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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Research on CT Image Grading of Superior Mesenteric Artery Based on AA Res-Unet 7th

Research on CT Image Grading of Superior Mesenteric Artery B...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Han, Yu Chen, Jinghao Xu, Peixia Wang, Meirong Zhu, YuanFan Zhang, Kun He, Bosheng School of Electrical Engineering Nantong University Nantong China Department of Radiology The Second Affiliated Hospital of Nantong University Nantong China Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong China School of Management Science and Engineering ChongqingTechnology and Business University Chongqing China

ISBN: (纸本)9789811672064

In recent years, the incidence of Crohn’s disease (CD) is on the rise, which calls for more accurate and less invasive diagnostic tools. Crohn’s disease usually need to be confirmed by colonoscopy, and this paper puts forward a network model AA Res-Unet based on the fundamental Unet, can realize automatic of superior mesenteric artery CT image segmentation, and further to realize the automatic grading function, help doctors by observing the superior mesenteric artery features, early diagnosis of disease. Image segmentation achieved a per case precision of 98.76%, recall of 98.00%, and 98.34% of F1 score. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Proximal-like algorithms for equilibrium seeking in mixed-integer Nash equilibrium problems

Proximal-like algorithms for equilibrium seeking in mixed-in...

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IEEE Conference on Decision and control

作者： Filippo Fabiani Barbara Franci Simone Sagratella Martin Schmidt Mathias Staudigl Department of Engineering Science University of Oxford United Kingdom IMT School for Advanced Studies Lucca Lucca Italy Department of Advanced Computing Sciences Maastricht University Maastricht The Netherlands Department of Computer Control and Management Engineering Antonio Ruberti (DIAG) Sapienza University of Rome Rome Italy Department of Mathematics at Trier University Universitätsring 15 54296 Trier Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider potential games with mixed-integer variables, for which we propose two distributed, proximallike equilibrium seeking algorithms. Specifically, we focus on two scenarios: i) the underlying game is generalized ordinal and the agents update through iterations by choosing an exact optimal strategy; ii) the game admits an exact potential and the agents adopt approximated optimal responses. By exploiting the properties of integer-compatible regularization functions used as penalty terms, we show that both algorithms converge to either an exact or an ϵ-approximate equilibrium. We corroborate our findings on a numerical instance of a Cournot oligopoly model.

关键词： Computational modeling Measurement uncertainty Stochastic processes Games Approximation algorithms Nash equilibrium Cost function

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High-Gain Like Observer Design for Nonlinear Systems Applied to Vehicle Motion Estimation

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IFAC-PapersOnLine 2023年第2期56卷 5673-5678页

作者： H. Bessafa C. Delattre Z. Belkhatir A. Zemouche R. Rajamani Université de Lorraine CRAN CNRS UMR 7039 54400 Cosnes et Romain France School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Laboratory for Innovations in Sensing Estimation and Control Department of Mechanical Engineering University of Minnesota Minneapolis USA

This work investigates the problem of state estimation for nonlinear triangular systems having additional output measurements. The main objective of this paper is to propose observer design strategies that account for the available additional information, which is difficult to consider using the standard high-gain observer methodology. To deal with this challenge, we propose a novel observer design method to handle the additional output measurements. This method can be thought of as an extension of the standard high-gain observer by introducing a weighting matrix as a tuning parameter, which uses both the high-gain methodology and the LPV/LMI technique. The proposed approach was applied to a nonlinear longitudinal dynamic model that uses extra measurement information, in addition to the measured longitudinal position of the vehicle.

关键词： Observer design High-gain observer LPV/LMI observer Autonomous Vehicles Longitudinal dynamic model

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Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements

arXiv

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arXiv 2024年

作者： Tucker, Kris Rege, Amit Kiran Smith, Conor Monteleoni, Claire Albash, Tameem Department of Applied Math University of Colorado Boulder United States Department of Computer Science University of Colorado Boulder United States Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States INRIA Centre de Recherche de Paris France Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised and unsupervised contexts. We demonstrate that the model benefits from larger training sets establishing that it is in fact "learning," and we show robustness to errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single particle relaxation. © 2024, CC BY.

关键词： Qubits

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

arXiv

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arXiv 2023年

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong The School of Computer Science and Technology Tongji University Shanghai China Ant Group Shanghai China The School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The School of Computer Science Fudan University Shanghai China The Department of Control Science and Engineering Tongji University Shanghai China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named Learning from Heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2023, CC BY.

关键词： Contrastive Learning

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Foreign Material Detection and Localization in Stored Grain with Passive RFID Tag Arrays

Foreign Material Detection and Localization in Stored Grain ...

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IEEE International Conference on RFID-Technology and Applications (RFID-TA)

作者： Erbo Shen Weidong Yang Xuyu Wang Shiwen Mao Henan Key Laboratory of Grain Photoelectric Detection & Control Henan University of Technology Zhengzhou China Kaifeng University Kaifeng China College of Information Science & Engineering Henan University of Technology Zhengzhou China Knight Foundation School of Computing & Information Sciences Florida International University Miami FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In the early stage of harvesting, transportation, and storage, the grain could be mingled with clods from the field, metal pieces from aging machines, and other objects of foreign material, which will greatly influence the grain quality and food security. In this paper, we propose a novel radio frequency (RF) sensing system termed TagSee, which leverages passive RFID tag arrays. The goal is to simultaneously sense the presence of foreign materials and locate their locations in the 3D space, to automatically monitor the stored grain. Specifically, we use RFID received signal strength (RSS) and phase as features for foreign material detection. To design the TagSee system, we first introduce a sensing space division method. Then, an Euclidean Distance Ratio (EDR) algorithm and a heuristic method are proposed to achieve high localization accuracy. Experimental results show that TagSee can effectively detect foreign materials in stored grain and achieve a centimeter-level localization accuracy.

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Federated Reinforcement Learning for Electric Vehicles Charging control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Frustration Elimination and Excited State Search in Coherent Ising Machines

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Physical Review Letters 2025年第9期134卷 090401-090401页

作者： Zheng-Yang Zhou Clemens Gneiting J. Q. You Franco Nori Zhejiang Key Laboratory of Quantum State Control and Optical Field Manipulation Department of Physics Zhejiang Sci-Tech University 310018 Hangzhou China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama 351-0198 Japan Center for Quantum Computing RIKEN Wakoshi Saitama 351-0198 Japan Zhejiang Key Laboratory of Micro-Nano Quantum Chips and Quantum Control School of Physics and State Key Laboratory for Extreme Photonics and Instrumentation Zhejiang University Hangzhou 310027 China College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA

Frustration, that is, the impossibility of satisfying the energetic preferences between all spin pairs simultaneously, underlies the complexity of many fundamental properties in spin systems, including the computational difficulty in determining their ground states. Coherent Ising machines (CIMs) have been proposed as a promising analog computational approach to efficiently find different degenerate ground states of large and complex Ising models. However, CIMs also face challenges in solving frustrated Ising models: frustration not only reduces the probability of finding good solutions, but it also prohibits the leveraging of quantum effects in doing so. To circumvent these detrimental effects of frustration, we show how frustrated Ising models can be mapped to frustration-free CIM configurations by including ancillary modes and modifying the coupling protocol used in current CIM designs. Such frustration elimination may empower current CIMs to benefit from quantum effects in dealing with frustrated Ising models. In addition, these ancillary modes can also enable error detection and searching for excited states.

关键词： Ising model

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