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18th Chinese intelligent Systems Conference, CISC 2022

作者： Gao, Libin Hu, Zhongyi Li, Zuoyong Lu, Xingjin Li, Rui Tong, Yinsheng Lin, Dehou College of Computer Science and Aritificial Intelligence Wenzhou University Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou325035 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811962028

In this study, a novel training method is innovatively proposed to address the problem of poor generalisation of trained models due to imbalance in Alzheimer’s disease (AD) data. The method alternates AD image data with dog and cat data from natural images for collaborative learning training across datasets, enhancing the generalisation ability of the models. At the same time, inspired by current work related to attention mechanisms under the field of computer vision, the model is reconstructed and an attention module is added to the model to further improve the accuracy of the model. The experimental results show that the method has good classification performance and provides new ideas for the diagnosis of other brain diseases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Fractional-Order Sliding Mode Finite Time control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill

Fractional-Order Sliding Mode Finite Time Control for the Sp...

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第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

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Distributed impulsive control for secure consensus of nonlinear dynamical networks with node delay under denial-of-service attacks

Distributed impulsive control for secure consensus of nonlin...

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作者： Shi, Yu-Lun Liu, Zhi-Wei School of Artificial Intelligence and Automation Huazhong University of Science and Technology Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

This paper investigates secure consensus problems of nonlinear dynamical networks (NDNs) with node delay under denial-of-service (DoS) attacks, which can cause a break in communication links. It will lead to packet loss. To ensure the secure consensus in this scenario, we propose an effective impulsive control which only based on sampled information from neighbor nodes. Based on the Lyapunov stability theory and the impulsive comparison principle, an upper bound of the average impulse interval is derived to ensure the exponential consensus of NDNs under DoS attacks, and the convergence rate can be estimated. Finally, a numerical example is provided to check the theory results. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Time delay

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Defocus to focus: photo-realistic bokeh rendering by fusing defocus and radiance priors

arXiv

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arXiv 2023年

作者： Luo, Xianrui Peng, Juewen Xian, Ke Wu, Zijin Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

We consider the problem of realistic bokeh rendering from a single all-in-focus image. Bokeh rendering mimics aesthetic shallow depth-of-field (DoF) in professional photography, but these visual effects generated by existing methods suffer from simple flat background blur and blurred in-focus regions, giving rise to unrealistic rendered results. In this work, we argue that realistic bokeh rendering should i) model depth relations and distinguish in-focus regions, ii) sustain sharp in-focus regions, and iii) render physically accurate Circle of Confusion (CoC). To this end, we present a Defocus to Focus (D2F) framework to learn realistic bokeh rendering by fusing defocus priors with the all-in-focus image and by implementing radiance priors in layered fusion. Since no depth map is provided, we introduce defocus hallucination to integrate depth by learning to focus. The predicted defocus map implies the blur amount of bokeh and is used to guide weighted layered rendering. In layered rendering, we fuse images blurred by different kernels based on the defocus map. To increase the reality of the bokeh, we adopt radiance virtualization to simulate scene radiance. The scene radiance used in weighted layered rendering reassigns weights in the soft disk kernel to produce the CoC. To ensure the sharpness of in-focus regions, we propose to fuse upsampled bokeh images and original images. We predict the initial fusion mask from our defocus map and refine the mask with a deep network. We evaluate our model on a large-scale bokeh dataset. Extensive experiments show that our approach is capable of rendering visually pleasing bokeh effects in complex scenes. In particular, our solution receives the runner-up award in the AIM 2020 Rendering Realistic Bokeh Challenge. © 2023, CC BY-NC-ND.

关键词： image fusion

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization

A Fast-trained and Generalized Spiking Neural Network for Ro...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Chiyang Liu Xinxing Chen Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Training Location awareness Q-learning Mechatronics Pipelines Training data Spiking neural networks Data models Mobile robots Dispersion

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Infrastructure-Based Vehicle Localization System for Indoor Parking Lots Using RGB-D Cameras

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 61-69页

作者：曹炳全贺越生庄瀚洋杨明 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China

Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet ***,infrastructure-based cooperative driving systems have become a means to realizing intelligent *** this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking *** the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of ***,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass ***,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth ***,a complete hardware system was built to verify the feasibility of our solution in a real-world *** in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.

关键词： infrastructure-based RGB-D camera vehicle localization point cloud registration

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Multiple Unmanned Surface Vehicle Coverage control in Dynamic Environments

Multiple Unmanned Surface Vehicle Coverage Control in Dynami...

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Neuromorphic Computing (ICNC), International Conference on

作者： Hong Chen Dingxin He Huantao Mao Yong Yang Zhi-Wei Liu School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

This paper investigates the area coverage problem for multiple Unmanned Surface Vehicles (USVs) in dynamic environments. Multiple USVs are employed to execute the coverage task using the Voronoi Partition method for task allocation. Parameter adaptive laws are employed to estimate the parameters of the sensing function and the Voronoi cell centroids for updating the target positions in dynamic environments. Additionally, considering the influence of water surface disturbances, a nonholonomic underactuated model analysis is conducted for the USV under non-full constraints, resulting in a motion strategy for USVs. This enables the multiple USVs to effectively cover the task area. Finally, simulation results are presented to validate the effectiveness of the coverage scheme designed in this paper and the ability of USVs to effectively cover dynamic environments.

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Thermal Safety Analysis and Dynamic control Strategy for Mode Switching Process of Reversible Solid Oxide Cell System

Thermal Safety Analysis and Dynamic Control Strategy for Mod...

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Chinese control Conference (CCC)

作者： Wei Zhao Guoqiang Liu Zexin Li Xi Li Key Laboratory of Image Processing and Intelligent Control of Education Ministry School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China

Reversible solid oxide cells (rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both functions. When used in dynamic operation with intermittent electrical power sources, rSOC system switches from the fuel cell (SOFC) to electrolyzer (SOEC) and vice versa depending on load and grid peculiarities. This can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the system. In the present work, a system-level dynamic model of rSOC is established and validated against experimental data. Subsequently, detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed, and a temperature management controller based on Model Predict control method (MPC) is proposed. Gas flow and temperature at the stack inlet are controlled by air flow and bypass valves, avoiding problems associated with temperature overshoot during transient operation. The simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety, which indicates the competitiveness of the controller.

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Relative-Posture-Fixed Model Predictive Human-Following control with Visibility Constraints in Obstacle Environments 8

Relative-Posture-Fixed Model Predictive Human-Following Cont...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Yikun Huang, Jian Yu, Jinqi Zhu, Yaonan Hasegawa, Yasuhisa School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China Nagoya University Department of Micronano Mechanical Science and Engineering Nagoya464-8603 Japan

ISBN: (纸本)9798350300178

The relative-posture-fixed human following is critical for the human-robot interaction and cooperation in daily scenes such as domestic service and healthcare. However, few of previous researches have discussed the difficulties faced by mobile robots in the real world, including the limitation of sensing range and hindrance from obstacles. For practicality, in this paper we investigate the relative-posture-fixed following control with collision avoidance and visibility constraints. The control task is formulated as a receding-horizon optimization problem and solved under the Nonlinear Model Predictive control (NMPC) framework, while an Extended Kalman Filter (EKF) based human trajectory predict algorithm is integrated into the robot controller. The effectiveness of the proposed algorithm is verified in a simulation, and the results demonstrate a promising performance of our controller for the human-following task in complicated environments. © 2023 IEEE.

关键词： Human robot interaction

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When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

arXiv

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arXiv 2023年

作者： Liu, Tianqi Ye, Xinyi Zhao, Weiyue Pan, Zhiyu Shi, Min Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain non-local feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-to-line search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://***/TQTQliu/ET-MVSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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