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Toward Coordination Control of Multiple Fish-Like Robots:Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1964-1976页

作者： Tianhao Zhang Jiuhong Xiao Liang Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LaboratoryCollege of EngineeringPeking UniversityBeijing 100871China National Engineering Research Center of Software Engineering Peking UniversityBeijing 100871 Institute of Ocean Research Peking UniversityBeijing100871 Peng Cheng Laboratory Shenzhen 518055China

Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists,for which a fundamental but challenging topic is to robustly track the postures of the individuals in real *** requires detecting multiple robots,estimating multi-joint postures,and tracking identities,as well as processing fast in real *** the best of our knowledge,this challenge has not been tackled in the previous *** this paper,to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time,we propose a novel deep neural network-based method,named *** TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation,while the IOL part with long short-term memory considers the motion constraints among joints for precise pose *** satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy,speed,and robustness with most state-of-the-art ***,based on the precise pose estimation and tracking realized by our TAB-IOL,several formation control experiments are conducted for the group of fish-like *** results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working *** believe our proposed method will facilitate the growth and development of related fields.

关键词： Deep neural networks formation control multiple fish-like robots pose estimation pose tracking

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Causality-Guided Context-Aware Multimodal Public Speaking Anxiety Detection for Out-of-Distribution Generalization

Causality-Guided Context-Aware Multimodal Public Speaking An...

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

作者： Tingting Zhang Jiachen Tan Zihua Xiong Bin Wu Chunping Zheng Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China School of Humanities Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Public Speaking Anxiety Detection (PSAD) is a complex and challenging task that involves detecting anxiety through diverse multimodal cues. While deep neural networks have achieved remarkable success in this task, their performance tends to degrade significantly under distribution shifts, especially in Out-Of-Distribution (OOD) scenarios. Given the substantial impact of latent contexts in PSAD, it is crucial to explore how contexts influence OOD generalization. Causality plays a fundamental role in uncovering the relationships between multimodal data and labels. Understanding these causal relationships helps tackle the OOD generalization challenge via causal invariance under context-driven shifts. In this work, we propose a novel Context-Aware Multimodal Adversarial Learning (CAMAL) framework to model and debias complex multimodal contexts in PSAD. A dual adversarial learning strategy jointly optimizes the causal independence between multimodal causal features and contexts. Experimental results on two benchmark datasets demonstrate the superiority of our approach over state-of-the-art baselines under distribution shifts.

关键词： Training Anxiety disorders Cause effect analysis Artificial neural networks Signal processing Benchmark testing Speech Adversarial machine learning Speech processing Context modeling

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Self-Supervised Reinforcement Learning for Out-of-Distribution Recovery via Auxiliary Reward

Self-Supervised Reinforcement Learning for Out-of-Distributi...

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

作者： Yufeng Xie Yinan Wang Han Wang Qingshan Li Xi’an Key Laboratory of Intelligent Software Engineering Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

Recently, the real-world applications of reinforcement learning (RL) have seen the problem of taking actions in an out-of-distribution (OOD) state. However, most existing research is limited to take actions to narrow the visited training distribution and OOD, and does not consider the efficiency to choose such actions. In this paper, we propose a novel approach, called Self-Supervised Reinforcement Learning for OOD recovery via Auxiliary Reward (SRL-AR), to address this issue. By leveraging cumulative reward, we force the representations to discriminate state-action pairs with different returns as auxiliary task. Then, the auxiliary reward calculated from the auxiliary loss is used to generate a new policy that can effectively handle OOD situations. Moreover, we show that our method outperforms prior works in terms of asymptotic performance and sample efficiency on MuJoCo tasks.

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Reconfigurable intelligent Surface-Aided Covert Communications: A Multi-User Scenario

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Xiaoyan Zhao, Pengze Xiao, Han Zheng, Tong-Xing Wang, Wenjie Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering United Kingdom

This paper explores a multi-user covert communication scenario with the assistance of a reconfigurable intelligent surface (RIS). Except a covert user monitored by the warden, there are multiple public users existing in the service area. For the purpose of evaluating the efficacy of the covert communication system, a covert constraint is derived measuring the minimum detection error probability of the warden through the Kullback-Leibler (KL) divergence. Then an optimization problem is formulated to maximize the convert rate, while ensuring the constraints of covertness, transmit power and quality of service (QoS) for public users. A two-step iterative algorithm jointly designing the active and passive beamforming, is put forth to efficiently resolve this non-convex optimization problem. Additionally, the numerical results clearly demonstrate that the proposed RIS-assisted scheme can significantly enhance the covert communication performance on the premise of guaranteeing the public communication performance of the system. © 1967-2012 IEEE.

关键词： Convex optimization

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SFSM: A Serverless Function Scheduling Method for FaaS Applications over Edge Computing

SFSM: A Serverless Function Scheduling Method for FaaS Appli...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Tian Cheng Chen Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University China College of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the function instances with inter-dependencies are scheduled to proximate edge nodes in a distributed manner. However, the heterogeneity and unpredictability of edge networks bring significant challenge in realizing optimal scheduling decision to guarantee execution performance of applications without any prior. In view of this challenge, a Serverless Function Scheduling Method, named SFSM, is proposed in this paper for FaaS applications over edge computing. First, a long-term optimization problem is formulated to reduce completion time and decoupled into time-slot sub-problems via Lyapunov optimization. To avoid the cross-edge redundant data transmission overhead of inter-functions, a two-level graph optimization is designed with vertical and horizontal data merging. Then, SFSM incorporates an online multi-armed bandit-based scheduling algorithm that only requires the context of requests without complete information of edge networks. Finally, extensive experimental results based on real-world datasets demonstrate the effectiveness and superiority of SFSM.

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Optimal Strategies for the Game with Two Faster 3D Pursuers and One Slower 2D Evader 41

Optimal Strategies for the Game with Two Faster 3D Pursuers ...

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第41届中国控制会议

作者： Runqi Zhang Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University National Engineering Research Center of Software Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A pursuit-evasion differential game with two faster pursuers in 3 D space and one slower evader in a 2 D plane is *** pursuers,which have different speeds,attempt to cooperate to capture the evader while the evader struggles to avoid or delay the *** differential games and the concept of isochron,we present a complete solution to this game and develop the optimal strategies for both the pursuers and the *** order to guarantee the optimality of the proposed strategies,the value function of the game is given and is further proved to be the solution of the corresponding Hamilton-Jacobi-Isaacs ***,simulations are carried out with comparisons between the proposed strategies and other typical strategies,the results of which show the optimality of the proposed strategies.

关键词： Pursuit-evasion differential games multi-agent system optimal strategy

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DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for Image Classification 15

DNAT: Multi-scale Transformer with Dilated Neighborhood Atte...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Jiang, Chenfeng Zhou, Quan Mo, Zhiyi Wang, Jing Fan, Yawen Wu, Xiaofu Zhang, Suofei Kang, Bin Nanjing University of Posts & Telecommunications National Engineering Research Center of Communications and Networking China Wuzhou University Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou China Nanjing University of Posts & Telecommunications Department of Internet of Things China

ISBN: (纸本)9798350324662

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on ImageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification. © 2023 IEEE.

关键词： Dilated Neighborhood Attention Image Classification Self-attention visual Transformer

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QianXun: A Novel Enterprise File Search Framework 25

QianXun: A Novel Enterprise File Search Framework

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Si, Chun Qi, Peng Xue, Hongjia Sun, Yan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing100876 China University of Nottingham Computer Science Faculty of Science and Engineering United Kingdom

ISBN: (纸本)9781665405270

With the deepening of the digital transformation, enterprises have accumulated plenty of electronic files. For users, how to quickly find their desired files from the massive resources is a big challenge, which is also a critical concern for enterprises. Although a series of enterprise search engines have emerged in the market, there is a lack of search systems for enterprise files. In this paper, we design a novel enterprise file search framework, named QianXun, which supports searching in both Chinese and English. By introducing technologies such as automatic text summarization, online file previewing, and Knowledge Graph-enhanced search, our proposed framework improves the efficiency of file search as well as the user experience, which is of great significance to enterprises. © 2022 IEEE.

关键词： Knowledge graph

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Exploration of engineering Practice Teaching Guided by Design Thinking 22

Exploration of Engineering Practice Teaching Guided by Desig...

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6th International Conference on Education and Multimedia Technology, ICEMT 2022

作者： Xu, Ke Cai, Yi School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou China

ISBN: (纸本)9781450396455

As a cutting-edge innovative method and tool, Design Thinking has effectively promoted many changes in global business, social and education fields in recent years. This work briefly introduces Design Thinking and related teaching reform, and then summarizes two problems in existing engineering practice teaching. In view of these problems, this work introduces Design Thinking into engineering practice teaching. As its core, Design Thinking Workshops are well designated and organized. Two-semesters explorations show that under the guidance of Design Thinking, engineering practice teaching is more attractive and effective. It can not only help engineering students learn to empathize with end-users and cultivate innovative thinking, but also improve their ability to identify and address real problems. © 2022 ACM.

关键词： engineering education

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Environment-Tolerant Trust Opportunity Routing based on Reinforcement Learning for Internet of Underwater Things

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6348-6360页

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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