光电工程最新文献_第10页

Structural optimization design of lightweight rectangular reflective mirror 轻量化矩形反射镜结构优化设计

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-08-31 DOI: 10.12086/OEE.2020.200109

Wang Pengpeng, Xin Hongwei, Zhu Junqing, W. Yongxian, Xu Yanjun, C. Changzheng

{"title":"Structural optimization design of lightweight rectangular reflective mirror","authors":"Wang Pengpeng, Xin Hongwei, Zhu Junqing, W. Yongxian, Xu Yanjun, C. Changzheng","doi":"10.12086/OEE.2020.200109","DOIUrl":"https://doi.org/10.12086/OEE.2020.200109","url":null,"abstract":"In order to solve the problem that the mass and the surface figure accuracy of the space reflective mirror are often contradictory in the lightweight design process, a structural optimization design of a lightweight rectangular reflective mirror of an off-axis three-reflection optical system is performed. In this study, a lightweight structure based on the center support of SiC materials is proposed. At the same time, a multi-objective optimization method is introduced. With the RMS value and Mass as the optimization targets at the same time, a mirror optimal structure model is obtained with a mass of 2.32 kg. Compared with the solid mirror, the lightweight ratio is 73.8%. Then the mirror subassembly is designed and the integrated performance of it is simulated. It shows that the RMS value of the mirror reaches respectively 2.5 nm, 2.2 nm and 7.3 nm when gravity load is applied in the directions of X, Y and Z axes. Furthermore, the RMS value is 3.2 nm when the mirror subassembly is under the load condition of uniform temperature rise of 4 ℃, which is far less than the requirement of RMS≤λ/50(λ=632.8 nm). Therefore the data meets the design requirements.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82496051","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: 1

A review on the fabrication technology of X-ray reflector x射线反射器制造技术综述

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-08-31 DOI: 10.12086/OEE.2020.200205

Li Ming, Wu Jieli, Wu Yongqian, Xu Yan, Zhang Dongni, Hong Zhen, Yang Fugui, W. Yongjian

引用次数: 3

超大口径弯月镜支撑点布局-刚度-校正力联合优化超大口径弯月镜支撑点布局-刚度-校正力联合优化

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-08-31 DOI: 10.12086/OEE.2020.190551

习兴华, 张超杰, 胡海飞, 关英俊

{"title":"超大口径弯月镜支撑点布局-刚度-校正力联合优化","authors":"习兴华, 张超杰, 胡海飞, 关英俊","doi":"10.12086/OEE.2020.190551","DOIUrl":"https://doi.org/10.12086/OEE.2020.190551","url":null,"abstract":"Passive hydraulic support units (PHSUs) are frequently used in the in-situ fabrication and testing (in-situ support). However, the difference in PHSUs' stiffness will affect the mirror surface figure, especially for those thin meniscus mirrors. In order to solve this problem, the joint optimization method of layout, stiffness and active correction is studied. Firstly, for the difference of PHUS' stiffness, a hierarchical layout optimization method for support stiffness and support position is proposed to obtain the initial optimization solution of the support system. Then, the mode calibration method and the least square method is used for active correction of support system to obtain the final optimized solution of the mirror surface figure. Finally, the effectiveness of the method is verified by a numerical simulation experiment with specific cases. The results show that, for 4 m thin meniscus mirror, after layout optimization, with the hydraulic passive support system, the root mean square (RMS) of the mirror surface errors of 60 point axial support system is reduced from 150.6 nm to 32.9 nm, and the RMS value of the mirror surface errors of 78 point axial support system is reduced from 45.2 nm to 22.6 nm. The optimization effect is remarkable. After active correction, the RMS value of the mirror surface errors of 60 point axial support system is 14.6 nm, and it is 6.9 nm for 78 point axial support system. The requirement of the RMS value of the mirror surface error is less than λ/40 (λ=632.8 nm). The support systems meet the requirement. Finally, the 60 point axial support system was selected. Through the joint optimization of layout, stiffness and active correction for supporting points, it can greatly increase the applicability, flexibility and reduce the difficulty of implementation for the in-situ support system.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88060406","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

Residual stress measurement methods of optics 光学残余应力测量方法

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-08-31 DOI: 10.12086/OEE.2020.190068

Xiao Shilei, Li Bincheng

引用次数: 0

Progress on ultra precision manufacturing technology of large-aperture high-power laser optics 大口径大功率激光光学器件超精密制造技术研究进展

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-08-31 DOI: 10.12086/OEE.2020.200135

F. Fei, Xu Xi, X. Qiao, Wang Jian, Zhong Bo, Xie Ruiqing, L. Xiangyang, Chen Xianhua, Wang Shengfei, Hou Jing, Deng Wenhui, An Chenhui, Zhou Lian, Zhao Shijie, Li Defeng, Zhu Yujie

引用次数: 4

Despeckling for side-scan sonar images based on adaptive block-matching and 3D filtering 基于自适应块匹配和三维滤波的侧扫声纳图像去斑

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-07-30 DOI: 10.12086/OEE.2020.190580

Chen Peng, Cai Xuanwei, Zhao Dongdong, Liang Ronghua, Guo Xinxin

{"title":"Despeckling for side-scan sonar images based on adaptive block-matching and 3D filtering","authors":"Chen Peng, Cai Xuanwei, Zhao Dongdong, Liang Ronghua, Guo Xinxin","doi":"10.12086/OEE.2020.190580","DOIUrl":"https://doi.org/10.12086/OEE.2020.190580","url":null,"abstract":"Side-scan sonar (SSS) is an electronic device that utilizes the propagation characteristics of sound waves under water to complete underwater detection. Because the SSS produces images and maps according to the intensity of acoustic echo, speckle noise will be inevitably involved. A speckle denoising method based on block-matching and 3D filtering (BM3D) is proposed to filter the multiplicative speckle noise in SSS images. First, the SSS image is transformed by power and logarithm. The wavelet transform is used to estimate the general noisy level of the polluted image. Second, the parameters of the BM3D algorithm are updated according to the noise estimation results of each local patch. At last, after comparing the general noise estimation and the local noise estimation, the proposed algorithm chooses the best estimation to filter every patch separately to solve the problem that the noise is not evenly distributed. The experimental results show that the improved BM3D algorithm can effectively reduce the speckle noise in SSS images and obtain good visual effects. The Equivalent Number of Looks of the proposed algorithm is at least 6.83% higher, the Speckle Suppression Index is lower than traditional algorithm, and the Speckle Suppression and Mean Preservation Index is reduced by at least 3.30%. This method is mainly used for sonar image noise reduction, and has certain practical values for ultrasonic, radar or OCT images polluted by speckle noise.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87698813","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

Adaptive multi-filter tracker based on efficient convolution operator 基于高效卷积算子的自适应多滤波器跟踪器

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-07-30 DOI: 10.12086/OEE.2020.190510

Liu Guoyou, Zhang Fengxv, Jiao Zhian

引用次数: 0

A Hierarchical method for quick and automatic recognition of sunspots 一种快速自动识别太阳黑子的分层方法

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-07-30 DOI: 10.12086/OEE.2020.190342

Zhao Ziliang, Liu Jiazhen, Hu Zhen, Jia Yanhao, Wang Yue, Liang Qingwei, Zhao Zeyang, Liu Yangyi

{"title":"A Hierarchical method for quick and automatic recognition of sunspots","authors":"Zhao Ziliang, Liu Jiazhen, Hu Zhen, Jia Yanhao, Wang Yue, Liang Qingwei, Zhao Zeyang, Liu Yangyi","doi":"10.12086/OEE.2020.190342","DOIUrl":"https://doi.org/10.12086/OEE.2020.190342","url":null,"abstract":"The observation and recognition of sunspots is an important task of solar physics. By observing and analyzing sunspots, solar physicists are able to analyze and predict solar activities with higher accuracy. With the con-tinuous progress of observation instruments, solar full-disk image data amount is also on a rapid growth. In order to recognize and label sunspots quickly and accurately, a two-layer sunspot recognition model is proposed in this paper. The first layer model is based on deep learning model YOLO. In order to enhance the ability of YOLO to recognize small sunspots, the parameters of YOLO are optimized by using the k-means algorithm based on intersection-over-union. The final YOLO model can identify most large sunspots and sunspot groups, with only a few isolated small sunspots being unidentified. For the purpose of further improving recognition rate of small sunspots, the second layer model applies AGAST feature detection algorithm to specifically identify the missing small sunspots. The experimental results on SDO/HMI sunspot data set show that all kinds of sunspots can be recognized effectively with high recognition accuracy by using the model proposed in this paper, thus realizing the real-time sunspot detection task.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73019392","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: 2

An optimized light source layout model for visible light communication system 可见光通信系统光源布局优化模型

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-07-30 DOI: 10.12086/OEE.2020.190565

Liu Hong, Zhai Changxin, Wen Yan-yan, Xinzhuo Lei, Du Zongyu

引用次数: 0

Visual tracking algorithm based on robust PCA 基于鲁棒PCA的视觉跟踪算法

Guangdian Gongcheng/Opto-Electronic Engineering Pub Date : 2020-07-30 DOI: 10.12086/OEE.2020.190278

Yu Chenchen, Hou Zhiqiang, Yu Wangsheng, Pu Lei, Ma Sugang

{"title":"Visual tracking algorithm based on robust PCA","authors":"Yu Chenchen, Hou Zhiqiang, Yu Wangsheng, Pu Lei, Ma Sugang","doi":"10.12086/OEE.2020.190278","DOIUrl":"https://doi.org/10.12086/OEE.2020.190278","url":null,"abstract":"At present, several mainstream algorithms using color name (CN) all adopt Principal Component Analysis (PCA) to process the feature. However, PCA assumes that the noise of input data must obey Gaussian distribution, which is a conspicuous defect. Aim to address this problem, in this paper, we take Robust Principal Component Analysis (Robust PCA) to process CN features. The method projects the original RGB color space to a robust color space–CN space, which means that the input image is stratified to 11 layers according to color name. Then, it processes the CN features by the Robust PCA, so that the mapped image information is concentrated on a few layers, retaining a great quantity of image information and filting out noise. The processed feature is used for Color-tracking frame at the standard benchmark OTB100, and we set up different layers to compare the performance differences of the algorithm. The experimental results show that the success rate increases by 1.0% and the accuracy increases by 0.9% at OTB100. The result illustrates that the Robust PCA method can better bring color name feature superiority into full play and improve the performance of the algorithm effectively.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78788651","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