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Trajectory optimization for UAV-enabled relaying with reinforcement learning

Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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A Part-of-Speech Tagging Model Employing Word Clustering and Syntactic Parsing

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Chinese Journal of Electronics 2025年第1期23卷 109-114页

作者： Lichi Yuan School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classical HMM, Markov family model (MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-of-Speech tagging experiments show that the improved Part-Of-Speech tagging model has higher performance than Hidden Markov models (HMMs) under the same testing conditions, the precision is enhanced from 94.642% to 97.235%.

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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SlagNet: A Real-Time Slag Segmentation Model Based on Context and Edge Fusion 16th

SlagNet: A Real-Time Slag Segmentation Model Based on Conte...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Liu, Kecheng Li, Jiangyun Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the process of iron and steel smelting, steel slag is inevitably produced as a byproduct. Accurately identifying steel slag is a prerequisite for controlling the content of steel slag. Conventional vision-based steel slag processing solutions are constrained by real-time performance or robustness against disturbances, rendering them directly applied in industrial sites. To address these issues, SlagNet, a steel slag segmentation network with real-time performance and strong disturbance resistance, is designed in this paper. By designing a lightweight branch to extract rich context information, and combining edge branches to supplement the slag edge information, 77.69 mIOU and 13.25 FPS are achieved in the field collection of slagging data set. The results indicate that SlagNet achieves the optimal balance between real-time performance and accuracy in comparison with other segmentation methods, thereby meeting the requirements of industrial sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slags

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Prediction of Rolling Force in Hot Rolling Process Based on Rollformer Model 16th

Prediction of Rolling Force in Hot Rolling Process Based on ...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Li, Qing Jiang, Pengyuan Yuan, Li School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

ISBN: (纸本)9789819617760

In the steel manufacturing industry, the precise forecasting of rolling forces in the hot rolling process is vital for enhancing production efficiency and ensuring product quality. Traditional forecasting methods exhibit limitations when addressing the nonlinearity and variability of industrial data, and current deep learning models are slow to respond to real-time data, with room for improvement in predictive accuracy within dynamic production settings. This paper introduces the Rollformer deep learning model, which adeptly captures intricate nonlinear patterns through the self-attention mechanism's nonlinear transformation capabilities, and boosts sensitivity and adaptability to data variability via adaptive weight distribution. The Rollformer model also pioneers the Roll Self-attention mechanism, leveraging probabilistic sparsity to diminish computational complexity and accelerate the model’s response. Additionally, it employs a generative decoder for swift long-sequence predictions, thereby augmenting the model’s predictive precision. Validated with extensive real-world data from steel mills, the Rollformer model demonstrates superior predictive accuracy and response speed when compared to other Transformer-based deep learning models such as Autoformer and Conformer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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CASSNet: Cross-Attention Enhanced Spectral–Spatial Interaction Network for Hyperspectral Image Super-Resolution

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11716-11730页

作者： Zhang, Zhanxu Yang, Linzi Zhang, Guanglian Deng, Jiangwei Bian, Lifeng Yang, Chen Guizhou University Power Systems Engineering Research Center Ministry of Education College of Big Data and Information Engineering Guiyang550025 China Fudan University Frontier Institute of Chip and System Shanghai200433 China Guizhou University China State Key Laboratory of Public Big Data Guiyang550025 China

Deep-learning-based super-resolution (SR) methods for a single hyperspectral image have made significant progress in recent years and become an important research direction in remote sensing. Existing methods perform well in extracting spatial features, but challenges remain in integrating spectral and spatial features when modeling global relationships. In order to take full advantage of the higher spectral resolution of hyperspectral images, this article proposes a novel hyperspectral image SR method (CASSNet), which integrates convolutional neural networks and cross-attention mechanisms into a unified framework. This approach achieves comprehensive integration of spectral and spatial information, with extensive exploration at both local and global levels. In the local feature extraction stage, parallel 3-D/2-D convolutions work in tandem to efficiently capture detail information from both spectral and spatial dimensions. In addition, a spectral–spatial dual-branch module employing the cross-attention mechanism is designed to capture the global dependencies within the features, where the reconstructed spectral–spatial module and the spectral–spatial interaction unit can effectively promote the interaction and complementarity of spectral–spatial features. The experiments on three publicly available datasets demonstrated that the proposed method obtained superior SR results, outperforming state-of-the-art SR algorithms. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Dynamic graph-based graph attention network for anomaly detection in industrial multivariate time series data

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Gao, Cong Ma, Hongye Pei, Qingqi Chen, Yanping School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China School of Telecommunications Engineering Xidian University Xi’an710071 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China

For industrial big data, anomaly detection for multivariate time series data is of critical strategic significance. However, the complexity of industrial equipment and production processes, combined with the high dimensionality of production data, makes it challenging for traditional anomaly detection methods to effectively capture the complex interdependencies and dynamic evolutionary relationships among multiple variables. Additionally, issues such as unstable data distributions, variability, and data drift exacerbate the challenges faced by anomaly detection methods in industrial data analysis and decision-making. This study presents a dynamic graph-based graph attention network for anomaly detection in a multivariate time series data (D-GATAD) model, introducing an innovative approach to dynamic graph construction. The proposed method seamlessly integrates node content features with graph topological structure information, enabling adaptive construction of dynamic graphs based on the current sensor network structure. This design allows for precise modeling of the complex temporal dependencies between variables. Furthermore, the method incorporates an optimized prediction-based model design that organically combines embedding vectors with node data, thereby significantly enhancing the interpretability of the analytical results. Experimental evaluations demonstrate that the proposed method outperforms existing state-of-the-art models across multiple public benchmark datasets. Notably, on the highly complex WADI dataset, it achieves a 5.12% improvement in the AUC score, underscoring its robustness and effectiveness in industrial anomaly detection. This research offers an innovative and widely applicable solution for industrial data analysis and anomaly detection, with significant implications for practical deployment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph neural networks

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Design of PMCW Millimeter Wave Radar Algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Attention 31st

MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Att...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Yang, Enhui Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620630

The Segment Anything Model (SAM), introduced in 2023, has made significant advancements in the field of computer vision. However, SAM faces two major challenges: limitations related to single-scale processing and high computational demands for large-scale data handling, which restrict its widespread adoption in practical applications. Therefore, we propose MS-SAM to achieve better segmentation performance at a lower cost. We introduce a multi-scale image encoder to handle images of varying scales and complexities. Additionally, we propose a dynamic weighted agent attention, which can flexibly and efficiently adjust attention distribution while reducing computational costs. We train MS-SAM on a 1/20 subset of the SA-1B dataset for instance segmentation using points or boxes as prompts. The results show that we maintain the accuracy with the SAM method while reducing the parameters by about 6 times. We give sufficient experimental results to demonstrate its effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Theoretical analyses of solute concentration-gradient driven water flow and solute concentration variation in solution-saturated semi-permeable materials

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Science China Technological Sciences 2025年第5期68卷 247-257页

作者： Yao LIU Chongbin ZHAO Bruce HOBBS Alison ORD Xiangtao ZHANG School of Tourism Data Guilin Tourism University Guangxi Key Laboratory of Culture and Tourism Smart Technology Guilin Tourism University Computational Geosciences Research Centre Central South University School of Earth and Environment the University of Western Australia Department of Hydraulic Engineering Tsinghua University

Based on the commonly-used definition of the semi-permeable material(SPM), in which the solvent(such as water)can pass through but the solute cannot move freely, a novel constitutive equation is presented, in this paper, to theoretically express the intrinsic constitutive relationship between the porosity variation rate with time and the solute concentration variation rate with time. From this theoretical finding, a mathematical model is established to describe the solute concentrationgradient driven water flow and solute concentration variation problem in solution-saturated semi-permeable materials(SSSPMs). In particular, the solute concentration variation in the considered problem can be mathematically described as a linear homogeneous second-order partial differential equation with a variable coefficient in the front of the time term. A special mathematical transform is presented and used to solve this equation, so that the analytical solution for the solute concentration variation in the considered problem has been derived in a purely mathematical manner. The derived analytical solution is then used to provide some theoretical understanding of solute concentration variations in the SSSPM layer.

关键词： water flow concentration gradient semi-permeable material theoretical analyses analytical solution mathematical proof

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