Journal of Physics-Photonics最新文献_第8页

Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network 基于人工神经网络的双轴跟踪太阳能槽式集热器性能预测

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2636/1/012040

Jue Li, Ting Xia, Ruofan Wang, Haijun Chen, Xiran Xu

{"title":"Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network","authors":"Jue Li, Ting Xia, Ruofan Wang, Haijun Chen, Xiran Xu","doi":"10.1088/1742-6596/2636/1/012040","DOIUrl":"https://doi.org/10.1088/1742-6596/2636/1/012040","url":null,"abstract":"Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764055","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

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

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

Multi-pedestrian Tracking Method Fusing Two-stage Matching 融合两阶段匹配的多行人跟踪方法

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012025

Xin Deng, Lijun Zhao, Ruifeng Li

{"title":"Multi-pedestrian Tracking Method Fusing Two-stage Matching","authors":"Xin Deng, Lijun Zhao, Ruifeng Li","doi":"10.1088/1742-6596/2632/1/012025","DOIUrl":"https://doi.org/10.1088/1742-6596/2632/1/012025","url":null,"abstract":"Abstract Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135716633","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

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":null,"pages":null},"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

An airborne object detection and location system based on deep inference 一种基于深度推理的机载目标检测定位系统

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2632/1/012019

Xiao Hu, Shenfu Pan, Dongdong Li, Long Feng, Yuan Zhao

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":null,"pages":null},"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

Reaction Controllable preparation and electrocatalytic performance of two-dimensional sulfides 二维硫化物的反应可控制备及其电催化性能

Journal of Physics-Photonics Pub Date : 2023-11-01 DOI: 10.1088/1742-6596/2645/1/012017

None XinWang, Qi Chao Yang, Hai tao Wang, Yu Zheng, Geng hang Zhong, Jiang wei Zhao

引用次数: 0