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Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 685-699页

作者： Jie Hou Xianlin Zeng Gang Wang Jian Sun Jie Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Electronic and Information Engineering Tongji UniversityShanghai 200082and also with the National Key Laboratory of Autonomous Intelligent Unmanned Systemsthe School of AutomationBeijing Institute of TechnologyBeijing 100081China IEEE

This paper considers distributed stochastic optimization,in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a *** optimization problems are usually tackled by variants of projected stochastic gradient ***,projecting a point onto a feasible set is often *** Frank-Wolfe(FW)method has well-documented merits in handling convex constraints,but existing stochastic FW algorithms are basically developed for centralized *** this context,the present work puts forth a distributed stochastic Frank-Wolfe solver,by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over *** is shown that the convergence rate of the proposed algorithm is O(k^(-1/2))for convex optimization,and O(1/log_(2)(k))for nonconvex *** efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.

关键词： Distributed optimization Frank-Wolfe(FW)algorithms momentum-based method stochastic optimization

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Analysis of Acceleration Data Using Low-Power Embedded Devices to Detect Gear Faults 19

Analysis of Acceleration Data Using Low-Power Embedded Devic...

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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Rupprecht, Bernhard Sendlbeck, Stefan Vogel-Heuser, Birgit Brederlow, Ralf Knoll, Erich Stahl, Karsten The Institute of Automation and Information Systems Tum School of Engineering and Design Technical University of Munich Germany The Institute of Machine Elements Tum School of Engineering and Design Technical University of Munich Germany Tum School of Computation Information and Technology Technical University of Munich Circuit Design Germany

ISBN: (纸本)9798350320695

Gear condition monitoring can prevent unexpected downtimes or sudden failure of machinery. Since gear damage usually results from tooth contact, data for reliable fault detection should be acquired as close as possible to this engagement to reduce other components' disturbances (such as vibrations). One typical gear damage mechanism is pitting. Although the detection of gear pitting using acceleration data is already covered in research, methods with integrated sensors and electronics into the gear (in-situ) are still in their infancy. Most fault detection approaches still rely on external high-performance measurement systems unsuitable for in-situ integration. Thus, this paper proposes an algorithm pipeline for detecting gear pitting using acceleration data suitable for low-power embedded devices, such as Microcontrollers (MCUs). Downsampling provides the minimum required acceleration data sample rate necessary for detection. It is the basis for future work on suitable sensor and hardware selection. Finally, implementing the algorithm pipeline on a PC and a low-power ARM-Cortex M0+ MCU shows its applicability. © 2023 IEEE.

关键词： Fault detection

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Distributed formation control of multiple aerial vehicles based on guidance route

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Chinese Journal of Aeronautics 2023年第3期36卷 368-381页

作者： Jinyong CHEN Rui ZHOU Guibin SUN Qingwei LI Ning ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007China AVIC Xi’an Flight Automatic Control Research Institute Xi’an 710076China

Formation control of fixed-wing aerial vehicles is an important yet rarely addressed problem because of their complex dynamics and various motion constraints,such as nonholonomic and velocity *** guidance-route-based strategy has been demonstrated to be applicable to fixed-wing ***,it requires a global coordinator and there exists control lag,due to its own *** this reason,this paper presents a fully distributed guidance-route-based formation approach to address the aforementioned ***,a hop-count scheme is introduced to achieve distributed implementation,in which each aircraft chooses a neighbor with the minimum hop-count as a reference to generate its guidance route using only local ***,the model predictive control algorithm is employed to eliminate the control lag and achieve precise formation shape *** addition,the stall protection and collision avoidance are also ***,three numerical simulations demonstrate that our proposed approach can implement precise formation shape control of fixed-wing aircraft in a fully distributed manner.

关键词： Formation shape control Fixed-wing aircraft Guidance route Hop-count estimation Model predictive control

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A dense triple-level attention-based network for surgical instrument segmentation

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Multimedia Tools and Applications 2024年 1-20页

作者： Yang, Lei Wang, Hongyong Bian, Guibin Liu, Yanhong School of Electrical and Information Engineering Zhengzhou University Henan Province Zhengzhou City450001 China Multi-modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In robot-assisted surgery, precise and autonomous segmentation of surgical instruments is pivotal yet challenged by complexities such as intricate surgical scenarios, variable perspectives, low-contrast imaging, diverse instrument dimensions and forms, alongside class imbalance issue. While deep learning has significantly progressed in pixel-wise image segmentation, prevailing end-to-end models grapple with limitations including inadequate context understanding, inefficient feature extraction, and constrained receptive fields, leading to blurred boundaries and loss of local details. To surmount these challenges and enhance segmentation accuracy, this paper proposes a Dense Triple-Attention Network, named DTA-Net, specifically tailored for pixel-level segmentation of surgical instruments in robot-assisted interventions. Proposed DTA-Net introduces a dense attention block to bolster contextual representation, tackling the deficiency in capturing extensive surgical contexts. In addition, it further integrates a Triple-Attention (TA) block to refine feature processing, thereby extracting intricate pixel relationships and enhancing detail retrieval. Complementarily, a Multi-Scale Context Fusion (MCF) mechanism is devised to consolidate multi-resolution features, ensuring comprehensive context exploitation. Extensive evaluations on benchmark datasets, Kvasir-Instrument and Endovis2017, demonstrate that proposed DTA-Net outperforms state-of-the-art models in terms of segmentation accuracy and completeness, which underscores the potential in advancing the precision of robotic surgical interventions through refined instrument segmentation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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Privacy-Preserving Pseudonym Schemes for Personalized 3D Avatars in Mobile Social Metaverses 6

Privacy-Preserving Pseudonym Schemes for Personalized 3D Ava...

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6th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024

作者： Su, Cheng Luo, Xiaofeng Liu, Zhenmou Kang, Jiawen Hao, Min Xiong, Zehui Yang, Zhaohui Huang, Chongwen School of Automation Guangdong University of Technology Guangzhou China School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350366143

The emergence of mobile social metaverses, a novel paradigm bridging physical and virtual realms, has led to the widespread adoption of avatars as digital representations for Social Metaverse Users (SMUs) within virtual spaces. Equipped with immersive devices, SMUs leverage Edge Servers (ESs) to deploy their avatars and engage with other SMUs in virtual spaces. To enhance immersion, SMUs incline to opt for 3D avatars for social interactions. However, existing 3D avatars are typically generated through scanning the real faces of SMUs, which can raise concerns regarding information privacy and security, such as profile identity leakages. To tackle this, we introduce a new framework for personalized 3D avatar construction, leveraging a two-layer network model that provides SMUs with the option to customize their personal avatars for privacy preservation. Specifically, our approach introduces avatar pseudonyms to jointly safeguard the profile and digital identity privacy of the generated avatars. Then, we design a novel metric named Privacy of Personalized Avatars (PoPA), to evaluate effectiveness of the avatar pseudonyms. To optimize pseudonym resource, we model the pseudonym distribution process as a Stackelberg game and employ Deep Reinforcement Learning (DRL) to learn equilibrium strategies under incomplete information. Simulation results validate the efficacy and feasibility of our proposed schemes for mobile social metaverses. © 2024 IEEE.

关键词： Differential privacy

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Gait Generation for the Multi-legged Robot with Injured Legs 41

Gait Generation for the Multi-legged Robot with Injured Legs

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第41届中国控制会议

作者： Ming Zeng Zhijing Wang Yu Ma Jiazhe Liu Baofeng Zhang Yunke Nie Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

When multi-legged robots work in dangerous and complex environments,their legs are easily damaged,resulting in a sharp decline in their movement ability,or even completely out of *** this paper,the gait generation method combined deep reinforcement learning with bionic gait restrictions is presented for multi-legged robots with injured *** this study,we first built an ant gait video acquisition platform using the Camponotus Japonicus as the research object,and on this basis established a gait dataset of injured ***,the gait characteristics of ants in the injured state were ***,our proposed gait generation method combining bionic gait constraints with deep reinforcement learning was compared with the reinforcement-learning-based method in the MATLAB simulation *** test results show that the training reward values of our proposed algorithm are 1-2 times higher than those of the reinforcement learning method alone,and at the same time,the moving speed of the injured robot is also nearly twice as fast.

关键词： Injured multi-legged robot,Ant gait,Gait generation,Reinforcement learning Bionic constraints

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An Improved Object Detection Algorithm for Assembly Line Garbage Sorting 41

An Improved Object Detection Algorithm for Assembly Line Gar...

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第41届中国控制会议

作者： Ming Zeng Jiazhe Liu Xiangzhe Lu Zhijing Wang Yuanhao Li Siying Li Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Garbage object detection is of central importance to achieve accurate garbage *** this work,we propose an improved object detection algorithm for sorting complex garbage objects on the assembly *** algorithm utilizes the Faster RCNN as the baseline and improves its performance with several tips,including deformable convolution,PAFPN,mixup,cutout,color jitter,multi-scale training and ImageNet transfer *** the lack of public datasets for the garbage object detection task,we synthesize a highly realistic multi-object garbage detection dataset,M-GarbageNet,with 20000 training images,5000 validation images and 5000 test ***,we evaluate our algorithm than Faster RCNN,and obtain a 8.2%relative improvement on the M-GarbageNet dataset.

关键词： Garbage object detection Improved Faster RCNN M-GarbageNet Image augmentation

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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Unformer: A Transformer-Based Approach for Adaptive Multiscale Feature Aggregation in Underwater Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1024-1037页

作者： Qing, Yuhao Wang, Yueying Yan, Huaicheng Xie, Xiangpeng Wu, Zhengguang Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

Underwater imaging is often compromised by light scattering and absorption, resulting in image degradation and distortion. This manifests as blurred details, color shifts, and diminished illumination and contrast, thereby hindering advancements in underwater research. To mitigate these issues, we propose Unformer, an innovative underwater image enhancement (UIE) technique that leverages a transformer-based architecture for multiscale adaptive feature aggregation. Our approach employs a multiscale feature fusion strategy that adaptively restores illumination and detail features. We reevaluate the relationship between convolution and transformer to develop a novel encoder structure. This structure effectively integrates both long-range and short-range dependencies, dynamically combines local and global features, and constructs a comprehensive global context. Furthermore, we propose a unique multibranch decoder architecture that enhances and efficiently extracts spatial context information through the transformer module. Extensive experiments on three datasets demonstrate that our proposed method outperforms other techniques in both subjective and objective evaluations. Compared with the latest methods, Unformer has improved the peak signal-to-noise ratio (PSNR) by 19.5% and 14.8% respectively on the LSUI and EUVP datasets. The code is available at: https://***/yhflq/Unformer. © 2024 IEEE.

关键词： Light scattering

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Sparse Measurement Algorithm Execution Time Prediction on Heterogeneous Edge Devices for Early Stage Software-Hardware Matching

Sparse Measurement Algorithm Execution Time Prediction on He...

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Industrial Cyber-Physical systems (ICPS)

作者： Bernhard Rupprecht Birgit Vogel-Heuser Jannik Möhrle Dominik Hujo Yizhi Wang Institute of Automation and Information Systems School of Engineering and Design Technical Technical University of Munich

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

The design and implementation of edge computing solutions needs elaborate knowledge about the inter-dependencies of software and hardware, which are also often developed parallel to meet performance requirements like time constraints. However, the suitability of hardware to execute a given algorithm and vice versa is often assessed by time-consuming trial-and-error approaches. For algorithm assessment, the execution time is crucial. However, existing execution time estimation approaches usually rely on either thorough timing models for the underlying hardware or vast amounts of measurements. Consequently, these approaches are not feasible for an early design stage, where an assessment with limited effort is crucial. Thus, this paper tries to overcome those limitations by comparing a parametric and a non-parametric execution time prediction approach suitable for an early design stage. The evaluation with four edge devices out of heterogeneous categories using measured data is a first attempt at generalizability. A selection of four different algorithms applicable in smart manufacturing and benchmarking ensures the approaches’ broad applicability. The parametric and non-parametric model comparison shows the trade-off between source code analysis and performing measurements.

关键词： Source coding Software algorithms Benchmark testing Predictive models Prediction algorithms Hardware Software

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