Journals >Optics and Precision Engineering
Contents
2024
Volume: 32 Issue 5
12 Article(s)
Export citation format
[in Chinese]
Review on precision and agile imaging control technology of optical remote sensing satellites
Youyang QU, Xing ZHONG, Lu DAI, Lindong FAN, and Kai XU
From the perspective of control， this paper comprehensively examines the cutting-edge technology and advancements in the precision and agile control systems for high-resolution optical remote sensing satellites， characterized by their ultra-stability， ultra-accuracy， and ultra-agility， to meet the demands of multifunctFrom the perspective of control， this paper comprehensively examines the cutting-edge technology and advancements in the precision and agile control systems for high-resolution optical remote sensing satellites， characterized by their ultra-stability， ultra-accuracy， and ultra-agility， to meet the demands of multifunctional and high-quality imaging. It explores the link between satellite imaging capabilities and the performance of attitude control systems， highlighting the critical role of precision and agile control technologies in enhancing optical remote sensing satellite imaging through three dimensions： the capability for multifunctional imaging through attitude maneuvering， the impact of attitude determination accuracy on imaging quality， and the influence of attitude control precision on imaging quality. Building on this foundation， and considering the structure and principles of optical remote sensing satellites' attitude control systems， the paper delves into the primary factors that affect attitude control precision and maneuverability. Subsequently， it thoroughly reviews and summarizes three pivotal technical areas： high-precision attitude determination， precision attitude control， and agile attitude control. Concluding with an outlook on the current technological advancements， the paper forecasts future trends in precision and agile control technologies， centered around the concept of integrated imaging control design， offering a theoretical framework and practical guidance for achieving multifunctional and high-quality imaging control in high-resolution optical remote sensing satellites..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 678 (2024)
Integrated platform for micro-vibration simulation and active-passive vibration isolation
Chao QIN, Honghai ZHOU, Shuai HE, Zhenbang XU, and He ZHU
To meet the stringent micro-vibration experiment requirements for remote sensing satellites on the ground, a comprehensive experimental platform has been developed. This platform integrates micro⁃vibration simulation with both active and passive vibration isolation capabilities. It evaluates the active and passive vibrTo meet the stringent micro-vibration experiment requirements for remote sensing satellites on the ground, a comprehensive experimental platform has been developed. This platform integrates micro⁃vibration simulation with both active and passive vibration isolation capabilities. It evaluates the active and passive vibration isolation effectiveness and the micro⁃vibration simulation's impact separately. Passive isolation is achieved through air-floating support, while active isolation employs an active damping technique. A control strategy based on the linear system's frequency response function is utilized for micro-vibration simulation. The results indicate that the platform's first six modal frequencies are below 10 Hz. The passive isolation system effectively reduces ground micro-vibrations within the 10-200 Hz range, and the active isolation system attenuates the vibration isolation system's resonance peak by 14 dB. The micro-vibration simulation can accurately produce single and multi-frequency disturbances that closely mimic actual satellite conditions. During specific spectrum simulation experiments, the maximum amplitude error was 5.90%, which falls within the acceptable error margin. This versatile integrated experimental platform fulfills all the experimental requirements..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 694 (2024)
Five-axis CNC machining method for eyeglass lens edge profile
Hongtao LI, Huazhong XIANG, Zexi ZHENG, Qihui DING... and Songlin ZHUANG|Show fewer author(s)
To enhance the eyeglass lens manufacturing process and mitigate issues stemming from abrupt changes in tool axis vectors during lens edge processing, which could degrade surface quality, this study introduces a five-axis numerical control machining path strategy using UG NX (Unigraphics NX). Initially, an intelligent fTo enhance the eyeglass lens manufacturing process and mitigate issues stemming from abrupt changes in tool axis vectors during lens edge processing, which could degrade surface quality, this study introduces a five-axis numerical control machining path strategy using UG NX (Unigraphics NX). Initially, an intelligent frame scanner captures the eyeglass frame edges, generating point cloud data of the notches. A program is then crafted to convert the spherical coordinate data from OMA files, adhering to eyeglass lens processing standards, into the required coordinates and formats. Furthermore, employing the NURBS curve fitting algorithm enables continuous tool path trajectories, ensuring curve fitting accuracy. The NC (numerical control) machine tool, fitted with a specialized conical milling cutter for lens edge processing, thus improves the precision and efficiency of lens production. In addition, a technique is developed to ensure the tool axis vector changes smoothly, utilizing curve-driven tool axis motion and normal vector constraints of the lens surface for steady tool feeding. The tool path file undergoes post-processing to be converted into NC code. Upon manufacturing, lens error measurement reveals that the proposed method, focusing on curve fitting and tool axis control, significantly enhances machining surface accuracy and smoothness, reduces the manufacturing timeline, and boosts machining efficiency..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 704 (2024)
[in Chinese]
High-precision angle measurement based on two-dimensional hybrid position encoding
Huining ZHAO, Jie WEN, and Haojie XIA
For high-end manufacturing equipment or precision measurement instruments featuring precision rotating shafting, traditional circular grating struggles to negate the impact of eccentricity error on angle measurement accuracy. This paper introduces a highly precise method for measuring rotation angles utilizing two-dimeFor high-end manufacturing equipment or precision measurement instruments featuring precision rotating shafting, traditional circular grating struggles to negate the impact of eccentricity error on angle measurement accuracy. This paper introduces a highly precise method for measuring rotation angles utilizing two-dimensional hybrid position coding. Initially, the system comprises a two-dimensional hybrid position coding disk, two CCD cameras, and a telecentric lens. This assembly is attached to a precision rotating shaft to capture its rotation angle. Subsequently, an angle measurement model is developed, mathematically verifying that measurement accuracy remains unaffected by installation eccentricity. The method's accuracy is validated using a multi-tooth indexing table, confirming that angle measurement error is confined to within ±1". Furthermore, when applied to assess the angle positioning accuracy of a direct drive turntable, the method achieves an angle positioning error within ±5". Unlike traditional circular grating measurements, this approach eliminates the need to account for the impact of eccentricity error on accuracy, offering robustness and simplicity in application. Thus, it proves effective for detecting angle positioning errors..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 635 (2024)
Fabrications and characteristics of Ga-doped ZnO microrods ultraviolet photodetectors
Zhaolin YUAN, Yongwei WU, Luyao YU, Jianfeng HE... and Pengfei LU|Show fewer author(s)
To achieve high⁃performance, low⁃cost zinc oxide (ZnO)⁃based ultraviolet photodetectors, utilizing Ga⁃doped ZnO (ZnO:Ga) as the photosensitive layer is key. This study synthesized ZnO:Ga microrods with varying Ga doping concentrations (0% [undoped ZnO], 0.5%, 1%, 2%, and 4%) using a straightforward hydrothermal method.To achieve high⁃performance, low⁃cost zinc oxide (ZnO)⁃based ultraviolet photodetectors, utilizing Ga⁃doped ZnO (ZnO:Ga) as the photosensitive layer is key. This study synthesized ZnO:Ga microrods with varying Ga doping concentrations (0% [undoped ZnO], 0.5%, 1%, 2%, and 4%) using a straightforward hydrothermal method. The atomic ratios of Ga to Zn were meticulously adjusted. Initial analyses revealed that all samples possessed the hexagonal wurtzite ZnO structure, as confirmed by X⁃ray diffractometry (XRD). Scanning electron microscopy (SEM) showed that the microrods maintained a consistent rod⁃like morphology. Subsequently, these microrods were applied to fluorine⁃doped tin oxide (FTO) glass substrates with interdigital patterns to construct five ultraviolet photodetectors. Their performance was thoroughly evaluated, demonstrating that all devices efficiently responded to 365 nm light. Notably, the photodetector with 1% Ga⁃doped ZnO microrods achieved superior performance, delivering a responsivity of 13.13 A/W, a gain of 44.63, and a specific detectivity of 3.31×1012 Jones at 365 nm. Its response and decay times were recorded at 12.3 s and 36.4 s, respectively. These findings suggest that an optimal Ga concentration can significantly enhance the performance of ZnO⁃based ultraviolet photodetectors. This research contributes valuable insights for the development of advanced ultraviolet photodetectors and related devices utilizing ZnO:Ga materials..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 643 (2024)
Preparation and performance of self-powered Ag/Bi2O3 nanoblocks ultraviolet photodetector
Xiangming FANG, Qicheng ZHOU, Yu SUN, Zhiming QIAO... and Shiyong GAO|Show fewer author(s)
To effectively detect ultraviolet （UV） light without an external power source， a self-powered UV photodetector （UVPD） was developed using Ag/Bi2O3 nanoblocks. Initially， Bi2O3 nanoblocks were prepared through a calcination process， followed by the successful synthesis of Ag/Bi2O3 nanoblocks by depositing Ag nanoparticlTo effectively detect ultraviolet （UV） light without an external power source， a self-powered UV photodetector （UVPD） was developed using Ag/Bi2O3 nanoblocks. Initially， Bi2O3 nanoblocks were prepared through a calcination process， followed by the successful synthesis of Ag/Bi2O3 nanoblocks by depositing Ag nanoparticles on their surface via a solution reaction method at room temperature. The crystal structure and microstructures of the samples were analyzed， revealing that the Ag/Bi2O3 nanoblocks had an average diameter of approximately 1 μm， with Ag nanoparticles randomly distributed on the surface of the Bi2O3 nanoblocks. The Ag/Bi2O3 nanoblocks were then coated on FTO as the working electrode， leading to the construction of the self-powered UVPD. Upon exposure to UV light （365 nm）， the Ag/Bi2O3 nanoblocks UVPD quickly detected UV light at zero bias voltage， showcasing its self-powered capability. Compared to the Bi2O3 nanoblocks UVPD， the Ag/Bi2O3 nanoblocks UVPD displayed significantly improved photocurrents， with rise and decay times reduced to 29.1 ms and 40.2 ms， respectively， and demonstrated excellent cycling stability..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 653 (2024)
Acoustic-vibration intelligent detection of flexible shallow buried objects
Chi WANG, Peng CAO, Qing HUANG, Chao WANG, and Cailiang SHENG
A novel sound-vibration detection approach， leveraging a target detection algorithm， merges acoustic stimulation， laser speckle interferometry， and target detection algorithms for efficient and broad-range detection of flexible， shallowly buried objects. Initially， after discussing the YOLO series target detection algoA novel sound-vibration detection approach， leveraging a target detection algorithm， merges acoustic stimulation， laser speckle interferometry， and target detection algorithms for efficient and broad-range detection of flexible， shallowly buried objects. Initially， after discussing the YOLO series target detection algorithm principles， an optimal intelligent detection network model for these objects is chosen. Subsequently， a sound-light fusion intelligent detection system is developed， creating a dataset of laser speckle interference patterns for various flexible， shallowly buried objects. This dataset is then trained and tested to evaluate the algorithm's effectiveness in recognizing interference patterns. Experimental outcomes reveal that， under specified conditions， the model achieves a 98.39% accuracy rate， an 84.72% recall rate， and an average recognition accuracy of 99.66%. This sound-vibration detection method effectively identifies laser speckle interference patterns of numerous flexible， shallowly buried objects in the tested environment， proving its efficacy for quick， large-scale detection of such objects underground..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 661 (2024)
Online laser absorption spectroscopy detection of trace ethylene in coal pyrolysis
Zheng DUAN, Xingxing MENG, Kailiang LI, Xiaocong SUN... and Tingdong CAI|Show fewer author(s)
To achieve the online identification and detection of trace ethylene gas during coal pyrolysis, we constructed a vinyl gas concentration detection system using tunable diode laser absorption spectroscopy (TDLAS). This system combines wavelength modulation with long optical path technology. We employed a distributed feeTo achieve the online identification and detection of trace ethylene gas during coal pyrolysis, we constructed a vinyl gas concentration detection system using tunable diode laser absorption spectroscopy (TDLAS). This system combines wavelength modulation with long optical path technology. We employed a distributed feedback (DFB) laser with a central wavelength of 1 620 nm, situated in the communication band, and a 15⁃meter long optical path cell for sample absorption. The SR830 was used for wavelength demodulation, enabling us to determine ethylene concentrations through second harmonic signal inversion. We used high⁃precision flow controllers to dilute ethylene with high⁃purity nitrogen gas, creating standard ethylene gas samples with concentrations ranging from 10×10-6 to 90×10-6, achieving a linear fitting correlation coefficient R2 of 0.998 9. An Allan variance analysis experiment on a 20ppm ethylene sample over 4 000 s determined the minimum detection limit to be 121×10-9. To examine the evolution of ethylene concentration in various gas environments during coal pyrolysis, we maintained a gas flow rate at 150 mL/min. We analyzed the online release process of identified ethylene gas in nitrogen, air, and synthetic air (22% oxygen, 78% nitrogen) environments. Our findings revealed that below 500 ℃, ethylene release in all three environments was low and consistent. Between 500 to 700 ℃, ethylene release in a nitrogen environment was notably higher than in the other two gases, with the release rate in air being the fastest, peaking at about 40ppm. Above 700 ℃, ethylene release in all environments began to decline. These results provide a critical scientific basis for further exploration into the mechanism of ethylene generation during coal pyrolysis, the refinement of coal pyrolysis techniques, the enhancement of coal utilization efficiency, and the advancement of environmental preservation..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 670 (2024)
[in Chinese]
DRT Net： dual Res-Transformer pneumonia recognition model oriented to feature enhancement
Tao ZHOU, Caiyue PENG, Yuhu DU, Pei DANG... and Huiling LU|Show fewer author(s)
Deep learning for lung X-ray image recognition has emerged as a prominent research area. The challenge lies in the small， complexly shaped lesion areas within lung X-rays， where the boundary between the lesion and normal tissue is often unclear， complicating feature extraction in pneumonia images.This paper introduces Deep learning for lung X-ray image recognition has emerged as a prominent research area. The challenge lies in the small， complexly shaped lesion areas within lung X-rays， where the boundary between the lesion and normal tissue is often unclear， complicating feature extraction in pneumonia images.This paper introduces a Dual Res-Transformer pneumonia recognition model focused on feature enhancement. It incorporates three feature enhancement strategies to augment the model's feature extraction capabilities. The model's key components include： the Group Attention Dual Residual Module （GADRM）， which leverages a dual-residual structure for effective feature fusion and enhances local feature extraction through channel shuffle， channel attention， and spatial attention； the Global-Local Feature Extraction Module （GLFEM）， which applies at the network's higher levels， merging CNN and Transformer benefits to extract comprehensive global and local image features， thereby boosting the network's semantic feature extraction； and the Cross-layer Dual Attention Feature Fusion Module （CDAFFM）， designed to merge shallow network spatial information with deep network channel information， enhancing the network's cross-layer feature extraction.The model's efficacy was validated through ablation and comparative experiments on the COVID-19 CHEST X-RAY dataset. Results demonstrate the network's high performance， with accuracy， precision， recall， F1 score， and AUC values of 98.41%， 94.42%， 94.20%， 94.26%， and 99.65%， respectively.This model offers significant assistance to radiologists in diagnosing various pneumonia cases using chest X-rays， marking a crucial advancement in computer-aided pneumonia diagnosis..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 714 (2024)
Position-sensitive Transformer aerial image object detection model
Daxiang LI, Jiani XIN, and Ying LIU
Addressing the challenge of detecting numerous small objects in UAV⁃captured aerial images, this paper introduces the Position⁃Sensitive Transformer Target Detection (PS⁃TOD) model. Initially, it presents a multi⁃scale feature fusion (MSFF) module incorporating a Positional Channel Embedded 3D Attention (PCE3DA) mechanAddressing the challenge of detecting numerous small objects in UAV⁃captured aerial images, this paper introduces the Position⁃Sensitive Transformer Target Detection (PS⁃TOD) model. Initially, it presents a multi⁃scale feature fusion (MSFF) module incorporating a Positional Channel Embedded 3D Attention (PCE3DA) mechanism. PCE3DA leverages the interplay between spatial and channel data to generate 3D attention, enhancing feature representation in areas of interest. This foundation supports a bottom⁃up, cross⁃layer MSFF approach, augmenting the semantic richness of combined features. Subsequently, it proposes a novel Position⁃Sensitive Self⁃Attention (PSSA) mechanism, leading to the development of a position⁃sensitive Transformer encoder⁃decoder. This innovation heightens the model's sensitivity to target positioning, facilitating the capture of long⁃term dependencies within the image's global context. Comparative tests using the VisDrone dataset reveal that the PS⁃TOD model attains an Average Precision (AP) of 28.8%, marking a 4.1% enhancement over the baseline model (DETR). Furthermore, it demonstrates precise object detection in UAV aerial imagery against complex backdrops, significantly boosting the detection accuracy of small targets..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 727 (2024)
Multispectral image fusion method for surface defect detection of IC devices
Yaohua DENG, and Zhihai HUANG
To address the issue of low defect detection accuracy in IC devices due to insufficient contrast under either visible light or infrared conditions alone, this paper introduces a multi-spectral fusion approach. Initially, to overcome scale inconsistency and contrast inversion challenges during IC device image registratiTo address the issue of low defect detection accuracy in IC devices due to insufficient contrast under either visible light or infrared conditions alone, this paper introduces a multi-spectral fusion approach. Initially, to overcome scale inconsistency and contrast inversion challenges during IC device image registration, we enhance the ORB (Oriented FAST and Rotated BRIEF) algorithm with a Laplacian pyramid and feature descriptor recombination strategy. Following image registration, we propose the NSST_VP image fusion method, which processes the infrared and visible images' low and high frequency subbands through Non-Subsample Shearlet Transform (NSST). For fusion, the low frequency subband uses a visual significance map (VSM) weighted rule, and the high frequency subband employs a PA-Pulse Coupled Neural Network (PA-PCNN) decision rule, with the final image produced by reversing the NSST. The fused image is then analyzed using the YOLOv8s model. Experimental findings reveal an 87.8% average accuracy with the improved ORB registration, marking a 62% enhancement over the standard ORB. The NSST_VP fusion algorithm significantly boosts both subjective and objective metrics, achieving an mAP of 83.15%-surpassing single light mode detections by 22.97% and 28.31%, and outperforming Dual-Tree Complex Wavelet, Non-Subsampled Contourlet, and Curvelet Transform fusion methods by 13.14%, 15.01%, and 20.35%, respectively..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 740 (2024)
Binocular vision SLAM with fused point and line features in weak texture environment
Kun GONG, Xin XU, Xiaoqing CHEN, Yuelei XU, and Zhaoxiang ZHANG
Addressing the challenge of trajectory drift in visual Simultaneous Localization and Mapping (SLAM) due to point features in texture-deficient indoor settings, this study introduces a binocular visual SLAM system that combines point and line features. It emphasizes the extraction and matching of line features within biAddressing the challenge of trajectory drift in visual Simultaneous Localization and Mapping (SLAM) due to point features in texture-deficient indoor settings, this study introduces a binocular visual SLAM system that combines point and line features. It emphasizes the extraction and matching of line features within binocular visual SLAM. An enhanced line feature extraction technique, based on the Line Segment Detector (LSD) algorithm, is proposed. This includes improvements like length and gradient filtering, and the amalgamation of short lines. Additionally, the matching issue is redefined as an optimization challenge, creating a cost function based on geometric constraints. A novel, efficient line segment triangulation approach, leveraging the L1-norm sparse solution, is developed for effective line matching and triangulation. Experimental evidence shows that our method surpasses traditional descriptor-based approaches across various datasets, especially in texture-sparse indoor areas, achieving a remarkable average matching accuracy of 91.67% and a swift average matching time of 7.4 ms. Employing this technique, our binocular visual SLAM system records positioning errors of 1.24, 7.49, and 3.67 m on texture-sparse datasets, outperforming leading algorithms like ORBSLAM2 and PL-SLAM in positioning precision..
Optics and Precision Engineering
- Publication Date: Mar. 10, 2024
- Vol. 32, Issue 5, 752 (2024)
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20