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12th International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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A Novel Two-Layer Memristive Spiking Neural Network with Spatio-Temporal Backpropagation 14

A Novel Two-Layer Memristive Spiking Neural Network with Spa...

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14th International Conference on Advanced Computational Intelligence

作者： Zhang, Yaozhong Jiang, Mingxuan Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Min Sch Artificial Intelligence & Automat Wuhan 430070 Peoples R China

ISBN: (数字)9781665470452

ISBN: (纸本)9781665470469;9781665470452

In this paper, a novel two-layer memristive spiking neural network (MSNN) with spatio-temporal backpropagation (STBP) algorithm is proposed. The MSNN is composed of a memristive crossbar array, ten leaky integrate-and-fire (LIF) neurons and a winner-take-all (WTA) module. The memristive crossbar array with one memristor (1M) synapse performs the multiplication and accumulation without additional storage units. LIF neurons accumulate input current and fire spikes. WTA module ensures that only one neuron fires for one input pattern. The MSNN consumes a little power because there are no amplifiers or digital CMOS elements in the circuit. In order to train the memristive conductance, the LIF model is discretized and the gradients are propagated by the STBP algorithm. Furthermore, we perform a 6x5 black and white image classification based on the MSNN in PSPICE. Results verify that the MSNN realizes high recognition rates even under severe random noise and stuck-at faults

关键词： memristor spiking neural network winner-takeall image classification low-power circuit

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Opinion separation in leader-follower coopetitive social networks

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NEUROCOMPUTING 2021年 434卷 90-97页

作者： Liang, Haili Yuan, Fanli Zhou, Zhao Su, Housheng Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200444 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Luoyu Rd 1037 Wuhan Peoples R China

Opinion dynamics on social networks describe the time evolution of the opinions within the leader-follower coopetitive (cooperative-competitive) interactions. When does opinion formation result in opinion separation with the single leader? This paper investigates a continuous-time model of such opinion separation problems with signed networks. Our model combines the positive/negative weighting rule of Degroot to describe the impact of their neighbors and the reflected leader-follower mechanism of Jadbabaie to describe trust/distrust relationships from followers to the leader. When the leader is stationary, we establish a necessary and sufficient condition, which ensure all opinions finally converge to bipartite consensus as long as the network topology is structurally balanced. When the leader is dynamic, a sufficient condition is proposed to ensure the opinions of followers move with the opinion of leader or the opposite value in the same velocity. Subsequently, we introduce Laplacian schemes, the framework of signed networks and matrix analysis to develop the stability of networked systems. Numerical simulation examples are given to illustrate the performance of the proposed leader-follower coopetitive systems. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Opinion dynamics Multi-agent systems Leader?follower control Coopetitive networks

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UniAnimate: Taming Unified Video Diffusion Models for Consistent Human image Animation

arXiv

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arXiv 2024年

作者： Wang, Xiang Zhang, Shiwei Gao, Changxin Wang, Jiayu Zhou, Xiaoqiang Zhang, Yingya Yan, Luxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Alibaba Group Hangzhou310052 China University of Science and Technology of China Hefei230026 China

Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitations: i) an extra reference model is required to align the identity image with the main video branch, which significantly increases the optimization burden and model parameters;ii) the generated video is usually short in time (e.g., 24 frames), hampering practical applications. To address these shortcomings, we present a UniAnimate framework to enable efficient and long-term human video generation. First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model. Second, we propose a unified noise input that supports random noised input as well as first frame conditioned input, which enhances the ability to generate long-term video. Finally, to further efficiently handle long sequences, we explore an alternative temporal modeling architecture based on state space model to replace the original computation-consuming temporal Transformer. Extensive experimental results indicate that UniAnimate achieves superior synthesis results over existing state-of-the-art counterparts in both quantitative and qualitative evaluations. Notably, UniAnimate can even generate highly consistent one-minute videos by iteratively employing the first frame conditioning strategy. Code and models will be publicly available. Project page: https://***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Animation

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Fast Candidate Region Extraction for SAR Ship Target 37

Fast Candidate Region Extraction for SAR Ship Target

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Panpan Luo, Haibo Xu, Zheng He, Miao Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers

DAFormer: Depth-aware 3D Object Detection Guided by Camera M...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Junbin Gao Hao Ruan Bingrong Xu Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Automation Wuhan University of Technology Wuhan China

3D object detection plays a critical role in autonomous driving perception. Although multi-view-based perception solutions have made significant progress, their performance is still far from being ready for practical use. The estimation of pixel depth is dependent on camera intrinsic properties, which led us to explore a depth-aware model guided by camera parameters. Our contribution in this paper is the DAFormer, which incorporates camera parameters and position-aware image features to detect 3D objects. The depthaware module uses camera parameters to reweight image features and estimate depth, enhancing and offsetting the 3D position embedding. The object query uses depth information enhanced features for end-to-end 3D detection by Attention layers. DAFormer achieves impressive results on the standard nuScenes dataset without any additional embellishments.

关键词： Solid modeling Three-dimensional displays Estimation Object detection Detectors Cameras Feature extraction

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Coverage control of unicycle multi-agent network in dynamic environment

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2021年第0期

作者： Sun, Qihai Liu, Zhi-Wei Chi, Ming Dou, Yangming He, Dingxin Qin, Yijie Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

This paper studies the coverage control problem of an unicycle multi-agent network with external disturbance in a dynamic environment which is described by a time-varying density function. It is a challenging task to design a coverage control to simultaneously handle the external disturbance, underactuated model and time-varying density function. Based on Voronoi partition, we proposed a coverage control approach which can maximize the metric function via robust tracking the Voronoi centroid with external disturbance. It is worth to mentioning that the angle of motion of the unicycle agent can align with the Voronoi centroid in finite time. Finally, the effectiveness of the proposed control is shown by simulation results.

关键词： coverage optimization multi-agent system time-varying density function unicycle model Voronoi partition

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FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC 31

FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC

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2024 International Conference on image processing

作者： Wang, Dayong Yu, Junyi Lu, Xin Dufaux, Frederic Guo, Hongwei Guo, Hui Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Honghe Univ Sch Engn Mengzi Yunnan Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.

关键词： VVC SCC content type fast coding mode decision BDPCM

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Analysis of Government-Taxation Methods and Maritime Companies’ Strategies Under Emission-Reduction Incentives

SSRN

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SSRN 2023年

作者： Wang, Jian Wenbo, Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

The current emission-reduction trend in the shipping industry is inevitable. This study uses a multi-stage Stackelberg game to establish a low-carbon maritime supply chain model that includes a government, a carrier, and a port. The government can choose to impose carbon taxes only on the port or on both the port and carrier, while there are different power structures: port-led or carrier-led. Equilibrium solutions in different scenarios indicate that the leading port’s profit is not affected by the implementation of a carbon tax, while the carrier, surprisingly, hopes to join the carbon-tax policy. Both the government and the carrier prefer a bilateral carbon-tax scenario led by the carrier, while the port has the opposite preference. In the process of seeking coordination opportunities, we find that, driven by shippers’ low-carbon preferences, the maritime supply chain exhibits a "triple increasing" phenomenon, which means higher cargo volume, more emission reduction, and higher freight prices. The government and carrier can control the occurrence of this phenomenon and promote an all-win scenario through the use of carbon taxes and subsidies. We provide coordination strategies for unilateral carbon taxes in the maritime supply chain under different power structures and demonstrate the important role of the government in coordination. This provides a new direction for the implementation of carbon taxes in the maritime supply chain. © 2023, The Authors. All rights reserved.

关键词： Taxation

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Jointly detecting and multiple people tracking by semantic and scene information

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NEUROCOMPUTING 2020年 412卷 244-251页

作者： Pi, Zhixiong Qin, Huai Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

In this paper, we propose a new method for online multiple people tracking, which combines the detection process and the single object tracking process, and establishes the interactions between them. The detector detects objects in the still images which ignores the sequential information. Meantime, the single object tracker does not use the category semantic information during tracking. To take both the sequential and semantic information into account, we exchange information among the detector and the trackers. More specifically, the trackers deliver sequential information to the detector by providing the detector with the extra proposals. The detector supplements each tracker with the robust semantic information by using bounding box regression to modify the tracking result. Besides, the interactions also happen among the trackers through the occlusion speculation, the perspective model interpretation and the trajectory merging process. The experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art MOT methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Online multiple object tracking Detection Semantic information Scene information

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