Journal of Physics-Photonics最新文献_第8页

Optimization of Package Heat Dissipation Design Based on High-power WB-BGA Industrial Chip with a Wide Temperature Range 基于大功率宽温度范围WB-BGA工业芯片的封装散热优化设计

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2645/1/012003

Shihua Duan, Dejian Li, Yuan Guan, Bofu Li, Dameng Li, Baobin Yang, Shunfeng Han

引用次数: 0

Angle-only Autonomous Terminal Guidance and Navigation Algorithm for Asteroid Defense based on Meta-reinforcement Learning 基于元强化学习的小行星防御自主末制导导航算法

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012032

Yuhao Pu, Chao Bei

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Operation Optimization of Biomass Integrated Energy System Based on Adjustable Heat-to-Electric Ratio 基于可调热电比的生物质综合能源系统运行优化

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2636/1/012050

Changcheng Song, Zhaojun Lu, Wen Zhang, Ao Guo

引用次数: 0

Characteristics Analysis of Hybrid Optical Amplifier with Doped Fiber Variations for Fiber Optic Communications Network 光纤通信网络中掺光纤变化的混合光放大器特性分析

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2623/1/012022

R Ramadani, S A Khairunisa, M Khoiro

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Vibration Reduction of Robot End Effector Based on Co-simulation Method 基于联合仿真方法的机器人末端执行器减振

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012036

Daixing Lu, Yang Zhang, Junjie Lu

引用次数: 0

Intelligent Vehicle Systematic Design Based on Arduino and Raspberry Pi 基于Arduino和树莓派的智能汽车系统设计

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012017

Yulin Liu, Xiaolu Liu, Chunguang Lu, Lei Song, Guoyu Cui, Haifeng Qian, Nick Nianxiong Tan

引用次数: 0

English Pronunciation Quality Evaluation System Based on Continuous Speech Recognition Technology for Multi-Terminal 基于连续语音识别技术的多终端英语语音质量评价系统

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012024

Xianxian Wu, Yan Zhang, Bin Feng

引用次数: 0

An Improved Convolutional Neural Network for Particle Image Velocimetry 一种改进的卷积神经网络用于粒子图像测速

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2645/1/012013

Shuicheng Gong, Fuhao Zhang, Gang Xun, Xuesong Li

引用次数: 0

Insulator Defect Detection Method upon Fused Attention Mechanism and Bidirectional Feature Fusion 基于融合注意机制和双向特征融合的绝缘子缺陷检测方法

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012013

Yiming Chen

{"title":"Insulator Defect Detection Method upon Fused Attention Mechanism and Bidirectional Feature Fusion","authors":"Yiming Chen","doi":"10.1088/1742-6596/2632/1/012013","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012013","url":null,"abstract":"Abstract Insulators are important components for achieving electrical insulation and mechanical support, but they are prone to various defects in harsh operating environments, which can damage their mechanical strength and insulation performance. This article proposes the Shuffle YOLOv7 model based on the YOLOv7 algorithm for insulator defect detection, aiming to solve the weakness of low precision in traditional object detection algorithms when facing complex backgrounds and small-sized defects. To address the issue of low attention to flashover faults in traditional algorithms, the ShuffleAttention fusion attention mechanism is supplied to concentrate on both intra-channel and inter-channel deep features, and the original PANet structure is replaced with a pyramid which has a bidirectional feature fusion structure to enhance the network’s feature extraction ability. The Focal-EIOU LOSS optimization method focuses on high-quality prior boxes to improve model accuracy, and the effectiveness of the optimization method is verified through ablation experiments. These results of the experiment show that the proposed algorithm achieves varying degrees of performance improvement in terms of precision, recall, average precision, and overall loss compared to mainstream object detection algorithms in detecting insulator damage and flashover.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":"95 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OPSNet: Point Cloud Registration Based on Overlapping Predictive Segmentation OPSNet:基于重叠预测分割的点云配准

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012005

Jiuxin Hu, Zhihao Pan, Zhiyong Li, Jin Tang

{"title":"OPSNet: Point Cloud Registration Based on Overlapping Predictive Segmentation","authors":"Jiuxin Hu, Zhihao Pan, Zhiyong Li, Jin Tang","doi":"10.1088/1742-6596/2632/1/012005","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012005","url":null,"abstract":"Abstract Registration is a critical task in the field of point clouds, aiming to align data acquired at different times or from different viewpoints for accurate matching. Deep learning methods have made important progress in point cloud registration tasks. However, most existing approaches do not handle the non-overlapping parts of point clouds, resulting in poor performance in low-overlap and noisy scenarios. We propose a registration model called OPSNet, which achieves optimal alignment transformation estimation and overlapping region prediction through an iterative process. OPSNet consists of modules including global feature extraction, overlapping region prediction segmentation, and alignment registration. By utilizing a segmentation algorithm to deal with the non-overlapping parts of data, OPSNet reduces the adverse effects caused by non-overlapping regions in point cloud registration. The model learns feature representations and performs iterative optimization to achieve precise point cloud alignment. We conduct comprehensive experiments on common point cloud registration datasets and compare OPSNet with several classical point cloud registration methods. The experimental results display that OPSNet achieves outstanding performance in terms of rotation and translation errors, outperforming other methods. Additionally, we evaluate the registration performance under different overlap ratios and find that OPSNet can achieve better registration results even in low-overlap scenarios.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":"93 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0