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UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-Network

IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-Network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. Systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

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Conservative mixed Discontinuous Galerkin finite element method with full decoupling strategy for incompressible MHD problems

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Communications in Nonlinear Science and Numerical Simulation 2025年 147卷

作者： Huang, Huayi Huang, Yunqing Tang, Qili Yin, Lina Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

In this research, we propose a mixed Discontinuous Galerkin (DG) finite element method (FEM) for the numerical solution of incompressible magnetohydrodynamics (MHD) problems, ensuring the divergence-free condition for the velocity field. Our proposed methodology is characterized by its energy stability. Subsequently, we construct a novel finite element discretization scheme, for which we establish the theoretical framework of energy stability. Furthermore, we incorporate a preconditioning strategy that facilitates the development of an alternative positive definite, fully decoupled linearization approach. A significant advantage of this scheme is its ability to circumvent the complexities associated with solving saddle point problems at each time step. We demonstrate the uniqueness and convergence of the solutions within this fully decoupled, block-structured linear system. This theoretical validation is complemented by a series of numerical experiments that substantiate the effectiveness and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Finite element method

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A Fusion Tuning Method for Named Entity Recognition 12th

A Fusion Tuning Method for Named Entity Recognition

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12th CCF Conference on BigData, BigData 2024

作者： Wang, Jitian Chen, Yanping Zou, Anqi Qin, Yongbin Huang, Ruizhang Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819610235

In named entity recognition, the main methods for constructing deep neural networks are fine-tuning and prompt tuning. Fine-tuning is a commonly used paradigm to optimize neural networks by using task-specific objective functions to make them more adapted to solve downstream tasks. In which pre-trained language models are used as external resources to provide contextual representations. Prompt tuning is a recent paradigm in which the downstream tasks are reformulated to look more like those solved during the original PLM training with the help of a textual prompt. It is effective to utilize potential knowledge of PLMs. In this paper, we propose the fusion approach, which sufficiently fuses the semantics obtained by extracting fine-tuning and prompt-tuning. In our approach, the model makes decisions based on the information obtained from the reconstructive fusion of the fine-tuned semantics and the semantics of the pre-trained language model. The representation obtained by fine-tuning and the representation obtained by prompt tuning are reconstructed into a multidimensional semantics using a gate fusion module (GFM). In particularly, our model has two gate components, one gate to extract the semantics of fine-tuning and the other gate to extract the semantics of prompt tuning, and finally the reconstructed semantic representation is obtained. This takes full advantage of the potential knowledge of PLM. Our model is evaluated on the CONLL2003, ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chi, Jianning Lin, Geng Li, Zelan Zhang, Wenjun Chen, Jia-Hui Huang, Ying Northeastern University Faculty of Robot Science and Engineering Shenyang110167 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110167 China University of Saskatchewan Division of Biomedical Engineering SaskatoonS7V5A9 Canada Shengjing Hospital of China Medical University Department of Ultrasound Shenyang110004 China

Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice. The source code and relative datasets will be available at https://***/LanLanUp/SAHP-Net. © 2013 IEEE.

关键词： Ultrasonic imaging

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Homoclinic, heteroclinic and periodic orbits of singularly perturbed systems

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Science China Mathematics 2019年第9期62卷 1687-1704页

作者： Xiang Zhang School of Mathematical Sciences Key Laboratory of Scientific and Engineering Computing (Ministry of Education)Shanghai Jiao Tong University

The main aims of this paper are to study the persistence of homoclinic and heteroclinic orbits of the reduced systems on normally hyperbolic critical manifolds, and also the limit cycle bifurcations either from the homoclinic loop of the reduced systems or from a family of periodic orbits of the layer systems. For the persistence of homoclinic and heteroclinic orbits, and the limit cycles bifurcating from a homolinic loop of the reduced systems, we provide a new and readily detectable method to characterize them compared with the usual Melnikov method when the reduced system forms a generalized rotated vector field. To determine the limit cycles bifurcating from the families of periodic orbits of the layer systems, we apply the averaging *** also provide two four-dimensional singularly perturbed differential systems, which have either heteroclinic or homoclinic orbits located on the slow manifolds and also three limit cycles bifurcating from the periodic orbits of the layer system.

关键词： singular perturbation homoclinic and heteroclinic orbits limit cycle rotating vector fields averaging method

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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

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IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Brain-Inspired Fuzzy Graph Convolution Network for Alzheimer's Disease Diagnosis Based on Imaging Genetics Data

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IEEE Transactions on Fuzzy Systems 2025年

作者： Bi, Xia-An Huang, Yangjun Shen, Wenzhuo Yang, Zicheng Mao, Yuhua Xu, Luyun Liu, Zhonghua Hunan Normal University College of Information Science and Engineering Changsha China Hunan Normal University Key Laboratory of Computing and Stochastic Mathematics Ministry of Education School of Mathematics and Statistics Hunan Changsha410081 China Hunan Normal University College of Business Changsha410081 China Hunan Normal University Hunan Changsha410081 China

The analysis of multi-omics biomedical data has become increasingly critical in clinical decision-making for brain diseases like Alzheimer's Disease (AD). However, the inherent fuzziness of biomedical information limits the classification performance of existing methods, and current disease models struggle to explore pathogenetic mechanisms. Facing with these issues, this paper develops a fuzzy graph-based deep learning method to achieve accurate diagnosis and pathogeny identification for brain diseases. Firstly, fuzzy graphs are constructed to describe the associations between pathogenies using fuzzy memberships. Secondly, a mathematical model inspired by the fuzzy mechanisms of brain is established, effectively capturing the fuzzy congregation patterns of feature information across brain regions and genes. Finally, a Brain-Inspired Fuzzy Graph Convolutional Network (BI-FGCN) is proposed. In BI-FGCN, white-boxed convolutional operations are designed based on the mathematical model. Experimental results across multiple brain disease datasets demonstrate the superiority of BI-FGCN in AD diagnosis and pathogeny identification. We provide a reliable supporting method for the diagnosis and treatment of brain diseases. The code of BI-FGCN is available at: ***/fmri123456/BI-FGCN. © 1993-2012 IEEE.

关键词： Convolution

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories 34

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness...

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34th ACM Web Conference, WWW 2025

作者： Wang, Tianlong Jiao, Xianfeng Zhu, Yinghao Chen, Zhongzhi He, Yifan Chu, Xu Gao, Junyi Wang, Yasha Ma, Liantao School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400712746

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the "truthful" direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑ 142%), LLaMA2 (↑ 24%), Alpaca (↑ 36%), Vicuna (↑ 28%), LLaMA2-Chat (↑ 19%), and LLaMA3(↑ 34%). Furthermore, we verify ACT’s scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. © 2025 Copyright held by the owner/author(s).

关键词： hallucination large language model tuning-free

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