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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 247-257页

作者： Jiajun Wang Tufan Kumbasar the School of Automation Hangzhou Dianzi University IEEE the Control and Automation Engineering Department Faculty of Electrical and Electronics EngineeringIstanbul Technical University

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

关键词： Big bang-big crunch (BBBC) interval type-2 fuzzy neural networks (IT2FNNs) parameter optimization particle swarm optimization (PSO)

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Semidefinite programming for manipulating acoustic traps in real time (SMART)

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Scientific Reports 2025年第1期15卷 1-26页

作者： Zehnter, Sebastian Endres, Kevin Kronbichler, Martin Andrade, Marco A. B. Funke, Felix Ament, Christoph Chair of Control Engineering University of Augsburg Augsburg 86159 Germany Chair of High-Performance Scientific Computing University of Augsburg Augsburg 86159 Germany Faculty of Mathematics Ruhr University Bochum Bochum 44801 Germany Institute of Physics University of São Paulo São Paulo 05508-090 Brazil

Sound waves can be used for trapping and manipulating objects immersed in liquids or air. However, most acoustic levitation techniques are limited to particles with diameters much smaller than the acoustic wavelength or require time-consuming optimisation-based methods that hinder the dynamic manipulation of objects. Here, we present an approach based on semidefinite programming to manipulate levitated objects in real time. To demonstrate this technique, a phased array consisting of 256 ultrasonic transducers operating at 40 kHz is used for rotating a non-spherical Rayleigh object or to translate Mie spheres along various trajectories. In contrast to previous optimisation-based approaches, the proposed method can determine the emission phases of each transducer in real time, strongly facilitating the implementation of a model-based closed-loop control in future acoustic levitation systems. This is a fundamental step for manipulating levitated objects precisely and at high speeds. © The Author(s) 2025.

关键词： Acoustic levitation Acoustic traps Block-coordinate minimisation Burer–Monteiro method Contactless handling Phase recovery Phase retrieval problem Real time Semidefinite programming Sound pressure field

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization With Variance Reduction and Nonasymptotic Convergence Rates

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen Central South University School of Automation Hunan Changsha 410083 China City University of Hong Kong Department of Mechanical Engineering Hong Kong University of New South Wales School of Electrical Engineering and Telecommunications Sydney 2052 NSW Australia Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing 400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen 518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into Prox-DBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks. © 2013 IEEE.

关键词： Byzantine-resilient algorithms composite optimization decentralized stochastic optimization system security variance reduction

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FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors

arXiv

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arXiv 2025年

作者： Lin, Tao Wang, Qingwang Liang, Qiwei Tang, Minghua Sun, Yuxuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

Shadow removal is challenging due to the complex interaction of geometry, lighting, and environmental factors. Existing unsupervised methods often overlook shadow-specific priors, leading to incomplete shadow recovery. To address this issue, we propose a novel unsupervised Frequency Aware Shadow Removal Network (FASR-Net), which leverages the inherent frequency characteristics of shadow regions. Specifically, the proposed Wavelet Attention Downsampling Module (WADM) integrates wavelet-based image decomposition and deformable attention, effectively breaking down the image into frequency components to enhance shadow details within specific frequency bands. We also introduce several new loss functions for precise shadow-free image reproduction: a frequency loss to capture image component details, a brightness-chromaticity loss that references the chromaticity of shadow-free regions, and an alignment loss to ensure smooth transitions between shadowed and shadow-free regions. Experimental results on the AISTD and SRD datasets demonstrate that our method achieves superior shadow removal performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet decomposition

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Optimizing portable gas sensor system for hepatocellular carcinoma detection via volatile organic compounds analysis

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Viphavakit, Charusluk Sukaram, Thanikan Maung, Soe Thiha Rerknimitr, Rungsun Chaiteerakij, Roongruedee Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University Bangkok Thailand Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology Division of Gastroenterology Faculty of Medicine Chulalongkorn University Bangkok Thailand Ma Har Myaing Hospital Yangon Myanmar Integrated Innovation and Digital Technologies Affairs Faculty of Medicine Chulalongkorn University Bangkok Thailand

The analysis of volatile organic compounds (VOCs) in exhaled breath is a promising method for the early detection of hepatocellular carcinoma (HCC). However, the current technology suffers from a lack of portability, rendering it unsuitable for field-based screening applications. This study designed a compact sensor system operated in real-time, as detecting the 5 HCC biomarkers simultaneously was vital for accurate diagnosis due to cross-biomarkers for various diseases. The results showed that the optimized gas sensor system included injection volume of 3.0 mL, heating the Tedlar bag to 80 °C, and applying a 1-minute nitrogen purge, which achieved a rapid response time. The proposed gas sensor system has demonstrated excellent performance, with a concentration range of 10–1,000 ppb and strong linearity (R2 > 0.85). This portable analyzer is easy to use, delivers rapid results, and is suitable for point-of-care testing. Further studies are warranted to validate its performance with human breath samples before its implementation in clinical practice. © 2025

关键词： pH sensors

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Data imputation methods for intermittent renewable energy sources: Implications for energy system modeling

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Energy Conversion and Management 2025年 339卷

作者： Mantuano, Claudio Omoyele, Olalekan Hoffmann, Maximilian Weinand, Jann Michael Panella, Massimo Stolten, Detlef Forschungszentrum Jülich GmbH Institute of Climate and Energy Systems – Jülich Systems Analysis (ICE-2) Jülich 52425 Germany RWTH Aachen University Chair for Fuel Cells Faculty of Mechanical Engineering Aachen 52062 Germany Sapienza University of Rome Department of Computer Control and Management Engineering (DIAG) Rome 00185 Italy Sapienza University of Rome Department of Information Engineering Electronics and Telecommunications (DIET) Rome 00184 Italy

To incorporate a high share of intermittent renewable sources in energy systems, energy system optimization models rely on weather and climate time series data. However, data for renewable energy sources often contains missing values due to sensor or transmission faults. This study evaluates various data imputation methods for minutely-resolved global horizontal irradiance, direct normal irradiance, and wind speed time series, with missingness ranging from two to ninety percent. Alongside standard statistical tests, a novel validation criterion is introduced by directly evaluating the impact of imputation methods on energy system modeling. While certain imputation methods demonstrate strong point-wise statistical accuracy, they do not necessarily preserve the underlying data distribution. The performance of these methods is strongly influenced by the type of time series and the missingness mechanism, either continuous gaps or randomly missing data points. In energy system optimization, multiple imputation by chained equations, k-nearest neighbors, linear interpolation, and simple moving average yield the best results, outperforming more sophisticated deep learning-based methods. Overall, k-nearest neighbors consistently outperformed the other approaches across all validation criteria. By comprehensively evaluating the statistical performance of imputation methods and their impact on energy system modeling, this study offers valuable insights for researchers and practitioners addressing missing data in energy system applications. © 2025 The Authors

关键词： Data imputation Energy system optimization Energy time series Machine learning Renewable energy systems Self-sufficiency

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Robust adaptive feedforward output-feedback tracking control for microalgae cultures

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IFAC-PapersOnLine 2017年第1期50卷 12667-12672页

作者： Schaum, A. Weisbarth, H. Meurer, T. Chair of Automatic Control Faculty of Engineering Kiel University Kiel24143 Germany

A feedforward-feedback control strategy for trajectory tracking of microalgae cultures is developed and experimentally evaluated. On the basis of the Droop model an inversion-based feedforward control is determined, which steers the biomass from given initial to final values in a finite prescribed time. Feedback control is employed for disturbance resilience and improving the stability properties of the desired trajectory. The approach is tested using numerical simulations and experiments in a lab-scale photocatalytic reactor with Chlorella sorokiniana. © 2017

关键词： Feedforward control Algae Feedback control Microorganisms Navigation Robust control Stability Trajectories Chlorella sorokiniana Desired trajectories Feedforward feedback control Micro algae Photocatalytic reactors Practical stability Tracking controls Trajectory Planning

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