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An Aerial Image Stitching Algorithm Based on Long-distance Features 11

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An Aerial Image Stitching Algorithm Based on Long-distance F...

11th International Conference on Information Science and Technology, ICIST 2021

作者： Liu, Qiang Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Aerial image stitching is very important in obtaining UAV information. The Speeded Up Robust Features Algorithm (SURF) is an image matching method with high robustness. However, the SURF algorithm subjects to the problems of inexact edge positioning and low matching accuracy. In order to obtain high-precision aerial stitching images with high efficiency, an aerial image stitching algorithm based on long-distance features is proposed in this paper. First, the Canny-SURF algorithm is used for feature detection. After that, the Long-Fast Retina keypoint (L-FREAK) binary symbol is used to describe and match the feature points. Finally, the Random Sample Consensus Algorithm (RANSAC) is used to compute the projection transformation model, and the weighted average fusion algorithm is used to fuse the pixels. Experimental results show that the proposed algorithm is outperform the SIFT, SURF, ORB, and BRISK algorithms. The proposed algorithm has good stitching accuracy, and can stitch aerial images with wide rotation characteristics. © 2021 IEEE.

关键词： Antennas

Actuator Fault Estimation Based On Adaptive Iterative Learning Algorithm For Nonlinear Periodic Systems

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Actuator Fault Estimation Based On Adaptive Iterative Learni...

第40届中国控制会议

作者： Ya-qi Liu Yi-ming Fang Le Liu Xing-da Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University

A actuator fault estimation algorithm based on adaptive iterative learning is proposed for a class of nonlinear periodic systems with actuator fault and ***,the proposed method designs a state observer and a fault estimator based on adaptive iterative *** Lyapunov stability theory and the optimization theory are employed to prove the convergence of the *** the LMI toolbox is utilized to obtain the results of the observer gain matrix and fault estimator parameter *** method combines the iterative learning strategy with the adaptive algorithm,and utilizes not only the state error signals of the last iteration learning,but also the output error signals of this *** makes the estimated fault approximate to the real fault gradually and realizes the accurate estimation of the time-varying fault and the intermittent ***,the validity and accuracy of the proposed method are verified by the simulation of a single-link robotic arm mold.

关键词： Iterative learning scheme Adaptive algorithm Fault estimation Actuator fault Linear matrix inequality

Challenges for Monocular 6D Object Pose Estimation in Robotics

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arXiv 2023年

作者： Thalhammer, Stefan Bauer, Dominik Hönig, Peter Weibel, Jean-Baptiste García-Rodríguez, José Vincze, Markus The Industrial Engineering Departent UAS Technikum Vienna Austria The Automation and Control Institute TU Wien Austria The Columbia Artificial Intelligence and Robotics Lab Columbia University United States The Department of Computer Technology University of Alicante Spain

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well suited for robotics applications. We observe that previous surveys on establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved. © 2023, CC BY.

关键词： Robots

controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification

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arXiv 2023年

作者： Xing, Dong Gu, Pengjie Zheng, Qian Wang, Xinrun Liu, Shanqi Zheng, Longtao An, Bo Pan, Gang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore College of Control Science and Engineering Zhejiang University Hangzhou China

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation of types and then pre-train the best response for each type. However, most of them do not consider the distribution of teammate instances within a type. This could expose the agent to the hidden risk of type confounding. In the worst case, the best response for an abstract teammate type could be the worst response for all specific instances of that type. This work addresses the issue from the lens of causal inference. We first theoretically demonstrate that this phenomenon is due to the spurious correlation brought by uncontrolled teammate distribution. Then, we propose our solution, CTCAT, which disentangles such correlation through an instance-wise teammate feedback rectification. This operation reweights the interaction of teammate instances within a shared type to reduce the influence of type confounding. The effect of CTCAT is evaluated in multiple domains, including classic ad hoc teamwork tasks and real-world scenarios. Results show that CTCAT is robust to the influence of type confounding, a practical issue that directly hazards the robustness of our trained agents but was unnoticed in previous works. Copyright © 2023, The Authors. All rights reserved.

关键词： Feedback

UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

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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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Self-Enhanced Oxidation of Fluoroquinolone Antibiotics by Cuii/Peroxymonosulfate: Copper Coordination Mechanism with Antibiotics

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SSRN

SSRN 2024年

作者： Zhao, Qing Chen, Long Wang, Zhiqiang Zhou, Lei Gong, Xueqing Xiu, Guangli Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Centre for Computational Chemistry Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai200237 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Trace copper ions are commonly found in different water bodies. They can effectively promote the degradation of various pollutants by peroxymonosulfate (PMS). However, the underlying mechanism of copper's action has not yet been clarified. Fluoroquinolone antibiotics (FQs), which are commonly used in humans and animals, often coexisting with copper metal in environmental waters, especially in aquaculture and medical wastewater. The presence of CuII under neutral conditions is found to significantly enhance the degradation efficiency of FQs by PMS. The coordination of CuII with FQs plays a crucial role in this process. Taking pefloxacin (PE), one typical FQs as an example, the structure of the copper complexes was determined using UV-visible spectroscopy, fluorescence spectroscopy, and DFT calculations as [Cu (PE) (H2O)4]2+. Comprehensive quenching experiments and electron paramagnetic resonance (EPR) test indicated that high-valence copper was responsible for the accelerated degradation. The combined results, including PMS decay and DFT calculations, indicated that CuII is initially reduced to CuI at a relatively slow rate and then rapidly oxidized to CuIII in the presence of PMS. The results highlight the valence transition of CuII/CuI/CuIII experienced during PMS activation, and the crucial role of contaminant-metal coordination in the valence transformation of copper. © 2024, The Authors. All rights reserved.

关键词： Copper compounds

Self-Enhanced Oxidation of Fluoroquinolone Antibiotics by Cuii/Peroxymonosulfate: Copper Coordination Mechanism with Antibiotics

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SSRN

SSRN 2024年

作者： Zhao, Qing Chen, Long Wang, Zhiqiang Gong, Xueqing Zhou, Lei Xiu, Guangli Shanghai Environmental Protection Key Laboratory for environmental standard and risk management of chemical pollutants School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Centre for Computational Chemistry Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai200237 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

关键词： Copper compounds

Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence