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MLDA-Net: Multi-Level Deep Aggregation network for 3D Nuclei Instance Segmentation

IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3516-3525页

作者： Hu, Bin Ye, Zhiwei Wei, Zimei Snezhko, Eduard Kovalev, Vassili Ye, Mang Hubei University of Technology School of Computer Wuhan China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan China United Institute of Informatics Problems of the National Academy of Sciences of Belarus Belarus Wuhan University School of Computer Science Wuhan China

Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation standard. However, convolutional layers are limited by their finite receptive fields and weight-sharing mechanisms. Consequently, they struggle to effectively model long-range dependencies and spatial correlations, which may lead to inadequate nuclei segmentation. Moreover, the diversity in nuclear appearance and density poses additional challenges. This work proposes a lightweight multi-layer deep aggregation network, MLDA-Net, incorporating Wide Receptive Field Attention (WRFA). This module effectively simulates the large receptive field generated by self-attention in the Swin Transformer while requiring fewer model parameters. This design implements an extended global sensory field that enhances the ability to capture a wide range of spatial information. In addition, the multiple cross-attention (MCA) module in MLDA-Net enhances the output features of different resolutions from the encoder while maintaining global effectiveness. The Multi-Path Aggregation Feature Pyramid network (MAFPN) receives multi-scale outputs from the MCA module, generating a robust hierarchical feature pyramid for the final prediction. MLDA-Net outperforms state-of-the-art networks, including 3DU-Net, nnFormer, UNETR, SwinUNETR, and 3DUXNET, on the 3D volumetric datasets NucMM and MitoEM. It achieves average performance improvements of 4% to 7% in F1 score, MIoU, and PQ metrics, thereby establishing new benchmark results. © 2013 IEEE.

关键词： Fluorescence microscopy

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Multiagent Deep Reinforcement Learning for Transactive Energy Management of MMGs Incorporating Battery Swapping Stations

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IEEE Transactions on Industrial Informatics 2025年

作者： Cai, Ting Zhang, You Wu, Yuxin Yan, Haoyuan Zhao, Tianyang Hubei University of Technology School of Computer Science Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan430068 China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Jinan University Energy and Electricity Research Center International Energy College Zhuhai519070 China Xi'an Jiaotong University School of Electrical Engineering Xi'an710049 China

Optimal energy management between microgrids (MGs) and battery swapping stations (BSSs) offers significant economic benefits. However, existing works face challenges in formulating optimal interaction strategies between MGs and BSSs, due to the temporal-spatial uncertainty of distributed renewables and emerging loads, as well as incomplete information. This article addresses the energy transaction problem between multi-MGs and multi-BSSs using a hybrid multiagent deep reinforcement learning approach to minimize operation costs. Specifically, a hierarchical transactive energy management community is introduced to facilitate energy exchange between MMGs and BSSs, considering different stakeholder interests. The problem is modeled as a partially observable Markov game. The proposed hybrid algorithm, combining multiagent proximal policy optimization (MAPPO) and double deep Q-network (DDQN), handles the continuous scheduling of MGs and the discrete operations of BSSs. Numerical results show that, averaged over the baselines, the proposed MAPPO-DDQN reduces 13.71% of MGs' operation costs and increases 14.62% of BSSs' profit. © 2005-2012 IEEE.

关键词： Deep reinforcement learning

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Earthquake Magnitude Prediction Using Lstm Based on Strengthened Elitist Genetic Algorithm and Artificial Immune network

SSRN

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SSRN 2025年

作者： Zhou, Wen Wang, Han Ye, Zhiwei He, Qiyi Lin, Qiao Gong, Xiuwen Shen, Jun School of Computer Science Hubei University of Technology Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan430068 China School of Computer Science University of Nottingham NottinghamNG7 2RD United Kingdom School of Computer Science The University of Sydney SydneyNSW2006 Australia School of Computing and IT University of Wollongong WollongongNSW2522 Australia

Earthquakes, as a natural disaster, pose a significant threat to human life and property. Currently, earthquake magnitude prediction research is constantly deepening, aiming to improve prediction accuracy and reduce the losses caused by earthquakes. Machine Learning (ML) has been widely applied in earthquake prediction. However, the performance of these earthquake prediction approaches is limited by redundant seismic data features, the difficulty in capturing long-term dependencies in time-series data, and challenges in hyperparameter optimization. To address these issues, we propose an earthquake magnitude prediction method based on Long Short-Term Memory (LSTM) networks enhanced by Strengthened Elitist Genetic Algorithms and Artificial Immune networks (SEGA-AIN-LSTM). Specifically, a Strengthened Elitist Genetic Algorithm (SEGA), which combines the cloning selection strategy from immunology, is employed for feature selection in seismic data, efficiently selecting the optimal subset of features that are most strongly correlated with earthquake magnitude. To handle the issue of capturing long-term dependencies in time-series data, LSTM is introduced for training and prediction using the selected feature data. Furthermore, for obtaining the optimal hyperparameter combination and improving the model's prediction accuracy, a novel Artificial Immune network (AIN) incorporating Latin Hypercube Sampling (LHS) and an Adaptive Gaussian Mutation (AGM) strategy is used for LSTM hyperparameter optimization. The seismic acoustic and electromagnetic feature data used in this study are sourced from our self-developed Acoustic \& Electromagnetism to AI (AETA) system. To validate the effectiveness of the proposed method, we conducted training and prediction on six station datasets of AETA. By comparing various methods on evaluation metrics such as RMSE, MAE, and R², experimental results demonstrate that our proposed SEGA-AIN-LSTM earthquake magnitude prediction model outperforms st

关键词： Time series

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A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras

arXiv

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arXiv 2025年

作者： Xiao, Teng Hu, Qi Yan, Qingsong Liu, Wei Ye, Zhiwei Deng, Fei School of Computer Science Hubei University of Technology Wuhan China Hubei Key Laboratory of Green Intelligent Computing Power Network Wuhan China School of Surveying and Mapping Wuhan University Wuhan China Wuhan Tianjihang Information Technology Co. Ltd. Wuhan China

Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. This paper presents a Forward Distortion and Backward Warping network (FDBW-Net), a novel framework for wide-angle image rectification. It begins by using a forward distortion model to synthesize barrel-distorted images, reducing pixel redundancy and preventing blur. The network employs a pyramid context encoder with attention mechanisms to generate backward warping flows containing geometric details. Then, a multi-scale decoder is used to restore distorted features and output rectified images. FDBW-Net’s performance is validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net achieves SOTA performance in distortion rectification, boosting the adaptability of PTZ cameras for practical visual applications. © 2025, CC BY.

关键词： Nonlinear distortion

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm Via Proximal Policy Optimization in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Cai, Ting Hu, Ying Li, Xiang Chen, Wuhui Wu, Yuxin Ye, Zhiwei Zheng, Zibin Hubei University of Technology China China Hubei University of Technology School of Computer Science The Provincial Key Laboratory of Green Intelligent Computing Network Wuhan430068 China Sun Yat-sen University China Pengcheng Laboratory China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China

Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-Trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection;2) incentives are designed to maximize instantaneous profit without considering the long-Term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-Term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an 'intelligent engine' to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-Term maximization of the system's social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment. © 1975-2011 IEEE.

关键词： Differential privacy

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