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arXiv 2025年

作者： Xu, Yuanze Dai, Ming Cai, Wenxiao Yang, Wankou School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Province Nanjing210096 China Advanced Ocean Institute of Southeast University Jiangsu Province Nantong226019 China Stanford University StanfordCA94305 United States

Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning. However, most existing methods primarily focus on localizing objects captured by UAVs through complex part-based representations, often overlooking the unique challenges associated with UAV self-positioning, such as fine-grained spatial discrimination requirements and dynamic scene variations. To address the above issues, we propose the Context-Enhanced method for precise UAV Self-Positioning (CEUSP), specifically designed for UAV self-positioning tasks. CEUSP integrates a Dynamic Sampling Strategy (DSS) to efficiently select optimal negative samples, while the Rubik’s Cube Attention (RCA) module, combined with the Context-Aware Channel Integration (CACI) module, enhances feature representation and discrimination by exploiting interdimensional interactions, inspired by the rotational mechanics of a Rubik’s Cube. Extensive experimental validate the effectiveness of the proposed method, demonstrating notable improvements in feature representation and UAV self-positioning accuracy within complex urban environments. Our approach achieves state-of-the-art performance on the DenseUAV dataset, which is specifically designed for dense urban contexts, and also delivers competitive results on the widely recognized University-1652 benchmark. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Maximum power point tracking of photovoltaic array based on improved Particle Swarm Optimization Algorithm 4

Maximum power point tracking of photovoltaic array based on ...

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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Lai, GuangZhi Li, HaoRan Zhang, ChangZhi Zhang, YiHao Wang, ZhiJin State Grid e-commerce Co. Ltd Beijing10053 China State Grid Tianjin Electric Power Company China School of Automation Southeast University Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason affecting the efficiency. The efficiency of photovoltaic power generation can be effectively improved by adopting the maximum power point tracking method (MPPT), but the traditional MPPT method is not ideal in the partial shadow occlusion of the photovoltaic array. To solve this problem, this paper proposes an improved particle swarm optimization method to effectively improve the tracking efficiency of MPPT when multiple peaks appear in the photovoltaic arrays power curve (P-V) under the partial shadow. The proposed method improves the learning factor of the traditional particle swarm optimization algorithm and designs the initial position of the particles according to the characteristics of the photovoltaic array. By adding the particle elimination mechanism, the number of particles changes dynamically, and the tracking speed of the algorithm for the maximum power of the photovoltaic array is improved. Through the result of the simulation, it is not difficult to get the conclusion that the improved particle swarm optimization algorithm can effectively improve the performance of the photovoltaic system under partial shadows. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Efficiency

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Attentive online sequential extreme learning machine for nonlinear dynamic process monitoring

Attentive online sequential extreme learning machine for non...

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第34届中国过程控制会议

作者： Jingxin Zhang Yuying Cao Hai Zhou Department of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education Department of Automation Southeast University New Energy Research Center China Electric Power Research Institute

This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction of outputs and the update of parameters by sequential *** cope with nonlinearity,the attention mechanism is utilized to map data to a high-dimensional feature *** effect of the attention mechanism and the single hidden layer feedforward network trained with OS-ELM work collaboratively in monitoring the real-time process data to detect possible *** addition,fault detection rate and false alarm rate are adopted to evaluate the monitoring *** effectiveness of the proposed method is illustrated by a typically complicated industrial system.

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Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

arXiv

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arXiv 2023年

作者： Lu, Xin Chen, Shikun Cao, Yichao Zhou, Xin Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Nanjing Enbo Technology Co. Ltd. Nanjing China

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Hash functions

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Heterogeneous Multi-Robot Cooperative Welding Task Allocation Based on Task-Centered Optimization: An Industrial Case Study

SSRN

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SSRN 2023年

作者： Liu, Wenbo Kuang, Zhian Zhou, Bo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shanghai Friendess Electronic Technology Shanghai201100 China

In this article, the complex task assignment, scheduling and planning of complex heterogeneous multi-robot welding process are studied. For large workpiece welding tasks that involve complex heterogeneous multi-robot task allocation under different internal and external constraints, the traditional multi-robot task allocation (MRTA) framework is extended to a novel and unified heterogeneous multi-robot (H-MRTA) framework, and a hierarchical optimization algorithm is proposed. Firstly, an optimization model based on the H-MRTA framework is established. Taking into account constraints such as accessibility, collision and welding process, the optimization objectives are formulated by combining the total welding time and the task balance between robots. Then, to obtain the optimal solution, a stepwise optimization algorithm is devised to solve the MRTA problem from top to bottom in two layers: working area division problem and multi-robot path planning problem. For the working area division problem, a task-centered optimization model is proposed, which aims to balance the workload among robots, and is solved by branch-and-bound method. For the multi-robot path planning problem, multiple process constraints are considered and the total working time is minimized as the objective function. The effectiveness of the optimization algorithm is verified by a case study on the welding task of a large plate element in a ship hull. compared to two other state-of-the-art algorithms, the proposed optimization algorithm achieves a task allocation scheme with a shorter welding time, a shorter waiting time and a higher welding productivity, which demonstrates the effectiveness and feasibility of the stepwise optimization-based task allocation algorithm for complex heterogeneous multi-robot. © 2023, The Authors. All rights reserved.

关键词： Industrial robots

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Enabling few-shot learning with PID control: a layer adaptive optimizer 24

Enabling few-shot learning with PID control: a layer adaptiv...

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Proceedings of the 41st International Conference on Machine Learning

作者： Le Yu Xinde Li Pengfei Zhang Zhentong Zhang Fir Dunkin School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China and Southeast University Shenzhen Research Institute Shenzhen China School of Automation Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAMLbased approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID.

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Asymptotic Stabilization of Time-Varying Delay system with Actuator Saturation via Multi-dimensional Taylor Network

Asymptotic Stabilization of Time-Varying Delay System with A...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Guo-Biao Wang Hong-Sen Yan School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing Jiangsu China

This paper addresses the stabilization problem of time-varying delay systems subject to actuator saturation. A novel class of Lyapunov functional with a nonlinear metric is developed to guarantee that there exists a Lyapunov-Razumikhin function that strictly decreases for the controlled system. Such a nonlinear metric is parameterized by a multi-dimensional Taylor network with concise topology, which brings into the high-order dynamic information and guarantees real-time performance. A sum-of-squares programming approach based on the designed Lyapunov functional is formulated to deduce the stability criteria, which maximizes the estimate of the region of attraction and ensures the uniform asymptotic stability of the closed-loop system theoretically. A numerical example demonstrates the effectiveness of the proposed stabilization control scheme.

关键词： measurement Actuators Asymptotic stability Delay systems Stability criteria Programming Real-time systems

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Magnetically Driven Soft Continuum Microrobot for Intravascular Operations in Microscale

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Cyborg and Bionic systems 2022年第1期2022卷 265-272页

作者： Dan Liu Xiaoming Liu Zhuo Chen Zhaofeng Zuo Xiaoqing Tang Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Sytems Ministry of EducationState Key Laboratory of Ielligent Control and Decision.of Complex SystemBeijing Advanced Innovation Center for Itelligent Robots and Systemsand School of Mechatronical EngineeringBeijing Institute of TechnologyBejing 100081China Center for Neuroscience and Biomedical Engineering The University of Electro-CommunicationsTokyo 1828585Japan

Remotely controlled soft continuum robots with active steering capability have broad prospects in medical ***,conventional continuum robots have the miniaturization *** paper presents a microscale soft continuum microrobot with steering and locomotion capabilties based on magnetic field *** magnetically driven soft continuum microrobot is made of NdFeB particles and polyd imethylsiloxane(PDMS),and it can be as small as 200 pum in ***,a hydrogel layer is covered on the surface of the microrobot,which not only overcomes the adhesion force between the microobjects and the soft tip but also reduces the friction between the microrobot and *** performance test indicates the soft continuum microrobot featured excellent control and steering *** experimental results demonstrate that the soft continuum microrobot can travel through the microfluidic channel by its own vibration and flexibly steer in a bifurcation ***,the micromanipulation of microbeads in the microfluidic channels proves that the proposed microscale soft continuum microrobot has a great potential for intravascu lar manipu lation.

关键词： friction robot overcome

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PCDH-based nonlinear H∞control of VSC-HVDC with wind power integrated

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Journal of control and Decision 2021年第1期8卷 41-49页

作者： Xingyu Lv Bangjun Lei Shumin Fei Foreign language Teaching Center Guizhou Institute of TechnologyGuiyangPeople’s Republic of China Key Laboratory of Power Dig Data of Guizhou Province School of Electrical and Information EngineeringGuizhou Institute of TechnologyGuiyang 550003People’s Republic of China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast UniversityNanjingPeople’s Republic of China

Based on the port-controlled dissipative Hamiltonian(PCDH)system,a novel nonlinear(NN)H∞control is designed for the wind farm side(WFS)voltage source converter(VSC)of VSC-based high-voltage direct current(VSC-HVDC)transmission system connected large wind ***,the uncertain nonlinear dynamical model of WFS VSC in the VSC-HVDC is established by taking external disturbances and parameter variations into ***,the dissipative Hamiltonian structure of the WFS VSC system is constructed by means of variable ***,on the basis of the obtained dissipative Hamiltonian structure,a NN H∞control is put ***,the simulation results show that the proposed NN H∞control gains the advantage over the interconnection and damping assignment passivity-based(IDA-PB)control.

关键词： VSC-HVDC large wind farm Hamiltonian function method nonlinear H∞control

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Probabilistic Decomposition Transformer for Time Series Forecasting

SSRN

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SSRN 2022年

作者： Tong, Junlong Xie, Liping Yang, Wankou Zhang, Kanjian School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China

Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeling. However, the complex temporal patterns of the time series hinder the model from mining reliable temporal dependencies. Furthermore, the autoregressive form of the Transformer introduces cumulative errors in the inference step. In this paper, we propose the probabilistic decomposition Transformer model that combines the Transformer with a conditional generative model, which provides hierarchical and interpretable probabilistic forecasts for intricate time series. The Transformer is employed to learn temporal patterns and implement primary probabilistic forecasts, while the conditional generative model is used to achieve non-autoregressive hierarchical probabilistic forecasts by introducing latent space feature representations. In addition, the conditional generative model reconstructs typical features of the series, such as seasonality and trend terms, from probability distributions in the latent space to enable complex pattern separation and provide interpretable forecasts. Extensive experiments on several datasets demonstrate the effectiveness and robustness of the proposed model, indicating that it compares favorably with the state of the art. © 2022, The Authors. All rights reserved.

关键词： Time series

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