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

作者： Zike Cheng Hengwang Zhao Qiyuan Shen Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene diversity. Meanwhile, the prevalent methods are projection-based, struggling with the challenge of blurred boundaries. To tackle the dataset issue, we introduce a nuScenes-based MOS dataset, which provides richer scenes and more dynamic instances. To alleviate the boundary blur-ring issue and further improve accuracy and generalizability, we propose a dual-branch multimodal fusion MOS network, MOSFormer. The Transformer structure is incorporated to extract spatio-temporal information better, while image semantic information is utilized to refine the boundaries of moving objects. Finally, experiments on two datasets show that our method achieves state-of-the-art performance, and a mapping experiment with our method confirms its effectiveness in downstream tasks such as mapping and localization.

关键词： Location awareness Three-dimensional displays Autonomous systems Semantics Object segmentation Transformers Real-time systems Data mining Faces intelligent robots

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A Deep-Convolution-Generative-Adversarial-Networks-based Missing Data Filling Method for Blast Furnace Gas System in Steel Industry 11

A Deep-Convolution-Generative-Adversarial-Networks-based Mis...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Yang, Canguang Jin, Feng Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The integrity of monitoring data is of great significance to ensure the accuracy of data analysis and reliable operation in blast furnace gas system of steel industry. In this study, a Deep-Convolution-Generative-Adversarial-Networks (DCGAN)-based data filling method is proposed for high proportion missing in time series, and the corresponding network structure is designed. Time series are transformed non-destructively into time domain images by a Gram matrix, and their temporal characteristics are preserved. The authenticity constraint and the context similarity are established to optimize the hidden variables, and the high-precision time domain image is generated through the DCGAN. The completed time series is obtained by inversely transforming the Gram matrix. The simulation results based on the actual operating data of a steel enterprise indicate that the proposed method is capable of has high padding accuracy in the case of a high proportion of random missing data and continuous missing data. © 2022 IEEE.

关键词： Time series

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Non-Repetitive: A Promising LiDAR Scanning Pattern

Non-Repetitive: A Promising LiDAR Scanning Pattern

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

作者： Angchen Xie Yeqiang Qian Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR is an essential sensor for intelligent vehicles. Recently, LiDARs used in vehicles produced by different companies have significant differences in their scanning patterns. Some vehicles use mechanical and solid-state (repetitive) LiDARs, while others use prism-based (non-repetitive) LiDARs. The scanning pattern of a LiDAR has a profound impact on its scanning performance. To investigate the influence of LiDAR scanning patterns, we created the "Repetitive-or-not" dataset, which is collected simultaneously by LiDARs with both repetitive and non-repetitive scanning patterns in the CARLA simulation environment. Using this dataset, we conducted a comprehensive statistical analysis of the scanning ability of repetitive and non-repetitive LiDARs. Furthermore, we looked into the effects of these two LiDAR scanning patterns on the performance of various 3D object detection algorithms. Finally, we explored the domain gap in the point cloud data produced by repetitive and non-repetitive LiDARs. Through an in-depth investigation of the "Repetitive-or-not" dataset, we have discovered that non-repetitive LiDAR shows great promise. This conclusion is primarily supported by its superior object scanning capabilities.

关键词： Point cloud compression Laser radar Three-dimensional displays Statistical analysis intelligent vehicles Object detection Companies Robot sensing systems intelligent robots

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Learning Preconditioners in Gates-controlled Deep Unfolding Networks based on Quasi-Newton Methods For Accelerated MRI Reconstruction

Learning Preconditioners in Gates-controlled Deep Unfolding ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding networks (DUNs) have made significant progress in MRI reconstruction, successfully tackling the problem of prolonged imaging time. However, the ill-conditioned nature of MRI reconstruction often causes slow convergence in iterative optimization, potentially compromising the performance of DUNs. In this study we propose a preconditioned and gates-controlled DUN (PGDUN) to address these challenges. Our approach starts with optimizing the step size of proximal gradient descent (PGD) through a preconditioner. To improve flexibility and adaptability, we relax the constrains on quasi-newton-based optimization procedure. We design ConvLSTM-based modules, where the gate units automatically preserve necessary long- and short-term information, facilitating the learning of optimized variables and their combinations. Furthermore, we design gate units to modulate the features fed to regularizers across different iterations, boosting their robustness against potential accumulated errors. Evaluations using PSNR and SSIM metrics reveal that our approach outperforms existing state-of-the-art methods, achieving superior reconstruction results across various sequences.

关键词： Measurement Magnetic resonance imaging Logic gates Signal processing Robustness Sensors Iterative methods Speech processing image reconstruction Optimization

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Cross-Modal Mask and Detail Alignment for Text-Based Person Retrieval 24th

Cross-Modal Mask and Detail Alignment for Text-Based Perso...

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24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Guo, Ao Liu, Xuan Liu, Xianggan Hu, Bingmeng Yuan, Jie Chu, Chiawei Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China City University of Macau China

ISBN: (纸本)9789819615278

Text-based person retrieval primarily aims to retrieve the images of target persons represented by a given text query. In this task, how to effectively align images and text globally and locally is an important challenge. At the same time, since multiple modalities are involved, reducing the differences between different modalities is also an important challenge. Existing work has focused more on reconstructing an image rather than the semantic consistency between the image and text modalities. Therefore, we introduce the Cross-Modal Mask and Detail Alignment (CMDA) framework to address these challenges. Under this framework, our proposed Cross-Modal Mask Alignment module (CMA) semantically aligns features generated by supplementing randomly masked image/text with another modality text/image. Additionally, to narrow the gap between the image and text modalities, we designed the Cross-Modal Detail Alignment module (CDA), which establishes connections between images and texts and facilitates interactions between these two different modalities. Experimental results show that our model exhibits outstanding performance across multiple public datasets, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReID. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Stacking U-Nets in U-shape: Redesigning the Information Flow in Model-based Networks for MRI Reconstruction

Stacking U-Nets in U-shape: Redesigning the Information Flow...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Model-based networks have shown convincing performance in MRI reconstruction. However, the unrolled cascades within the networks are constrained to solely obtain information from the preceding counterpart, resulting in potential error accumulation. Moreover, the linear structure fails to address the challenge of recovering fine-grained details. To tackle these problems, we propose to redesign the information flow in model-based networks. Our method features a large U-shaped network, where the nodes are built with unrolled cascades and U-Net-based regularizers. We design an input-level integration module to help the cascades acquire information from adjacent and skip-connected counterparts, building robust mappings to the target. We further design a coarse-to-fine feature-level integration module, aiming at guiding the network to progressively recover fine details. Intermediate reconstructions produced by subnetworks of different scales are integrated, enabling the extraction of complementary information to enhance the final performance. Compared with cutting-edge methods on different datasets, our method exhibits superior performances.

关键词： Magnetic resonance imaging Stacking Buildings Signal processing Feature extraction Acoustics Sensors Data mining Speech processing

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Digital Twin Enabled Flight control System Testing: A Physical-Virtual Mapping Experiment

Digital Twin Enabled Flight Control System Testing: A Physic...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Cheng Ren Cailian Chen Shanying Zhu Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Flight control system testing (FCST) is one of the most important testing during aircraft final assembly, however, traditional testing method highly rely on manual labor, resulting in low testing quality and efficiency. Targeting at improving the testing quality and efficiency, in this paper, we apply digtial twin (DT) technology in the FCST process for the first time. A virtual experimental platform named DT-FCST is constructed which is identical to a real wing experimental platform, supporting testing elements management and physical-virtual mapping experiments. The development process of the DT-FCST platform is detailed. Several experiments are conducted to demonstrate the effectiveness of DT technology. Based on motion scripts integrating real-time data from the cockpit, the cockpit control sticks and flight control surfaces are driven to achieve consistency between physical and virtual motions. With the help of DT technology, the entire movement process of control sticks and flight control surfaces are mapped with high fidelity in the DT-FCST platform, greatly enhancing the testing efficiency and monitoring quality.

关键词： System testing Process control Manuals Real-time systems Aircraft Informatics Surface treatment Monitoring Testing Aerospace control

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Energy Management Strategies for PEMFC-Lithium Battery Hybrid Power Systems Based on Pt Degradation

Energy Management Strategies for PEMFC-Lithium Battery Hybri...

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第42届中国控制会议

作者： Ziang Guo Beijia Li Linghong Zeng Jun Fu Chuang Sheng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the platinum(Pt) catalyst during *** this study,we have established a one-dimensional model to investigate the effects of different voltage conditions on Pt *** potential is found to be the most significant factor affecting the *** exposure to high potentials will significantly reduce the PEMFC's lifespan,and frequent voltage changes will exacerbate the *** mitigate this issue,we have designed three energy management strategies with different limitations and simulated their performance in the operating scenario of a fuel-cell hybrid electric vehicle(FCHEV).The results demonstrate that strategies restricting high potential occurrences and voltage variations can effectively alleviate Pt degradation.

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