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Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Science China(Information Sciences) 2021年第5期64卷 177-192页

作者： Zhijie LIU Zhiji HAN Zhijia ZHAO Wei HE Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Institute of Artificial IntelligenceUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing School of Mechanical and Electrical Engineering Guangzhou University

In this paper, we address simultaneous control of a flexible spacecraft's attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures. Using Hamilton's principle, the system dynamics is modeled as an infinite dimensional system captured using partial differential equations. Moreover, a novel adaptive fault tolerant control strategy is developed to suppress the vibrations of the flexible panel in the course of the attitude stabilization. To determine whether the system energies,angular velocities and transverse deflections, remain bounded and asymptotically decay to zero in the case wherein the number of actuator failures is infinite, a Lyapunov-based stability analysis is conducted. Finally,extensive numerical simulations are performed to demonstrate the performance of the proposed adaptive control strategy.

关键词： adaptive control actuator failures infinite-dimensional systems flexible spacecraft fault tolerant control

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Wang Juan Wang Haodong Bian Maozu Guo School of Computer Science Inner Mongolia University and Ecological Big Data Engineering Research Center of the Ministry of Education Hohhot China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing P.R. China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing P.R. China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.

关键词： Proteins Location awareness Support vector machines Deep learning Pathology Predictive models Feature extraction

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Back-n white neutron source at CSNS and its applications

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Nuclear Science and Techniques 2021年第1期32卷 95-104页

作者： Jing-Yu Tang Qi An Jiang-Bo Bai Jie Bao Yu Bao Ping Cao Hao-Lei Chen Qi-Ping Chen Yong-Hao Chen Zhen Chen Zeng-Qi Cui Rui-Rui Fan Chang-Qing Feng Ke-Qing Gao Xiao-Long Gao Min-Hao Gu Chang-Cai Han Zi-Jie Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Han-Xiong Huang Xi-Ru Huang Hao-Yu Jiang Wei Jiang Zhi-Jie Jiang Han-Tao Jing Ling Kang Bo Li Chao Li Jia-Wen Li Qiang Li Xiao Li Yang Li Jie Liu Rong Liu Shu-Bin Liu Xing-Yan Liu Ze Long Guang-Yuan Luan Chang-Jun Ning Meng-Chen Niu Bin-Bin Qi Jie Ren Zhi-Zhou Ren Xi-Chao Ruan Zhao-Hui Song Kang Sun Zhi-Jia Sun Zhi-Xin Tan Xin-Yi Tang Bin-Bin Tian Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Zhong-Wei Wen Xiao-Guang Wu Xuan Wu Li-Kun Xie Xiao-Yun Yang Yi-Wei Yang Han Yi Li Yu Tao Yu Yong-Ji Yu Guo-Hui Zhang Lin-Hao Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Lu-Ping Zhou Zhi-Hao Zhou Ke-Jun Zhu 无 State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China The CSNS Back-n Collaboration

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source(CSNS)that emit through the incoming proton channel were exploited to build a white neutron beam facility(the so-called Back-n white neutron source),which was completed in March *** Back-n neutron beam is very intense,at approximately 29107 n/cm2/s at 55 m from the target,and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the *** addition,the excellent energy spectrum spanning from 0.5 eV to 200 MeV,and a good time resolution related tothe time-of-flight measurements make it a typical white neutron source for nuclear data measurements;its overall performance is among that of the best white neutron sources in the *** with advanced spectrometers,detectors,and application utilities,the Back-n facility can serve wide applications,with a focus on neutron-induced cross-sectional *** article presents an overview of the neutron beam characteristics,the experimental setups,and the ongoing applications at Backn.

关键词： White neutron source Nuclear data measurements Experimental setups Neutron applications

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Detector development at the Back-n white neutron source

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Radiation Detection Technology and Methods 2023年第2期7卷 171-191页

作者： Fan Ruirui Li Qiang Bao Jie Li Yang Liu Rong Jiang Wei Ren Jie Zhang Qiwei Cao Ping Gu Minhao Ren Zhizhou Yi Han Tang Jingyu An Qi Bai Haofan Bai Jiangbo Chen Qiping Chen Yonghao Chen Zhen Cui Zengqi Fan Anchuan Feng Changqing Feng Fanzhen Gao Keqing Han Changcai Han Zijie He Guozhu He Yongcheng Hong Yang Hu Yiwei Huang Hanxiong Jia Weihua Jiang Haoyu Jiang Zhijie Jin Zhengyao Kang Ling Li Bo Li Chao Li Gong Li Jiawen Li Xiao Liu Jie Liu Shubin Luan Guangyuan Ning Changjun Qi Binbin Ruan Xichao Song Zhaohui Sun Kang Tan Zhixin Tang Shengda Wang Pengcheng Wang Zhaohui Wen Zhongwei Wu Xiaoguang Wu Xuan Xie Likun Yang Yiwei Yu Yongji Zhang Guohui Zhang Linhao Zhang Mohan Zhang Xianpeng Zhang Yuliang Zhang Yue Zhang Zhiyong Zhao Maoyuan Zhou Luping Zhou Zhihao Zhu Kejun The CSNS Back-n Collaboration Institute of High Energy Physics CASBeijing 100049People’s Republic of China Spallation Neutron Source Science Center Dongguan 523803People’s Republic of China State Key Laboratory of Particle Detection and Electronics Beijing 100049People’s Republic of China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413People’s Republic of China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900People’s Republic of China Department of Modern Physics University of Science and Technology of ChinaHefei 230026People’s Republic of China State Key Laboratory of Particle Detection and Electronics Hefei 230026People’s Republic of China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871People’s Republic of China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026People’s Republic of China Northwest Institute of Nuclear Technology Xi’an 710024People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China 不详

Purpose Back-n is a white neutron beamline at China spallation neutron source,which was established in the year of *** is a powerful facility for nuclear data measurement,neutron detector calibration,and radiation effect *** A series of detectors were built for different experiments,including beam monitoring,beam profile measurement,neutron induced secondaries(fission fragments,light charged particles and gamma)cross section measurement,and neutron resonance radiography,etc.A common digitization electronics and a cluster-based DAQ were developed for these detector *** detectors have been employed at Back-n and serviced for experiments from the beginning of the beamline *** and conclusion As an overview of detectors of Back-n,the details of the detector design and the experiment performing are described in this *** developing systems,e.g.,MTPC and B-MCP,are also included.

关键词： White neutron source Beam diagnostics Fission fragment Charged particle Gamma Neutron resonance radiography

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Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off

arXiv

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arXiv 2023年

作者： Fu, Min Mei, Weidong Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies. © 2023, CC BY.

关键词： Location

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On the noise robustness of in-context learning for text generation 24

On the noise robustness of in-context learning for text gene...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hongfu Gao Feipeng Zhang Wenyu Jiang Jun Shu Feng Zheng Hongxin Wei Department of Statistics and Data Science Southern University of Science and Technology and School of Economics and Finance Xi'an Jiaotong University School of Economics and Finance Xi'an Jiaotong University Department of Statistics and Data Science Southern University of Science and Technology and National Key Laboratory for Novel Software Technology Nanjing University School of Mathematics and Statistics Xi'an Jiaotong University Department of Computer Science and Engineering Southern University of Science and Technology Department of Statistics and Data Science Southern University of Science and Technology

ISBN: (纸本)9798331314385

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking

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Channel Estimation and Signal Detection for NLOS Ultraviolet Scattering Communication with Space Division Multiple Access

arXiv

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arXiv 2023年

作者： Zhang, Yubo Pan, Yuchen Gong, Chen Liu, Beiyuan Xu, Zhengyuan Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences University of Science and Technology of China Anhui Hefei230027 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Shaanxi Xi’an710072 China

We design a receiver assembling several photomultipliers (PMTs) as an array to increase the field of view (FOV) of the receiver and adapt to multiuser situation over None-line-of-sight (NLOS) ultraviolet (UV) channels. Channel estimation and signal detection have been investigated according to the space division characteristics of the structure. Firstly, we adopt the balanced structure on the pilot matrix, analyze the channel estimation mean square error (MSE), and optimize the structure parameters. Then, with the estimated parameters, an analytical threshold detection rule is proposed as a preliminary work of multiuser detection. The detection rule can be optimized by analyzing the separability of two users based on the Gaussian approximation of Poisson weighted sum. To assess the effect of imperfect estimation, the sensitivity analysis of channel estimation error on two-user signal detection is performed. Moreover, we propose a successive elimination method for on-off keying (OOK) modulated multiuser symbol detection based on the previous threshold detection rule. A closed-form upper bound on the detection error rate is calculated, which turns out to be a good approximation of that of multiuser maximum-likelihood (ML) detection. The proposed successive elimination method is twenty times faster than the ML detection with negligible detection error rate degradation. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

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Research on Production Condition Identification Method of Pumping Well Based on an Improved Pattern Recognition Algorithm

Research on Production Condition Identification Method of Pu...

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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Haili Wu Xiangyang Zhang Dan Wu Qun Li Gang Yao Key Laboratory of Internet of Things CNPC Petroleum and Petrochemical Research Center of National Engineering Laboratory of Big Data Collaborative Security CNPC Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China

In the process of oil and gas production, indicator diagram is an important basis for production management. Diagnosing the working condition of pumping unit based on indicator diagram can quickly grasp the working state of oil well, analyze and judge whether the production parameters of oil well are reasonable. The pattern recognition technology of indicator diagram generally includes two stages: feature extraction and classification. Classification methods mainly use a few common machine learning methods such as artificial neural network and SVM. In recent years, feature extraction methods mainly include geometric feature method, grid feature and moment invariant feature methods. Due to the limitations of these extraction methods, most of them can only extract some primary feature quantities. However, using these primary feature quantities to recognize the indicator diagram often has the technical problems of low recognition efficiency and low recognition accuracy. In this paper, an improved indicator diagram pattern recognition method based on binary gradient direction histogram feature is proposed. Compared with the previous feature extraction methods, this method has good robustness, fast calculation speed and high recognition accuracy.

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Robust Weather Recognition in Images via Interactive CNN-Transformer

Robust Weather Recognition in Images via Interactive CNN-Tra...

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Chinese Control Conference (CCC)

作者： Tianyu Liu Qingwen Hou Jie Zhang Xianzhong Chen Jinghui Cheng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China

Weather recognition is a fundamental yet challenging task for the multi -sensor perception system of driverless vehicles. It is difficult for current joint perception framework to autonomously recognize weather scenes and adaptively adjust the weight of camera involved in perception. This paper proposes a weather recognition method with a new interactive CNN - Transformer architecture to learn global and local contextual information from images. The framework starts by deepening and widening the convolution blocks to obtain multi-scale local scene features. We re-weight different feature channels by channel-attention mechanism. Then, a new interactive feature fusion network between convolutional structure and stacked Transformer is developed, which can gradually complement the global feature information lost in CNN. We evaluated the proposed method on a public weather image dataset. The recognition accuracy reaches 95.14%, which improves 5.68% and 6.15% compared with the ResNet-50 and ViT16 and achieves state-of-the-art performance on the experimental dataset. At the same time, the comprehensive evaluation of recall and precision show that the proposed method is more robust when dealing with multi-class weather recognition tasks.

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Improved Active Disturbance Rejection Control of Inverted Pendulum Based on Differential Flatness

Improved Active Disturbance Rejection Control of Inverted Pe...

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Chinese Automation Congress (CAC)

作者： Lijun Wang Jieming Li Xianzhong Chen School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China

Aiming at the control problem of an underdriven inverted pendulum with instability and uncertain external disturbances, a novel improved active disturbance rejection control (ADRC) method based on differential flatness is proposed. Firstly, the non-minimum phase characteristics of the inverted pendulum model are established and analyzed. A flat output is found to overcome the input-output imbalance inherent in underdriven system. Secondly, to address the limitations of the fal function, an improved extended state observer based on a new nonlinear functions is proposed. The theoretical analysis demonstrates the improved performance in terms of tracking speed and accuracy. Furthermore, based on the improved extended state observer, an ADRC method is designed for the flat inverted pendulum system, enabling precise control of the cart displacement and pendulum angle with external disturbances. Finally, simulation results show the effectiveness of the proposed observer and control strategies.

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