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Dynamic Pitch Misalignment Angle Measurement System for Precision Centrifuges

Laser and Optoelectronics Progress 2025年第9期62卷

作者： Liu, Xin Wang, Shanshan Zhang, Peihan He, Dong Hao, Leilei Qi, Yuxiang Hao, Qun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China National Key Laboratory on Near-Surface Detection Beijing100072 China Laboratory of Science and Technology on Ultra-Precision Aerospace Control Instrument Beijing100039 China Beijing Institute of Aerospace Control Devices Beijing100039 China China Aerospace Academy of Electronics Technology Beijing100094 China School of Opto-Electronic Engineering Changchun University of Science and Technology Jilin Changchun130022 China

The measurement system was designed that utilizes the principle of photoelectric autocollimation to detect the dynamic pitch misalignment angle of a precision centrifuge. The system allows for real-time measurement of pitch angle variations at the end of the centrifuge arm, situated outside the centrifuge chamber. An external trigger module determines when the centrifuge reaches the measurement position and generates a trigger signal to initiate the measurement process. Experimental analyses were conducted to evaluate the signal-to-noise ratio of the detector under varying exposure levels, and an automatic dimming module was implemented to mitigate inconsistencies in image gray levels at different centrifuge speeds. Simulation experiments were conducted to analyze measurement errors in the optical path and the effects of environmental temperature fluctuations. Finally, on-site measurements of the dynamic pitch angle were performed at the centrifuge facility, achieving a measurement repeatability of 0. 207″. © 2025 Universitat zu Koln. All rights reserved.

关键词： Centrifuges

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Improved IMM algorithm based on support vector regression for UAV tracking

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Journal of Systems Engineering and Electronics 2022年第4期33卷 867-876页

作者： ZENG Yuan LU Wenbin YU Bo TAO Shifei ZHOU Haosu CHEN Yu Shanghai Spaceflight Electronic and Communication Equipment Research Institute Shanghai 201109China Science and Technology on Near-Surface Detection Laboratory Wuxi 214035China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China

With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

关键词： interacting multiple model(IMM)filter constant acceleration(CA) unmanned aerial vehicle(UAV) support vector regression(SVR)

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3D Object Tracking with Transformer 32

3D Object Tracking with Transformer

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32nd British Machine Vision Conference, BMVC 2021

作者： Cui, Yubo Fang, Zheng Shan, Jiayao Gu, Zuoxu Zhou, Sifan Northeastern University Shenyang China Science and Technology on Near-Surface Detection Laboratory Wuxi China

Feature fusion and similarity computation are two core problems in 3D object tracking, especially for object tracking using sparse and disordered point clouds. Feature fusion could make similarity computing more efficient by including target object information. However, most existing LiDAR-based approaches directly use the extracted point cloud feature to compute similarity while ignoring the attention changes of object regions during tracking. In this paper, we propose a feature fusion network based on transformer architecture. Benefiting from the self-attention mechanism, the transformer encoder captures the inter- and intra- relations among different regions of the point cloud. By using cross-attention, the transformer decoder fuses features and includes more target cues into the current point cloud feature to compute the region attentions, which makes the similarity computing more efficient. Based on this feature fusion network, we propose an end-to-end point cloud object tracking framework, a simple yet effective method for 3D object tracking using point clouds. Comprehensive experimental results on the KITTI dataset show that our method achieves new state-of-the-art performance. Code is available at: https://***/3bobo/lttr. © 2021. The copyright of this document resides with its authors.

关键词： Computer vision

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A Hyperspectral Nonlinear Unmixing Network for nearshore Underwater Target detection 5

A Hyperspectral Nonlinear Unmixing Network for Nearshore Und...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Liu, Jiaxuan Qi, Jiahao Zhong, Ping Jiang, Junhe National University of Defense Technology Key Laboratory of Science and Technology On Automatic Target Recognition Changsha China Science and Technology On Near-Surface Detection Laboratory Wuxi China

ISBN: (纸本)9798350385557

The existing hyperspectral underwater target detection algorithms are ineffective in adapting to nearshore environments, owing to the difficulty of extracting target signals from cluttered nearshore backgrounds combined with weak target signals. To this end, hyperspectral unmixing (HU) is well suited in this paper for nearshore underwater target detection (UTD) task to address these issues, by extracting background endmembers and separating out weak target signals simultaneously. Specifically, we propose a novel nonlinear unmixing network for nearshore underwater target detection (NUN-UTD). It comprises an unsymmetrical autoencoder and a target prior preservation module to adapt to the complex nearshore environment. Experiments demonstrate that NUN-UTD achieves the best detection performances and visual results compared with other state-of-the-art methods. © 2024 IEEE.

关键词： Learning systems

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Short-time and Spectrum Features of Noises Made by Vehicles for Recognition 3

Short-time and Spectrum Features of Noises Made by Vehicles ...

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3rd IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2020

作者： Ding, Kai Zhang, Shigong Zhang, Kesheng Lei, Zhen Science and Technology on Near-Surface Detection Laboratory Wuxi China Guizhou Institute of Technology Guiyang China

ISBN: (数字)9781728198279

ISBN: (纸本)9781728198279

The recognition and classification of moving maneuvering targets play an important role in intelligent vehicle highway system. In this paper, based on short-time and spectrum technologies, some general used features of noises made by vehicles are analyzed. Short-time zero-crossing rate and short-time energy do not show obvious identifiable characteristics for recognition. While, spectrum features of noises made by different vehicles represent obvious characteristics. These features include amplitude frequency spectrum, power spectrum density, and short time amplitude frequency spectrum, Engine speed and vehicle weight can be estimated from the spectrum features. Besides, nonlinear idle engine noise features can also be used in vehicle recognition. With a good theory basis, the voice-print features, such as pitch and formant frequencies, are considered the best methods for recognition, but the two features do not really display the corresponding role. Feature extraction based on spectrum features can provide a powerful theoretical and experimental basis for the next pattern recognition. © 2020 IEEE.

关键词： Maneuverability

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Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

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China Communications 2024年第3期21卷 37-50页

作者： Pan Hao Chen Yu Qi Xiaogang Liu Meili School of Mathematics and Statistics Xidian UniversityXi’an 710126China Science and Technology on Near-Surface Detection Laboratory Wuxi 214035China School of Software Nanchang Hangkong UniversityNanchang 330063China

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much *** placement of access points(APs)significantly influences localization accuracy and network ***,the indoor scenario and network access are not fully considered in previous AP placement optimization *** study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network *** order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction *** that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP ***,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

关键词： indoor localization MOPSO network access RSS prediction

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A Co-offset GPR Data Inversion Based on Ray Theory 9

A Co-offset GPR Data Inversion Based on Ray Theory

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9th International Conference on Environmental and Engineering Geophysics, ICEEG 2020

作者： Xue, Meiqi Liu, Sixin Lu, Qi Li, Hongqing Wang, Yuanxin School of Earth Exploration Science and Technology Jilin University Changchun China Science and Technology on Near-Surface Detection Laboratory Wuxi China

We use Monte Carlo method to establish a randomly undulating homogeneous layered medium model. Then the co-offset GPR data for the built geological model is simulated by FDTD. The ray-tracing based inversion is applied for interval velocity estimation. During the inversion, the offset between antennas, the velocity value of the first layer, the picked amplitudes values of each reflection layer and reference amplitude are the input data. The thickness and velocity of each layer are calculated by this recursive method. It is proved that this inversion method is feasible for the layered geological model with random undulation, and the fact that undulation, i.e. RMS height and correlation length, influences the inversion results is discussed. © Published under licence by IOP Publishing Ltd.

关键词： Monte Carlo methods

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Research on radio imaging method (RIM) multifrequency information fusion methods 9

Research on radio imaging method (RIM) multifrequency inform...

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9th International Conference on Environmental and Engineering Geophysics, ICEEG 2020

作者： Ni, Jianfu Han, Xue Liu, Sixin College of Geo-Exploration Science and Technology Jilin University Changchun China Science and Technology on Near-Surface Detection Laboratory Wuxi China

Radio imaging method (RIM) also called cross-hole electromagnetic wave method, is widely used in mineral exploration, engineering exploration and other fields. The RIM system can collect multi-frequency information, but the use of multi-frequency information is still limited to qualitative analysis or only one type of frequency information is used. By summarizing the multi-frequency information utilization methods appearing in a variety of geophysical methods, and the feasibility of RIM multi-frequency utilization is proved through theoretical analysis, three multi-frequency information fusion methods suitable for RIM are proposed, namely multi-frequency inversion strategies, Multi-frequency data fusion and multi-frequency image fusion. An example application analysis shows that the three multi-frequency information fusion methods make full use of the advantages of different frequencies and can improve the imaging result. © Published under licence by IOP Publishing Ltd.

关键词： Mineral exploration

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A Study on the Relationship between the detection Distance of Borehole Radar and the Resistivity of Background Medium 9

A Study on the Relationship between the Detection Distance o...

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9th International Conference on Environmental and Engineering Geophysics, ICEEG 2020

作者： Yan, Weicun Liu, Sixin College of Geo-exploration Science and Technology Jilin University Changchun China Science and Technology on Near-Surface Detection Laboratory Wuxi China

Borehole radar has been widely used in engineering, exploration and other fields, and the detection distance of borehole radar is an important indicator to measure its performance. The detection distance of borehole radar is affected by many factors, including the radar system, the physical properties of background medium, the borehole environment and so on. In this paper, the relationship between the detection distance of the borehole radar and the resistivity of background medium is studied by numerical simulation. The experimental results show that the detection distance of borehole radar increases with the increase of the resistivity of background medium, and the relationship between them is approximately linear. This kind of simulation is helpful to the theoretical study of the borehole radar. © Published under licence by IOP Publishing Ltd.

关键词： Boreholes

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Generative Adversarial Networks for Exo-atmospheric Infrared Objects Discrimination

Generative Adversarial Networks for Exo-atmospheric Infrared...

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2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Wu, Dongya Lu, Huanzhang Tao, Huamin Zhao, Bendong Zhao, Ming National University of Defense Technology Department of Electronic Science Changsha China Science and Technology on Near-Surface Detection Laboratory Wuxi China

ISBN: (纸本)9781728123455

External atmospheric infrared (IR) target recognition is an important research topic of space surveillance systems. The different micromotion states of the target result in respective features in the obtained sequence of IR radiation intensities, and such differences are difficult to visually describe and difficult to extract efficiently. Due to the difficulty of spatial experiments, the target sample data is very limited. In the case of limited labeled samples, there are few methods that can effectively classify time series effectively, resulting in low classification accuracy and difficulty in meeting practical application requirements. We use generative adversarial networks(GAN) for semi-supervised learning, combining neural network classifiers with adversarial generation models for the classification of infrared grayscale time series of spatial targets. The effective classification of the target is achieved with a small number of labeled target samples. The experiment proves the effectiveness of the method and the classification effect is better than other methods. © 2019 IEEE.

关键词： Generative adversarial networks

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