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Strategy and control for Robotic Detumbling of Space Debris by Using Flexible Brush 3

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Strategy and Control for Robotic Detumbling of Space Debris ...

3rd IEEE International Conference on Robotics and Automation Sciences, ICRAS 2019

作者： Cheng, Wenhua Li, Zhi He, Yingzi Department of Graduate Management Space Engineering University Beijing China Department of Space Command Space Engineering University Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781728108537

Active space debris removal (ASDR) is an effective and reliable mean to keep space exploration and space environment. But space debris is always with complex attitude, including spinning and nutation, due to space environmental torque, and causing trouble in ASDR. This paper focuses on the strategy and control for robotic detumbling of space debris by using flexible brush. First, the model of detumbling-robot and flexible brush, which acts as the end-effecter, are proposed. On this basis, the contact force model, including the friction, is derived based on mechanics of materials and Coulomb friction. Subsequently, the motion and dynamic equations between robot and debris are formulated. With detumbling strategy, the control law is presented. Simulation results are appended. The results show that it is difficult to ensure long-term contact between the detumbling-robot and debris because of the contact elastic force without control force. Instead, the angular rate of debris would be detumbled to desired value after several times contact, and the energy which used up during detumbling is also in reasonable span. The tumbling debris could be detumbled by flexible brush with the strategy and control law stated above. © 2019 IEEE.

关键词： Friction

Toward a direct measurement of the cosmic acceleration: the first preparation with FAST

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arXiv 2021年

作者： Lu, Chang-Zhi Jiao, Kang Zhang, Tingting Zhang, Tong-Jie Zhu, Ming Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China College of Command and Control Engineering PLA Army Engineering University Nanjing210000 China Institute for Astronomical Science Dezhou University Dezhou253023 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China

Damped Lyman-α Absorber(DLA) of HI 21cm system is an ideal probe to directly measure cosmic acceleration in real-time cosmology via Sandage-Loeb(SL) test. During short observations toward two DLAs in the commissioning progress of FAST, we manage to exhibit an HI 21cm absorption feature from PKS1413+135 spectrum in one epoch with our highest resolution up to 100 Hz, preliminarily validating the frequency consistency under different resolutions and bandwidths. We make a Gaussian fitting to extract the spectral features, introduce two theoretical indicators to describe the fitted velocity uncertainty, and ultimately give a mean redshift and its constraint of zM = 0.24670045 ± 0.00000036 in accord with most literature. But our redshift error of the target is still three magnitudes higher than the level we can reach the drift signal. Though our first preparation has some flaws in time recording and diode settings, it still proves the correctness of our data process. Confined by limited observing time, we do not strech FAST’s ability to obtain a better velocity constraint, so further researchs are needed and in schedule. With fine sensitivity and improving spectral resolution, such observations in FAST could have reasonable possibility to explore cosmic acceleration in late time universe practically. Copyright © 2021, The Authors. All rights reserved.

关键词： Cosmology

Learning Coated Adversarial Camouflages for Object Detectors

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arXiv 2021年

作者： Duan, Yexin Chen, Jialin Zhou, Xingyu Zou, Junhua He, Zhengyun Zhang, Jin Zhang, Wu Pan, Zhisong Department of Watercraft Power Army Military Transportation University of PLA Zhenjiang China College of Command and Control Engineering Army Engineering University of PLA Nanjing China The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Communication Engineering College Army Engineering University of PLA Nanjing China Railway Transportation College Hunan University of Technology Zhuzhou China

An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpoint changes. To remedy this issue, this work proposes the Coated Adversarial Camouflage (CAC) to attack the detectors in arbitrary viewpoints. Unlike the patch trained in the 2D space, our camouflage generated by a conceptually different training framework consists of 3D rendering and dense proposals attack. Specifically, we make the camouflage perform 3D spatial transformations according to the pose changes of the object. Based on the multi-view rendering results, the top-n proposals of the region proposal network are fixed, and all the classifications in the fixed dense proposals are attacked simultaneously to output errors. In addition, we build a virtual 3D scene to fairly and reproducibly evaluate different attacks. Extensive experiments demonstrate the superiority of CAC over the existing attacks, and it shows impressive performance both in the virtual scene and the real world. This poses a potential threat to the security-critical computer vision systems. © 2021, CC BY-NC-SA.

关键词： Object detection

The strength of nesterov’s extrapolation in the individual convergence of nonsmooth optimization

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TechRxiv

TechRxiv 2020年

作者： Tao, Qing Tao, Wei Pan, Zhisong Wu, Gaowei Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China Institute of Automation Chinese Academy of Sciences China Army Academy Of Artillery and Air Defense Hefei230031 China

The extrapolation strategy raised by Nesterov, which can accelerate the convergence rate of gradient descent methods by orders of magnitude when dealing with smooth convex objective, has led to tremendous success in training machine learning tasks. In this article, the convergence of individual iterates of projected subgradient (PSG) methods for nonsmooth convex optimization problems is theoretically studied based on Nesterov’s extrapolation, which we name individual convergence. We prove that Nesterov’s extrapolation has the strength to make the individual convergence of PSG optimal for nonsmooth problems. In light of this consideration, a direct modification of the subgradient evaluation suffices to achieve optimal individual convergence for strongly convex problems, which can be regarded as making an interesting step toward the open question about stochastic gradient descent (SGD) posed by Shamir. Furthermore, we give an extension of the derived algorithms to solve regularized learning tasks with nonsmooth losses in stochastic settings. Compared with other state-of-the-art nonsmooth methods, the derived algorithms can serve as an alternative to the basic SGD especially in coping with machine learning problems, where an individual output is needed to guarantee the regularization structure while keeping an optimal rate of convergence. Typically, our method is applicable as an efficient tool for solving large-scale l1-regularized hinge-loss learning problems. Several comparison experiments demonstrate that our individual output not only achieves an optimal convergence rate but also guarantees better sparsity than the averaged solution. © 2020, CC BY.

关键词： Extrapolation

A Reliable Calibration of HII Galaxies Hubble Diagram with Cosmic Chronometers and Artificial Neural Network

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arXiv 2022年

作者： Zhang, Jian-Chen Jiao, Kang Zhang, Tingting Zhang, Tong-Jie Yu, Bo Institute for Frontiers in Astronomy Astrophysics of Beijing Normal University Beijing100875 China Department of Astronomy Beijing Normal University Beijing100875 China College of Computer and Information Engineering Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 BolognaI-40129 Italy College of Command and Control Engineering PLA Army Engineering University Nanjing210000 China School of Mathematics and Big Data Dezhou University Dezhou253023 China Shandong Provincial Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China

The L − σ relation of HII galaxies (HIIGx) calibrated by a distance indicator is a reliable standard candle for measuring the Hubble constant H0. The most straightforward calibration technique anchors them with the first tier of distance ladders from the same galaxies. Recently another promising method that uses the cosmological model-independent Cosmic Chronometers (CC) as a calibrator has been proposed. We promote this technique by removing the assumptions about the cosmic flatness and using a non-parametric Artificial Neural Network for the data reconstruction process. We observe a correlation between the cosmic curvature density parameter and the slope of the L−σ relation, thereby improving the reliability of the calibration. Using the calibrated HIIGx Hubble diagram, we obtain a Type Ia Supernovae Hubble diagram free of the conventional assumption about H0. Finally we get a value of H0 = 65.9+3.0−2.9 km s−1 Mpc−1, which is compatible with latest Planck18 measurement. © 2022, CC BY.

关键词： Supernovae

A new analytical approximation of luminosity distance by optimal HPM-Padé technique

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arXiv 2021年

作者： Yu, Bo Zhang, Jian-Chen Zhang, Tong-Jie Zhang, Tingting School of Mathematics and Big Data Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China Shandong Provincial Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China School of Computer and Information Dezhou University Dezhou253023 China Department of Astronomy Beijing Normal University Beijing100875 China School of Command and Control Engineering Army Engineering University Nanjing210017 China

By the use of homotopy perturbation method-Padé (HPM-Padé) technique, a new analytical approximation of luminosity distance in the flat universe is proposed, which has the advantage of significant improvement for accuracy in approximating luminosity distance over cosmological redshift range within 0 ≤ z ≤ 2.5. Then we confront the analytical expression of luminosity distance that is obtained by our new approach with the observational data, for the purpose of checking whether it works well. In order to probe the robustness of the proposed method, we also confront it to supernova type Ia and recent data on the Hubble expansion rate H(z). Markov Chain Monte Carlo (MCMC) code emcee is used in the data fitting. The result indicates that it works fairly *** Codes 32E30 Copyright © 2021, The Authors. All rights reserved.

关键词： Luminance

Application of Intelligent Algorithms in UAV Geomagnetic Navigation Modeling

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Application of Intelligent Algorithms in UAV Geomagnetic Nav...

2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Huang, Jingli Mei, Dan Su, Feng Zhang, Xiangyu Naval Aeronautical University Department of Aeronautical Electrical Control Engineering and Command Qingdao China Naval Aeronautical University Qingdao Branch Qingdao China Naval Aeronautical University Institute of Information Fusion Yantai China

ISBN: (纸本)9781728137926

Rectangular harmonic model is a common model in UAV geomagnetic navigation. In order to reduce the navigational error caused by the model factors and achieve high-precision navigation, the original Earth's main magnetic field model is replaced by rectangular harmonic model. In creating a rectangular harmonic model, inertia weight adaptive particle swarm algorithm is adopted to solve the problem that too many undetermined coefficients can easily fall into local optimum in the central region. The ability of generalization to inhibit the boundary effect of radial basis function neural network is used in the border region. Meanwhile, the design of the error box to a more reasonable distinction between center and boundary is designed. The simulation results show that the optimized combination of the two intelligent algorithm makes the accuracy of the model further higher. © 2019 IEEE.

关键词： Errors

Adversarial examples with transferred camouflage style for object detection

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Xiaotong Deng Zheng Fang Yunfei Zheng Yang Wang Junming Huang Xiaobing Wang Tieyong Cao Institute of Command and Control Engineering Army Engineering University Nanjing Jiangsu 210000 China PLA Army Academy of Artillery and Air Defense Nanjing Jiangsu 210000 China

Most of existing adversarial examples attacking methods for object detection models aim at generating subtle perturbation which is invisible to human vision. However, some perturbations with stronger intensity are equally effective and can even make the objects in adversarial examples more invisible. In this paper, an attack model based on the Generative Adversarial Network and style transfer method is designed to craft and add perturbation with camouflage style to the object area in the image. Experiments on PASCAL VOC 2012 dataset demonstrated that Adversarial examples generated from our model can attack both proposal-based detectors and regression-based object detection models effectively, and reduce the visual saliency of the objects.

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Likelihood-free cosmological constraints with artificial neural networks: An application on hubble parameters and SNe Ia

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arXiv 2020年

作者： Wang, Yu-Chen Xie, Yuan-Bo Zhang, Tong-Jie Huang, Hui-Chao Zhang, Tingting Liu, Kun Department of Physics Beijing Normal University Beijing100875 China Department of Astronomy Beijing Normal University Beijing100875 China Institute for Astronomical Science Dezhou University Dezhou253023 China College of Command and Control Engineering PLA Army Engineering University Nanjing China

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat ΛCDM model. For SNe Ia, we use fitted light curve parameters to find constraints on H0,Ωm,ΩΛ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks