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41st International Conference on Machine Learning, ICML 2024

作者： Guo, Yongxin Tang, Xiaoying Lin, Tao School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Research Center for Industries of the Future Westlake University China School of Engineering Westlake University China

Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning system. Though recent research has focused on improving the optimization of FL when distribution shifts occur among clients, ensuring global performance when multiple types of distribution shifts occur simultaneously among clients-such as feature distribution shift, label distribution shift, and concept shift-remain under-explored. In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges. Through our research, we find that existing methods fail to address the clustering principle. Therefore, we propose a novel clustering algorithm framework, dubbed as FedRC, which adheres to our proposed clustering principle by incorporating a bi-level optimization problem and a novel objective function. Extensive experiments demonstrate that FedRC significantly outperforms other SOTA cluster-based FL methods. Our code is available at https://***/LINs-lab/FedRC. Copyright 2024 by the author(s)

关键词： Federated learning

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Design and Preliminary Evaluation of a Lightweight, Cable-Driven Hip Exoskeleton for Walking Assistance

Design and Preliminary Evaluation of a Lightweight, Cable-Dr...

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IEEE International Conference on robotics and Biomimetics

作者： Wei Jin Hongbin Fang Institute of AI and Robotics Fudan University Shanghai China MOE Engineering Research Center of AI & Robotics and Shanghai Engineering Research Center of AI & Robotics Fudan University Shanghai China

ISBN: (纸本)9781665481106

The hip exoskeleton has demonstrated the capability of enhancing human locomotion performance during walking. However, few of them reached the ideal effect and went out of the lab due to excessive weight, lack of degree of freedom and user discomfort. In this paper, we present a cable-driven hip exoskeleton that concentrates on lightweight, comfortability and human-machine interaction. The total weight of the device is 3.15 kg excluding the real-time controller. Active cable-driven actuators and passive actuators based on torsional springs are developed to provide assistive force in the sagittal plane, and allow the hip to move freely in the coronal plane. We proposed a hierarchical controller that calculates the motor current command based on sensory information and user instruction, and validated the usability and effectiveness of the exoskeleton through a series of evaluation experiments. The results showed the exoskeleton can provide 14 % of energy supply per step on average during walking. Our work may provide a possible solution for the future lightweight, high-performance, and humanized hip exoskeleton.

关键词： Legged locomotion Human computer interaction Actuators Exoskeletons Force Robot sensing systems Real-time systems

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Parameterization of Circuit Breakers for HVDC Transmission Line Between Egypt and the Kingdom of Saudi Arabia Toward Restriking Voltage

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IEEE Access 2024年 12卷 18259-18269页

作者： Eliyan, Tamer Elsisi, Mahmoud Alghaythi, Mamdouh L. Alshammari, Meshari S. Wadie, Fady Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Cairo 11629 Egypt Higher Institute of Engineering at El-Shorouk City Alshorouk Academy Department of Electrical Power and Machines Engineering Cairo 11837 Egypt National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung 807618 Taiwan Jouf University College of Engineering Department of Electrical Engineering Sakaka 72388 Saudi Arabia Egyptian Russian University Faculty of Engineering Mechatronics and Robotics Engineering Department Badr City 11829 Egypt

The high voltage direct current (HVDC) transmission lines represent the prospective way for long-distance transmission between countries, remote areas, and offshore wind farms to decrease power loss. However, the HVDC protection systems have many challenges against any system issue, such as maintenance and short circuits. Thus, the vital role played by high voltage direct current circuit breaker CB has made it the center of attention in HVDC protection systems. The main challenge in HVDC CB is the lack of naturally exiting current zero that allows the breaker to extinguish the arc during the opening process. Thus, a commutation L-C circuit is required to inject an oscillating current and enforce a zero-crossing. Nevertheless, the L-C branch is affected directly by the arcing time, the transient recovery voltage (TRV), and the rate of rise of recovery voltage (RRRV). This paper investigates the parametric uncertainties of L-C and SF6 mechanical interrupters, including cooling power and arc time constant upon TRV and RRRV, based on Mayr's black-box model. Furthermore, a part of 3000 MVA, 500 kV HVDC transmission line between Egypt and the Kingdom of Saudi Arabia is used as a testing system employing ATP/EMTP software package to demonstrate the effect of CB's parameters variations. The results indicate that the capacitance represents the major parameter having a direct impact on TRV and RRRV. In essence, the proper parameterization of the CB is highly required in the design process of HVDC CB to enhance the decision-making and ensure that the L-C is capable of reducing arcing time and TRV simultaneously. © 2013 IEEE.

关键词： arcing time black-box arc model circuit breakers HVDC protection systems RRRV Transmission lines TRV

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Dynamic Heart Simulator for Ultrasound-Guided Pericardiocentesis

Dynamic Heart Simulator for Ultrasound-Guided Pericardiocent...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kim Yan Wanquan Yan Shing Shin Cheng Department of Mechanical and Automation Engineering CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering CUHK T Stone Robotics Institute Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Multi-Scale Medical Robotics Center Hong Kong

Pericardiocentesis is an important surgical intervention to treat a medical condition called pericardial effusion, during which excessive fluid accumulates around the heart, potentially leading to life-threatening situation. It involves the insertion of a needle and catheter towards the heart into the pericardial space to drain the excessive fluid under ultrasound (US) guidance. The risky procedure requires surgeons to acquire sufficient training to ensure safe execution of the procedure. However, existing heart simulators lack dynamic features, do not offer realistic images under US imaging, and are not reusable. This work presents a dynamic heart simulator (DHS) with pericardial effusion to mimic the beating motion of the human heart and the realistic US imaging results. The beating heart motion is realized using a hydraulic actuation system connected to a double-layer balloon set. The clear and realistic US imaging results are obtained through a unique formula proposed for the chest tissue and the cardiac muscle. A characterization method was also developed to allow customization of important anatomical parameters in the DHS. The experimental results show that the DHS allowed highly realistic simulation of the beating heart, cardiac muscle, and pericardium under US imaging and has been demonstrated to enable successful US-guided pericardiocentesis.

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EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving

arXiv

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arXiv 2024年

作者： Lin, Jiacheng Chen, Jiajun Peng, Kunyu He, Xuan Li, Zhiyong Stiefelhagen, Rainer Yang, Kailun The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Robotics and An-Thropomatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving. Due to the lack of semantic modeling capacity in audio and video, existing works have mainly focused on text-based multiobject tracking, which often comes at the cost of tracking quality, interaction efficiency, and even the safety of assistance systems, limiting the application of such methods in autonomous driving. In this paper, we delve into the problem of AR-MOT from the perspective of audio-video fusion and audio-video tracking. We put forward EchoTrack, an end-to-end AR-MOT framework with dual-stream vision transformers. The dual streams are intertwined with our Bidirectional Frequency-domain Cross-attention Fusion Module (Bi-FCFM), which bidirectionally fuses audio and video features from both frequency- and spatiotemporal domains. Moreover, we propose the Audio-visual Contrastive Tracking Learning (ACTL) regime to extract homogeneous semantic features between expressions and visual objects by learning homogeneous features between different audio and video objects effectively. Aside from the architectural design, we establish the first set of large-scale AR-MOT benchmarks, including Echo-KITTI, Echo-KITTI+, and Echo-BDD. Extensive experiments on the established benchmarks demonstrate the effectiveness of the proposed EchoTrack and its components. The source code and datasets are available at https://***/lab206/EchoTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection

arXiv

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arXiv 2024年

作者： Tang, Canhui Zhou, Sanping Li, Yizhe Dong, Yonghao Wang, Le The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi 710049 China

With the wide application of knowledge distillation between an ImageNet pre-trained teacher model and a learnable student model, industrial anomaly detection has witnessed a significant achievement in the past few years. The success of knowledge distillation mainly relies on how to keep the feature discrepancy between the teacher and student model, in which it assumes that: (1) the teacher model can jointly represent two different distributions for the normal and abnormal patterns, while (2) the student model can only reconstruct the normal distribution. However, it still remains a challenging issue to maintain these ideal assumptions in practice. In this paper, we propose a simple yet effective two-stage industrial anomaly detection framework, termed as AAND, which sequentially performs Anomaly Amplification and Normality Distillation to obtain robust feature discrepancy. In the first anomaly amplification stage, we propose a novel Residual Anomaly Amplification (RAA) module to advance the pre-trained teacher encoder. With the exposure of synthetic anomalies, it amplifies anomalies via residual generation while maintaining the integrity of pre-trained model. It mainly comprises a Matching-guided Residual Gate and an Attribute-scaling Residual Generator, which can determine the residuals’ proportion and characteristic, respectively. In the second normality distillation stage, we further employ a reverse distillation paradigm to train a student decoder, in which a novel Hard Knowledge Distillation (HKD) loss is built to better facilitate the reconstruction of normal patterns. Comprehensive experiments on the MvTecAD, VisA, and MvTec3D-RGB datasets show that our method achieves state-of-the-art performance. Our code will be available at https://***/Hui-design/AAND. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

arXiv

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arXiv 2024年

作者： Chen, Shitao Zhang, Haolin Zheng, Nanning The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi’an China

3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding. Nevertheless, an overlooked practical issue in existing methods is the ambiguity in training sample allocation based on box Intersection over Union (IoUbox). This problem impedes further enhancements in the performance of anchor-based LiDAR 3D object detectors. To tackle this challenge, this paper introduces a new training sample selection method that utilizes point cloud distribution for anchor sample quality measurement, named Point Assisted Sample Selection (PASS). This method has undergone rigorous evaluation on two widely utilized datasets. Experimental results demonstrate that the application of PASS elevates the average precision of anchor-based LiDAR 3D object detectors to a novel state-of-the-art, thereby proving the effectiveness of the proposed approach. The codes will be made available at https://***/XJTU-Haolin/Point_Assisted_Sample_Selection. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Perception Robustness Testing at Different Levels of Generality

Field Robotics

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Field robotics 2025年 1卷 253-286页

作者： Zachary Pezzementi Trenton Tabor Jonathan K. Chang Carter Tiernan Bill Drozd Eric Sample Chris Hazard Michael Wagner Philip Koopman National Robotics Engineering Center (NREC) Carnegie Mellon University Pittsburgh Pennsylvania USA Edge Case Research LLC Pittsburgh Pennsylvania USA Electrical and Computer Engineering Department Carnegie Mellon University Pittsburgh Pennsylvania USA

Ensuring the success and safety of deployed robotic systems often requires predicting their behavior in a wide range of conditions, precluding practical testing of the physical system itself. We introduce an approach to predict the robustness of a perception system in a broad range of conditions through simulation of challenging real-world conditions achieved by modifying real images according to physical phenomena, producing physically-realistic, mutated images. Typical perception-system performance metrics are difficult to relate to system-level application- or safety-requirements. We propose performance-evaluating metrics that are more closely tied to these targets, a relationship dependent on the amount of application-specific context available and assumptions imposed. We also introduce a novel robustness metric, Robustness ROC, that can be applied to any of these performance metrics. We demonstrate this method on NREC-AgPD, a large dataset focused on person detection for off-road driving in orchard environments, assessing several recent person detectors, and showing how considerations of robustness can often change the best-choice algorithm.

关键词： Robustness Robots Measurement Training Market research Three-dimensional printing Snow Object detection MCDM Detectors

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Coupled Transformed Induced Tensor Nuclear Norm for Robust Tensor Completion

Coupled Transformed Induced Tensor Nuclear Norm for Robust T...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mengjie Qin Zheyuan Lin Minhong Wan Chunlong Zhang Jason Gu Te Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax

This study focuses on exploring the problem of low-rank tensor completion (LRTC), where the main challenge lies in accurately representing the inherent structures of the underlying data. Typical approaches exhibit either suboptimal performance due to the lack of suitable priors or combine many constraints, resulting in increased model complexity. In this paper, we propose a robust transformed t-SVD by utilizing the discrete cosine transform in conjunction with a unitary transformation matrix. Nonetheless, the t-SVD-based framework exhibits a deficiency in its capacity to flexibly explore diverse correlations across tensor modes. To address this issue, we employ the representation of the tensor as a series of multi-dimensional unfolding tensors, which fully capture the embedded structure in the original data. Moreover, on the basis of this tensor representation, we construct a transformed t-SVD method within the LRTC model and solve it by the alternating-direction method of multiplier (ADMM) approach. Extensive experiments demonstrate that the proposed model outperforms existing state-of-the-art methods.

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Text Grouping Adapter: Adapting Pre-Trained Text Detector for Layout Analysis

Text Grouping Adapter: Adapting Pre-Trained Text Detector fo...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Tianci Bi Xiaoyi Zhang Zhizheng Zhang Wenxuan Xie Cuiling Lan Yan Lu Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Microsoft Research Asia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Significant progress has been made in scene text detection models since the rise of deep learning, but scene text layout analysis, which aims to group detected text instances as paragraphs, has not kept pace. Previous works either treated text detection and grouping using separate models, or train a model from scratch while using a unified one. All of them have not yet made full use of the already well-trained text detectors and easily obtainable detection datasets. In this paper, we present Text Grouping Adapter (TGA), a module that can enable the utilization of various pretrained text detectors to learn layout analysis, allowing us to adopt a well-trained text detector right off the shelf or just fine-tune it efficiently. Designed to be compatible with various text detector architectures, TGA takes detected text regions and image features as universal inputs to as-semble text instance features. To capture broader contextual information for layout analysis, we propose to predict text group masks from text instance features by one-to-many assignment. Our comprehensive experiments demonstrate that, even with frozen pretrained models, incorporating our TGA into various pretrained text detectors and text spotters can achieve superior layout analysis performance, simultaneously inheriting generalized text detection ability from pretraining. In the case of full parameter fine-tuning, we can further improve layout analysis performance.

关键词： Deep learning Analytical models Adaptation models Computer vision Layout Text detection Detectors

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