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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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A Single-Loop Narrow-Band Force Rebalance Control Method with Temperature Self-Compensation for a MEMS Gyroscope 38

A Single-Loop Narrow-Band Force Rebalance Control Method wit...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： He, Chunhua Xu, Yingyu Wu, Heng Huang, Qinwen Zhao, Qiancheng Yan, Guizhen Guangdong University of Technology School of Computer China Ceprei Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory China Guangdong University of Technology School of Automation China Peking University School of Integrated Circuits Beijing China National Key Lab of Micro/Nano Fabrication Technology Beijing China

ISBN: (纸本)9798331508890

This paper presents a novel single-loop narrow-band force rebalance control method with temperature self-compensation for a MEMS gyroscope. The narrow-bandwidth force rebalance control for the sense mode is achieved using a simple bandpass filter (BPF) and without modulation or demodulation. The center frequency of the BPF is adjusted automatically to match the resonant frequency of the sense mode. Meanwhile, its gain is tuned based on the real-time quality factor of the sense mode. Experimental results demonstrate that the bias drift of the gyroscope with self-compensation is reduced from 1320 deg/h to 16 deg/h, while the scale factor drift is reduced from 0.616 mV/deg/s to 0.075 mV/deg/s. © 2025 IEEE.

关键词： Gyroscopes

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

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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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Research on Citrus Leaf Disease Recognition Using Class-Agnostic Contrastive Learning and Supervised Organizational Mapping

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IEEE Access 2025年

作者： Xiao, Wen Shang, Jinxia Li, Fan Ao, Ou Wang, Xin Tian, Shiyi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Lab of Computer Technology Applications Kunming650500 China

Early detection of plant diseases is crucial for enhancing agricultural productivity and ensuring crop protection. While computer vision offers scalable alternatives to manual inspection, existing methods face two understudied challenges: (1) performance degradation in cluttered real-world conditions, and (2) ineffective feature separation for randomly distributed lesions with diverse symptoms. Notably, current research predominantly relies on controlled-environment datasets, lacking publicly accessible benchmarks with authentic field complexities. To address these, we present the first large-scale citrus leaf disease dataset (5,763 images, 6 categories) capturing complex field conditions including varying illumination, occlusion, multi-scale lesions, and deceptive backgrounds. Our key methodological innovations include a three-stage weakly supervised framework that systematically resolves background interference and feature redundancy: (i) Class-agnostic contrastive localization enabling precise lesion mapping without region annotations, (ii) Stable feature initialization through region-guided classification training, (iii) Sparse orthogonal mapping with experimentally-determined nodes introducing orthogonality constraints to enhance feature separation. Through feature alignment and feature fusion, an accuracy of 97.1% is achieved (+2.4% vs ResNet-18). Evaluations on citrus leaf and public datasets demonstrate superior accuracy and interpretable lesion localization, advancing plant disease diagnosis. © 2013 IEEE.

关键词： Citrus fruits

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High-Quality Novel View Synthesis of Robotic Surgical Scenes using Gaussian Splatting with Depth Prior

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computer Methods and Programs in Biomedicine 2025年 268卷 108856-108856页

作者： Song, Changshun Peng, Jun Chen, Qi Qin, Zhibao Tai, Yonghang Yunnan Key Lab Optoelectronic Information Technology Yunnan Normal University Kunming650500 China Department of Thoracic Surgery Institute of The First People's Hospital of Yunnan Province Kunming650032 China School of Mathematics and Computer Science Yunnan Minzu University Kunming650031 China Yunnan Province Photoelectric Detection and Perception Technology Engineering Research Center China

Background and Objective: Robot-assisted surgery has revolutionized modern medical procedures by enhancing precision, reducing invasiveness, and providing a clearer, more controlled environment. However, it still faces challenges in fully visualizing the target tissue, particularly from multiple perspectives. This limitation is most evident in minimally invasive surgeries, Therefore, the ability to synthesize new views of the surgical scene is becoming increasingly critical. By generating multi-view visualizations, surgeons can gain a more comprehensive understanding of the target tissue, improving spatial awareness and decision-making during surgery. Methods: This article proposes an innovative novel view synthesis method for robotic surgical scenarios, which utilizes pre-trained depth estimation model to obtain global depth information and solves the scale ambiguity problem encountered in the transition region of the Gaussian distribution in the 3D Gaussian model. In addition, we introduce a multi-scale loss optimization strategy that captures features of various scales through a multi-scale loss function to regularize the Gaussian parameters while maintaining the 3D consistency of the splatting. Results: Our method is evaluated against current scene novel view synthesis techniques using our robotic surgery scene dataset, along with the Hamlyn and Stereo MIS public datasets. The proposed approach achieved an average PSNR of 33.45, SSIM of 0.939, LPIPS of 0.153, and an RMSE of 0.022 across the three datasets. Conclusions: Our approach helps to enhance the visualization capabilities of robotic surgical systems by synthesizing novel views of surgical scenes. A deeper understanding of the target tissue will enhance patient safety during surgery and support surgeon training. These advancements will contribute to the improvement of robot-assisted surgery, making it more adaptable to diverse clinical scenarios. © 2025 Elsevier B.V.

关键词： Robotic surgery

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Efficient Restoration for Binary Neural Networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2025年第2期53卷 568-580页

作者： Zeng, Kai Wan, Zi-Xin Wang, Ming-Tao Shen, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Yunnan Kunming650500 China

Restoring the weight distribution, activation distribution, and gradient to the original full precision network data as much as possible can greatly improve the inference ability of the binary network. However, existing methods directly apply the restoration operation in forward propagation to binary data, and the gradient approximation functions for back-propagation are fixed or manually determined, resulting in the need for improvement in the restoration efficiency of binary networks. To address this problem, the efficient restoration method is investigated for binary neural networks. Firstly, a distribution recovery method for maximizing information entropy is proposed. By shifting the original full precision weight mean and scaling the modulus, the quantized binary weight directly has the characteristic of maximum distribution restoration. At the same time, a simple statistical translation and scaling factor is used to greatly improve the restoration efficiency of weight and activation. Furthermore, it is proposed a gradient function based on adaptive distribution approximation, which dynamically determines the update range of the current gradient in the P-percentile according to the actual distribution of the current full precision data. It adaptively changes the shape of the approximation function to efficiently update the gradient during the training process, thereby improving the convergence ability of the model. On the premise of ensuring the improvement of execution efficiency, theoretical analysis has confirmed that the method proposed in this paper can achieve maximum restoration of binary data. Compared with the existing advanced binary network models, the experimental results of our method show excellent performance, with a 60% and 67% reduction in computational time for the distribution restoration operation quantization of ResNet-18 and ResNet-20, respectively. An accuracy of 93.0% is achieved for VGG-Small binary quantization on the CIFAR-10 dataset

关键词： Restoration

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

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IEEE Transactions on Multimedia 2025年

作者： Wang, Lei Zhan, Yibing Ma, Leilei Tao, Dapeng Ding, Liang Gong, Chen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Jiangsu Nanjing210094 China JD Explore Academy Beijing100000 China Anhui University School of Computer Science and Technology Anhui Heifei230601 China Yunnan University FIST LAB School of Information Science and Engineering Yunnan Kunming650091 China

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this article, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice"in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias;2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also provide a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the potential of extending our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://***/zuiran/SpliceMix. © 1999-2012 IEEE.

关键词： Image classification

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A Single-Loop Narrow-Band Force Rebalance Control Method with Temperature Self-Compensation for a MEMS Gyroscope

A Single-Loop Narrow-Band Force Rebalance Control Method wit...

引用

IEEE International Conference on Micro Electro Mechanical Systems

作者： Chunhua He Yingyu Xu Heng Wu Qinwen Huang Qiancheng Zhao Guizhen Yan School of Computer Guangdong University of Technology China Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory CEPREI China School of Automation Guangdong University of Technology China School of Integrated Circuits Peking University Beijing China National Key Lab of Micro/Nano Fabrication Technology Beijing China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

关键词： Micromechanical devices Band-pass filters Temperature sensors Q-factor Force Resonant frequency Real-time systems Gyroscopes Temperature control Phase locked loops

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