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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

arXiv

引用

arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Information filtering

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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

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IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Long-range Brain Graph Transformer

arXiv

引用

arXiv 2025年

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. Copyright © 2025, The Authors. All rights reserved.

关键词： Neurons

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Multispectral point cloud superpoint segmentation

引用

Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Existence and Non-Existence of Solutions to Some Degenerate Coercivity Quasilinear Elliptic Equations with Measure Data

引用

Journal of Mathematical Research with applications 2022年第2期42卷 173-188页

作者： Maoji RI Xiangrui LI Qiaoyu TIAN Shuibo HUANG School of Mathematics and Computer Science Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University

In this article,we study the existence and non-existence of weak solutions to the following quasilinear elliptic problem with principal part having degenerate coercivity and nonlinear term involving gradie,■where??RN... 详细信息

关键词： elliptic equation degenerate coercivity measures data existence non-existence

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Multi-Modal Instrument Recognition Method Based on Improved YOLOv5s and ESPNet 6

Multi-Modal Instrument Recognition Method Based on Improved ...

引用

6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Shen, Shixian Wang, Jianguo Feng, Yong Li, Yingna Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China China Copper Industry Co. Ltd Kunming China

ISBN: (纸本)9798350374315

Addressing the challenges of high cost, low accuracy, and poor real-time performance in anomaly detection within industrial production processes, this study proposes a multi-modal instrument recognition method based on improved YOLOv5s and ESPNet. Specifically, a dynamic non-monotonic focusing mechanism is introduced into the YOLOv5s object detection model, enhancing its ability to accurately detect the position of pointer-type instruments. Simultaneously, affine and perspective transformations are employed to rectify skewed and rotated images. Secondly, variable convolutions are integrated into the ESPNet segmentation network to adaptively capture nonlinear deformation elements of pointers and dials in images, thereby extracting comprehensive key information from both. Subsequently, a Hough transform operation is applied to fit the straight line representing the pointer, while contour tracking methods are utilized to extract the valid range of dial data. Based on linear proportional relationships, the reading of pointer-type instruments is computed. Finally, a comprehensive model is built by integrating image-acquired instrument data with corresponding sensor data, enabling anomaly detection from a multimodal data analysis perspective. Experimental results demonstrate an identification accuracy of 96.21% for pointer-type instruments, with an average detection speed of 0.216 seconds and a data anomaly detection rate of 99.43%. This method accurately and rapidly identifies data anomalies, meeting the demands for key indicator monitoring in industrial production. © 2024 IEEE.

关键词： Hough transforms

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Developing microwave-induced thermoacoustic tomography:System,application,and reconstruction

引用

Journal of Electronic Science and Technology 2023年第4期21卷 1-19页

作者： Shuang-Li Liu Wan-Ting Peng Xiao-Zhang Zhu Jian Song Xin Shang Hua Zhang Zhi-Qin Zhao School of Information Engineering Southwest University of Science and TechnologyMianyang621010China School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China

A microwave-induced thermoacoustic imaging(MITAT)system is a non-destructive physical medical imaging method that combines the advantages of the high contrast of microwave imaging and the high resolution of ultrasound *** uses the microwave as the excitation source and ultrasound as the information *** different kinds of biological tissue absorb electromagnetic energy,it results in localized temperature *** thermal expansion will induce ultrasonic signals(i.e.,thermoacoustic signals),known as the thermoacoustic *** microwave absorption image of the sample can be reconstructed by algorithm *** MITAT contrast depends on different dielectric parameters of different kinds of *** introduce the developed system and its *** addition,the challenges and prospects of MITAT for further development are discussed.

关键词： application Microwave-induced thermoacoustic tomography(MITAT) Reconstruction System

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