms in terms of preserving texture details and quantitative metrics. Furthermore, we also compare with a two-step fusion strategy that first deblurring then fusion, both with SOTA methods. The results reveal superiority of our framework in coupling and reciprocity between two tasks.

关键词： Measurement Couplings Adaptive systems Image edge detection Cameras complexity theory Decoding

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Mechanical design paradigm based on ACP method in parallel manufacturing 1

Mechanical design paradigm based on ACP method in parallel m...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Shimeng Wang, Yutong Wang, Xiao Wang, Feiyue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781665433372

Parallel Manufacturing is a new manufacturing paradigm in industry, deeply integrating informalization, automation, and artificial intelligence. In this paper we propose a new mechanical design paradigm in Parallel Manufacturing based on ACP method. The key is to regard the design procedure based on artificial design and emulation method as two independent procedures, which can be modeled as a parallel system. The design procedure based on ACP method does not include a real system, which is an inventive extension of the traditional parallel system. This method can be implemented with social information by introducing the definition of SDV, SDM, and intelligent Design Manager, making it highly adaptive for social manufacturing and Parallel Manufacturing. © 2021 IEEE.

关键词： Manufacture

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Multiscale Feature Fusion Module for Prohibited Item Detection

Multiscale Feature Fusion Module for Prohibited Item Detecti...

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6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Ruiqi Xu Chirui Zhang School of Automation China University of Geosciences Wuhan Hubei China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan Hubei China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

X-ray security check is a prevalent placement measure that plays a vital role in ensuring public safety. However, detecting prohibited items poses a complex challenge due to their variable sizes within X-ray images. In this study, we tackle this issue by curating a novel dataset through the integration of open-source data and introducing a Multiscale Feature Fusion Module. This module facilitates robust detection by amalgamating features across different scales. Extensive experiments verified that the superiority of our method compared to existing methods on the prohibited items test set constructed in this paper demonstrates extremely well the practical effectiveness of our proposed method.

关键词： Signal processing Feature extraction Public security Security Data mining X-ray imaging

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Hierarchical Adaptive Tracking control for Mobile Robots With Multiple Constraints

Hierarchical Adaptive Tracking Control for Mobile Robots Wit...

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2021 China Automation Congress, CAC 2021

作者： Zhao, Chenghao Zheng, Shiqi Li, Haiming Jian, Haitao Wang, Shihao China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China

ISBN: (纸本)9781665426473

This paper studies the mobile robots with multiple constraints based on path planning of A-star algorithm. A hierarchical adaptive control method is presented to handle multiple constaints. On the upper layer, a Astar path planning algorithm is designed. On the lower layer, a new controller is provided. Then, we design barrier Lyapunov function and adaptive RBF neural network control method to make sure that tracking error and velocity error converges to the specified set within a finite amount of time. Compared with other existing works, this system considers multiple consraints include tracking error, constraints velocity constraints and output saturation constraint, provides guarantee for the safe operation of the mobile robot. In addition, because of the unknown conditions on the ground, system also considers the impact of slipping. Computer simulation result demonstrates the effectiveness of control method. In the experimental verification, we use Astar algorithm to generate the appropriate path as the desired trajectory of the mobile robot. The experiment proved the stability of control method. © 2021 IEEE

关键词： Mobile robots

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Design of a Constant Current Output High Frequency AC-DC Converter for Battery Charging Applications 26

Design of a Constant Current Output High Frequency AC-DC Con...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wei, Zihao Song, Xiaoxian Jin, Liangwei Huang, Dingbang Zhang, Sheng Fang, Zhijian School of Future Technology China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

How to realize the high power factor, high efficiency, miniaturization and high power density of AC-DC converter is the key problem of battery charging applications. In this paper, an isolated AC-DC converter is proposed. The input voltage is rectified by the first stage AC-DC conversion, and the output is stabilized by the series resonant converter at the secondary stage. A sinusoidal modulation method is adopted to realize the constant current output characteristic independent of load. Then through the output current control loop and the PFC control loop, the constant current output and input high power factor are realized. The design of high switching frequency and resonant frequency provides conditions for high power density and miniaturization of converter design. Finally, the simulation of rated power of 960W was built by Matlab-simulink, which verified the rationality of the converter design. © 2025 IEEE.

关键词： State of charge

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Improved Shufflenet V2 Network with Attention for Speech Emotion Recognition

SSRN

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SSRN 2024年

作者： Udeh, Chinonso Paschal Chen, Luefeng Du, Sheng Liu, Yulong Li, Min Wu, Min 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

This paper introduces a novel algorithm for recognizing speech emotions, utilizing an enhanced version of the ShuffleNet V2 network. Previous studies typically analyze speech signals solely from the time or frequency domain, resulting in the loss of detailed information. Our algorithm initially converts speech data into an acoustic spectrogram to address this limitation, preserving rich original information across both time and frequency domains. This approach optimizes feature extraction by leveraging the capabilities of deep convolutional networks to capture local changes in images. Furthermore, we integrated the attention mechanism from ECA-Net into the ShuffleNet V2 model. This incorporation enhances the extraction of significant features, amplifying the expression of features strongly correlated with the emotional state conveyed in speech while suppressing irrelevant ones. In addition to these enhancements, we replace the ReLU activation function with Hardswish to deepen the neural network, thereby improving feature information without compromising model accuracy. Finally, the training stage incorporates the hot restart Cosine Annealing Learning Rate to optimize the network further. Simulation experiments are conducted on the enterface'05 dataset to evaluate the accuracy of the proposed algorithm. The experimental results show that the recognition rate of the method is higher than the existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

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自动化学报（英文版） 2021年第10期8卷 1657-1666页

作者： Ting Zhang Wenjie Song Mengyin Fu Yi Yang Meiling Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081 Nanjing University of Science and Technology Nanjing 210014China

Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20％. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.

关键词： Autonomous vehicle intersection motion prediction prior trajectories model turning intention

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Dual-mode FMF to SOI chip edge coupler

Dual-mode FMF to SOI chip edge coupler

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Zhang, Weilun Ye, Mengyuan Li, Yunlong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Dual-mode edge coupler for few mode fiber (FMF) to silicon chip is proposed. LP01 and LP11 modes could be efficiently coupled into TE0 and TE1 modes in waveguides via the proposed coupler in simulation. © Optica ...

ISBN: (纸本)9781957171005

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High Q silicon micro-ring resonator based on elliptical bending waveguides

High Q silicon micro-ring resonator based on elliptical bend...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Li, Yunlong Ye, Mengyuan Zhang, Weilun Liu, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

By adopting elliptical bending waveguides, a silicon micro-ring resonator with footprint ~ 440 μm and Q factor ~ 6.2×106 is proposed and demonstrated. © Optica Publishing Group 2021

ISBN: (纸本)9781957171005

By adopting elliptical bending waveguides, a silicon micro-ring resonator with footprint ~ 440 μm and Q factor ~ 6.2×106 is proposed and demonstrated. © Optica Publishing Group 2021

关键词： Waveguides

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Data-driven control of consensus tracking for discrete-time multi-agent systems

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Journal of the Franklin Institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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