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Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification

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Nature communications 2025年第1期16卷 5027页

作者： Tao Liu Jiahao Liu Dong Li Shan Tan Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. State Key Laboratory of Membrane Biology Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure IDG/McGovern Institute for Brain Research New Cornerstone Science Laboratory School of Life Sciences Tsinghua University Beijing 100084 China. li-dong@***. Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. shantan@***.

The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell super-resolution imaging. Although recent deep learning techniques have substantially advanced SIM, their transparency and reliability remain uncertain and under-explored, often resulting in unreliable results and biological misinterpretation. Here, we develop Bayesian deep learning (BayesDL) for SIM, which enhances the reconstruction of densely labeled structures while enabling the quantification of super-resolution uncertainty. With the uncertainty, BayesDL-SIM achieves high-fidelity distribution-informed SIM imaging, allowing for the communication of credibility estimates to users regarding the model outcomes. We also demonstrate that BayesDL-SIM boosts SIM reliability by identifying and preventing erroneous generalizations in various model misuse scenarios. Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. Furthermore, we demonstrate the effectiveness and superiority of BayesDL-SIM in live-cell imaging, which reliably reveals F-actin dynamics and the reorganization of the cell cytoskeleton. This work lays the foundation for the reliable implementation of deep learning-based SIM methods in practical applications.

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Efficient 3D Deep LiDAR Odometry

arXiv

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arXiv 2021年

作者： Wang, Guangming Wu, Xinrui Jiang, Shuyang Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Artificial Intelligence of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper. In this architecture, the projection-aware representation of the 3D point cloud is proposed to organize the raw 3D point cloud into an ordered data form to achieve efficiency. The Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to estimate and refine the pose in a coarse-to-fine approach. A projection-aware attentive cost volume is built to directly associate two discrete point clouds and obtain embedding motion patterns. Then, a trainable embedding mask is proposed to weigh the local motion patterns to regress the overall pose and filter outlier points. The trainable pose warp-refinement module is iteratively used with embedding mask optimized hierarchically to make the pose estimation more robust for outliers. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask, and all operations involving point cloud sampling and grouping are accelerated by projection-aware 3D feature learning methods. The superior performance and effectiveness of our LiDAR odometry architecture are demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms all recent learning-based methods and even the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. We open sourced our codes at: https://***/IRMVLab/EfficientLO-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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城市耦合风险——以人为本的复杂系统视角（英文）

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Engineering 2025年第1期 44-50页

作者：欧阳敏程泽楷马佳欣王红卫 Stergios Aristoteles Mitoulis School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology School of Management Huazhong University of Science and Technology School of Engineering University of Birmingham

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

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Visual SLAM Based on Dynamic Object Detection

Visual SLAM Based on Dynamic Object Detection

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第33届中国控制与决策会议

作者： Bocheng Chen Gang Peng Dingxin He Cheng Zhou Bin Hu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Shantui Construction Machinery Co. Ltd.

On the one hand,traditional visual SLAM does not consider dynamic objects in the scene,on the other hand,deep learning technology has been widely used in computer *** paper combines the two organically,and proposes an algorithm that uses dynamic object detection to improve the robustness of visual SLAM in a dynamic ***,we use the object detection network integrated into the attention mechanism to detect the dynamic target in the key ***,we follow the optical flow detection to further determine the dynamic feature points in the scene and eliminate ***,we use the static feature points for camera tracking to achieve highly robust monocular visual *** method described in this paper can not only eliminate dynamic feature points,but also retain as many static feature points as *** method described in this paper is compared with the original ORB-SLAM2 algorithm and DS-SLAM algorithm,and tested with public data *** results show that the method described in this paper can effectively eliminate the influence of dynamic objects on the visual SLAM algorithm.

关键词： Visual SLAM dynamic object detection deep learning optical flow detection attention mechanism

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Topology Inference on Partially Observable Mobile Robotic Networks under Formation control

Topology Inference on Partially Observable Mobile Robotic Ne...

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European control Conference (ECC)

作者： Yushan Li Jianping He Lin Cai The 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 China University of Victoria BC Canada

Mobile robotic networks (MRNs) have received considerable attention in recent years. Among their wide applications, the interaction topology plays a critical role in achieving efficient cooperation. We demonstrate that the observable interaction process of MRNs under formation control will reveal the interaction topology, presenting increasingly viable threats to the security of MRNs. To practice, we focus on a general situation where the external observer has only partial observation over the global MRN and aims to infer the interaction topology. This is a very challenging problem because the observable state evolution is a coupled consequence of the underlying interaction topology, the unknown leader dynamics, and the unobservable part. To address this issue, we novelly identify the steady pattern of the MRN and filter the impacts of the observable parts by shrinking the inference range. By constructing the informative dataset reflecting the interaction process, the local interaction topology is approximated with high accuracy. Simulations illustrate the feasibility and effectiveness of the proposed method.

关键词： Sufficient conditions Network topology Shape Process control Europe Estimation Observers

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Evolutionary attraction–repulsion algorithm embedded with LLM for UAV task allocation

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Advanced Engineering Informatics 2025年 66卷

作者： Wu, Bowen Xiao, Renbin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

Labor division provides an adaptive and scalable technique for unmanned systems. However, its designing process usually relies heavily on the manually crafted paradigms, leading to inefficiencies and insufficient performance. To overcome these challenges, this paper introduce an evolutionary approach to labor division algorithm design. Drawing inspiration from the regulatory behaviors observed in natural lion populations, we propose a bipolar feedback-guided attraction–repulsion mechanism. A formal model is developed to encapsulate this mechanism, and we provide an in-depth analysis of the algorithm's evolution through the framework of systems science. In addition, we leverage prompt engineering techniques to integrate a large language model (LLM) with our proposed mechanism, facilitating a bidirectional enhancement between the two. This integration culminates in the development of the ARE-LLM framework, a robust and adaptive solution for designing labor division algorithms. To demonstrate the practical applicability of our approach, we apply the ARE-LLM framework to the UAV-based task allocation problem for enemy air defense suppression. The experimental results indicate that our method outperforms several advanced approaches. In a scenario involving 50 UAVs tasked with completing 300 assignments, the total distance traveled is only 9 % of that required by the ALPDA algorithm, while the processing time is reduced to 8 % of that used by ALPDA. © 2025 Elsevier Ltd

关键词： Target drones

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Global Update Guided Federated Learning

arXiv

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arXiv 2022年

作者： Wu, Qilong Liu, Lin Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electronic Information Engineering Shanghai Dianji University Shanghai201306 China

Federated learning protects data privacy and security by exchanging models instead of data. However, unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning algorithms. To alleviate this problem, unlike previous studies that limit the distance of updates for local models, we propose global-update-guided federated learning (FedGG), which introduces a model-cosine loss into local objective functions, so that local models can fit local data distributions under the guidance of update directions of global models. Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models. Numerical simulations show that, compared with other advanced algorithms, FedGG has a significant improvement on model convergence accuracies and speeds. Additionally, compared with traditional fixed loss weights, adaptive loss weights enable our algorithm to be more stable and easier to implement in practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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High-Resolution representation object pose estimation from monocular images

High-Resolution representation object pose estimation from m...

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Chinese Automation Congress (CAC)

作者： Qi Guan Wenfa Li Shibei Xue Dewei Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665426480

This paper addresses the 6D challenge of monocular 6D pose estimation on small targets and weak texture objects. We propose a monocular object pose estimation algorithm, High Resolution Object Pose Estimation (HRPE) based on the high-resolution feature representation, which detects the 2D keypoints and uses the Perspective-n-Point (PnP) algorithm to compute the 6D pose. HRPE can maintain high-resolution feature representation through parallel convolution of multi-resolution feature branches and repeated cross-scale fusion, generating more precise pose estimation. Experimental results show that HRPE surpasses the existing state-of-the-arts, and achieves a significant accuracy improvement for small objects, which demonstrates the effectiveness of high-resolution feature representation on pose estimation.

关键词： Measurement Automation Convolution Pose estimation Feature extraction

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Acquisition and Analysis of Rotating Machinery Rotor Vibration Signal by Non-Contact Laser Sensing

SSRN

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SSRN 2024年

作者： Yang, Shangjun Wu, Jiali Ke, Chenghu Zhang, Feng Zhu, Chunhua Ke, Xizheng Key Laboratory of Grain Information Processing and Control [Henan University of Technology Ministry of Education Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China School of Information Engineering Xi’an University Xi’an China School of Electronic Information Engineering Xi’an Technological University Xi’an China School of Automation and Information Engineering Xi’an University of Technology Xi’an China

Conventional high-speed rotating machinery uses a contact structure to realize data transmission between rotating parts and fixed parts. Mechanical wear and vibration noise can interfere with the subsequent processing and fault diagnosis of vibration signals. This paper designs a contactless vibration signal transmission system for rotating machinery using laser sensing. It analyzes the non-coincidence error between the optical axis and the rotating axis, which causes the effective photosensitive area and practical power of the detector to change periodically with the frequency of the rotational speed, resulting in the vibration signal presenting various harmonics with the rotational speed angular frequency as the fundamental wave. A convenient time-frequency joint analysis method is proposed using fractional Fourier transform in time-frequency domain projection localization. Using mixed programming techniques of C# and Matlab, an upper computer interface for data storage, processing, network publishing, is developed to form a complete vibration signal processing system. © 2024, The Authors. All rights reserved.

关键词： Rotating machinery

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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