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Cloud-Edge Test-Time Adaptation for Cross-Domain Online Machinery Fault Diagnosis via Customized Contrastive Learning

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Zhu, Mengliang Liu, Jie Hu, Zhongxu Liu, Jiawei Jiang, Xingxing Shi, Tielin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Image Informat Proc & Intelligent Con Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Beijing Aerosp Measurement & Control Technol Co L Beijing 100144 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China

Nowadays offline transfer learning (TL) is the mainstream research for cross -domain machinery fault diagnosis (MFD). However, the target data is usually collected online by edge devices in real -world applications, causing practicality issue of offline TL. To address this issue, a new and practical online TL scenario test -time adaptation (TTA) is considered in this paper. In TTA, the labeled source data is inaccessible for privacy, and only a pretrained source model is provided for online model adaptation with the online unlabeled target data. To enable TTA for MFD, a dynamic data division strategy is proposed to divide each mini -batch of the online target data into the certain -aware and uncertain -aware sets based on the spectral entropy and prediction confidence for the subsequent fine-grained online model adaptation. Delving into underlying data properties, a customized contrastive learning (CL) framework is proposed, consisting of specifically designed CL strategies for the different divided sets, respectively. Specifically, prototypical CL based on the representative class -wise prototypes is proposed to boost the feature discriminability of certain -aware set, while neighborhood CL based on the local neighborhood structure is proposed to refine the numerous noise of uncertain -aware set. Meanwhile, smooth entropy minimization is devised for reliable model uncertainty reduction. The cloud -edge TTA implementation framework is further considered in the scenario of cloud manufacturing and edge computing for practical realworld applications. Specifically, the pre -trained source model is directly deployed from cloud to edge for local TTA. A simple source model pruning strategy is proposed to obtain the lightweight pre -trained source model for efficient deployment on resource -limited edge devices. Practical experiments on two edge devices Raspberry Pi and NVIDIA Jetson Nano verify the effectiveness and efficiency.

关键词： Customized contrastive learning Cloud -edge test -time adaptation Dynamic data division Machinery fault diagnosis Online transfer learning

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells 43

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

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43rd Chinese control Conference (CCC)

作者： Lei, Jingzhi Sheng, Chuang Wang, Jingjing Yu, Liansong Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China Wuhan Nanrui Ltd Liabil Co State Grid Elect Power Res Inst Wuhan 430073 Hubei Peoples R China

ISBN: (纸本)9798350366907;9789887581581

With the increasing demand for efficient and reliable renewable energy sources, Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as a promising technology. Recent experiments demonstrate that Electrochemical Impedance Spectroscopy (EIS) effectively differentiates various states of PEMFCs. Employing broadband signals as testing excitations significantly reduces EIS measurement time, facilitating real-time monitoring of PEMFCs' health under dynamic conditions. Broadband signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational disturbances. This paper introduces an EIS simulation environment, leveraging PEMFCs' impedance and electrochemical noise models. It focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary sequences. The results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB image

arXiv

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arXiv 2023年

作者： Jiang, Changlong Xiao, Yang Wu, Cunlin Zhang, Mingyang Zheng, Jinghong Cao, Zhiguo Zhou, Joey Tianyi Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Alibaba Group China Singapore Singapore

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands’ local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer’s non-local encoding-decoding framework to build A2J-Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints’ articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://***/ChanglongJiangGit/A2J-Transformer. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Semi-Supervised Class-Agnostic Motion Prediction with Pseudo label Regeneration and BEVMix

arXiv

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arXiv 2023年

作者： Wang, Kewei Wu, Yizheng Pan, Zhiyu Li, Xingyi Xian, Ke Wang, Zhe Cao, Zhiguo Lin, Guosheng S-Lab Nanyang Technological University Singapore Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China SenseTime Research Hong Kong

Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this challenge, our study explores the potential of semi-supervised learning (SSL) for class-agnostic motion prediction. Our SSL framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data by generating pseudo labels through test-time inference. To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module. This module effectively selects reliable pseudo labels and regenerates unreliable ones. Furthermore, we propose two data augmentation strategies: temporal sampling and BEVMix. These strategies facilitate consistency regularization in SSL. Experiments conducted on nuScenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. These results highlight the ability of our method to strike a favorable balance between annotation costs and performance. Code will be available at https://***/kwwcv/SSMP. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Impact of Rail Transit Systems on Transport Accessibility under Road Congestion

Impact of Rail Transit Systems on Transport Accessibility un...

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2022世界交通运输大会(WTC2022)

作者： Bo Zhan Hui Zhang Min Ouyang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Lab.for Image Processing and Intelligent Control Huazhong University of Science and TechnologyMinistry of Education

Investment inrail transit systems is widely advocated in large,high-density cities on the basis of its purported role in reducing road congestion,but the impact of rail transit systems on congestion remains a *** paper provides a method for evaluating the impact of rail transit systems on road congestion in terms of transport accessibility,and applies the method to 43 Chinese cities that have opened rail transit *** show that the rail transit systems can reduce road congestion in most cities,but their performance largely depends on the congestion speed and the rail transit network ***,the structural properties analysis of rail transit networks in two typical cities(Dalian and Changchun)indicates that rail transit lines with longer length,smaller detour factor,and larger population coverage rate tend to perform better in congestion *** findings in this paper provide guidance for city authorities regarding current and future investment in rail transit systems.

关键词： Rail transit system Congestion reduction Accessibility Rail transit network size Congestion speed Structural properties

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SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis

arXiv

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arXiv 2022年

作者： Li, Xingyi Hong, Chaoyi Wang, Yiran Cao, Zhiguo Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China S-Lab Nanyang Technological University Singapore

We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is available at: https://***/xingyi-li/SymmNeRF. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects

arXiv

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arXiv 2022年

作者： Peng, Juewen Zhang, Jianming Luo, Xianrui Lu, Hao Xian, Ke Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China Adobe Research

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Feature Aggregation and Propagation Network for Camouflaged Object Detection

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IEEE TRANSACTIONS ON image processing 2022年 31卷 7036-7047页

作者： Zhou, Tao Zhou, Yi Gong, Chen Yang, Jian Zhang, Yu Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In PCA Lab Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Lehigh Univ Dept Bioengn Bethlehem 18015 PA USA

Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://***/taozh2017/FAPNet.

关键词： Camouflaged object detection boundary guidance module multi-scale feature aggregation cross-level fusion feature propagation

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Accurate Harmonic Calculation for Digital SPWM of VSI With Dead-Time Effect

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第7期36卷 7892-7902页

作者： Ye, Jie Huang, Songtao Liu, Linguo Li, Lanxin Xu, Jinbang Shen, Anwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article presents an accurate model of a digitally controlled voltage-source inverter with the dead-time effect. The impact of two nonlinear behaviors of digital sinusoidal pulsewidth modulation and dead time on the output waveform is analyzed in the time domain. The problematic waveform with sinusoidal pulsewidth modulation is displayed, which highlights the necessity of establishing a new model. In the frequency domain, considering the dead-time effect on the current double Fourier transform is used to give a harmonic expression without a current sign function. Furthermore, this avoids the error caused by the measurement of the current polarity, so the exact amplitude of each harmonic of the output voltage can be accurately calculated, which is important for eliminating specific harmonics and improving power quality. Moreover, the problem caused by the current zero crossing is solved. The result of the analysis is verified by simulation and experiment on DSP and SIC platform. And the result shows that the harmonics and their amplitudes are in good agreement with our formula.

关键词： Harmonic analysis Modulation Power harmonic filters Manganese Transistors Time-domain analysis Inverters Dead time digital sinusoidal pulsewidth modulation (DSPWM) double Fourier transform harmonics voltage-source inverter (VSI)

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