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2023 China Automation Congress, CAC 2023

作者： Du, Shaochong Chen, Kang Yang, Shijie Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches. © 2023 IEEE.

关键词： Knowledge graph

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

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2023 China Automation Congress, CAC 2023

作者： Chen, Kang Du, Shaochong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations. © 2023 IEEE.

关键词： Reinforcement learning

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Multi-Task Affinity Propagation Based Natural image Matting

Multi-Task Affinity Propagation Based Natural Image Matting

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IEEE International Conference on image processing

作者： Renkai Zhang Nong Sang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

image matting, aiming to accurately extract foreground objects by estimating their opacity against the background, has made remarkable progress through deep-learning approaches. Nevertheless, the majority of these methods require a user-defined auxiliary input, such as a trimap, which limits their applications in real-world scenarios. There are many auxiliary input-free methods that have been proposed by now, and some of them adopt a multi-task learning framework that includes a shared encoder and two separate decoders. However, these methods lack interactions between the two decoders, or interactions are implemented through simple summation or concatenation. Unfortunately, the integration of different features may cause negative transfer and limit the model performance due to the invisible information transmission process. To address the issue, we introduce the Pattern-Affinitive Propagation Module (PAP) to explicitly model cross-task propagation and task-specific propagation. Furthermore, image matting not only requires high-resolution detail features, but also semantic features. However, current CNN-based methods have limited receptive fields, making it challenging to capture global semantic features. Therefore, we design a module that integrates Dilated Convolution and Spectral Transformer (DSM), which can effectively capture global features and enhance global-local feature fusion. Extensive experiments on AM-2k and P3M-10k datasets demonstrate the superiority of our method.

关键词： Fuses Computational modeling Semantics Urban areas information processing Multitasking Feature extraction

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Replace Anyone in Videos

arXiv

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arXiv 2024年

作者： Wang, Xiang Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Recent advancements in controllable human-centric video generation, particularly with the rise of diffusion models, have demonstrated considerable progress. However, achieving precise and localized control over human motion, e.g., replacing or inserting individuals into videos while exhibiting desired motion patterns, still remains challenging. In this work, we propose the ReplaceAnyone framework, which focuses on localizing and manipulating human motion in videos with diverse and intricate backgrounds. Specifically, we formulate this task as an image-conditioned pose-driven video inpainting paradigm, employing a unified video diffusion architecture that facilitates image-conditioned pose-driven video generation and inpainting within masked video regions. Moreover, we introduce diverse mask forms involving regular and irregular shapes to avoid shape leakage and allow granular local control. Additionally, we implement a two-stage training methodology, initially training an image-conditioned pose-driven video generation model, followed by joint training of the video inpainting within masked areas. In this way, our approach enables seamless replacement or insertion of characters while maintaining the desired pose motion and reference appearance within a single framework. Experimental results demonstrate the effectiveness of our method in generating realistic and coherent video content. Copyright © 2024, The Authors. All rights reserved.

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Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

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Complex System Modeling and Simulation 2022年第1期2卷 18-34页

作者： Yindong Shen Leqin Yu Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution *** electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the *** some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still ***,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual ***,we propose the robust optimization model based on a route-related uncertain set to tackle this ***,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the *** effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

关键词： electric vehicle routing problem time windows uncertain demand energy consumption model robust optimization adaptive large neighbourhood search

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Degree of Spiking Neural P Systems Without Delay

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Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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Gait Recognition Algorithm GCGait based on Skeleton 3

Gait Recognition Algorithm GCGait based on Skeleton

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3rd IEEE International Conference on information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Ma, Ru Zhang, Jikai Jia, Mengyu Lv, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Inner Mongolia University of Technology School of Information Engineering Hohhot China

ISBN: (纸本)9781665490788

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method. © 2023 IEEE.

关键词： Complex networks

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Novel measuring techniques with applications in pattern classification and diagnostic process under Pythagorean fuzzy environment

Novel measuring techniques with applications in pattern clas...

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2023 International Conference on New Trends in Computational Intelligence, NTCI 2023

作者： Ejegwa, Paul Augustine Zhang, Yanxiong Li, Haiqing Feng, Yuming Institute of Intelligent Information Processing South China Business College Guangdong University of Foreign Studies Guangdong Guangzhou510545 China Joseph Sarwuan Tarka University Department of Mathematics Makurdi Nigeria Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control Chongqing Wanzhou404100 China

ISBN: (纸本)9798350380859

Distance and similarity measures are significant tools for information measure applicable in real-world under Pythagorean fuzzy domain. Some techniques for the computation of distance and similarity have been investigated between Pythagorean fuzzy sets (PFSs), notwithstanding with low performance indexes. To resolve these setbacks, this paper introduces some new measuring techniques with reliable performance in comparison to similar measures. The new techniques are confirmed with a number of theoretic results to illustrate their aptness as unfailing information measuring methods. Certain numerical experiments are carried out to ascertain the advantages of the proposed measures over the existing measures. We demonstrate the applications of the novel techniques in real-life decision-making situations pertaining to pattern recognition and disease diagnostic process where patterns and diseases are encapsulated in Pythagorean fuzzy pairs. © 2023 IEEE.

关键词： Fuzzy sets

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Distributed Optimization of Heterogeneous Linear Multi-Agent Systems with Unknown Disturbances 63

Distributed Optimization of Heterogeneous Linear Multi-Agent...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Duan, Mengmeng Zhu, Shanying Yang, Ziwen Chen, Cailian Guan, Xinping The authors are with the Department of Automation Shanghai Jiao Tong University Shanghai 200240 ChinaKey Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China and also Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (纸本)9798350316339

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. By applying the primal-dual method and the time-scale separation technique, a distributed dynamic controller without using any disturbance information is proposed. Based on the optimal condition, the relationship between the optimal solution and the equilibrium point of the system is established, and it is shown that the distributed optimization problem is solved provided that the closed-loop system is stable. Then, inspired by the nonsingular perturbation analysis, it is proved that the closed-loop system is exponentially input-to-state stable with respect to the derivative of the disturbance. Numerical examples are provided to illustrate the theoretical results. © 2024 IEEE.

关键词： Closed loop systems

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A semantic segmentation method for UAV image based on improved BiseNetv2 5

A semantic segmentation method for UAV image based on improv...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Guo, Fengjuan Han, Sen Zhao, Jiahang Han, Jiale Xin, Jing Shaanxi Key Laboratory of Integrated and Intelligent Navigation The 20th Research Institute of China Electronics Technology Corporation Shaanxi Xi’an710068 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510668621

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for "Human", the IoU of TBiseNetv2 is about 4.3% higher than that of the popular UNetFormer. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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