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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Wang, Changqing Liang, Xinkai Zhang, Shan Shen, Chao Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

By making full use of the curvature of the earth and ground clutter, the ultra-low altitude flight technique can significantly increase the difficulty of detecting and intercepting unmanned aerial vehicles (UAVs), and improve their penetration probabilities. However, unpredicted static/dynamic obstacles seriously threaten the flight safety of the ultra-low altitude UAVs. Therefore, fine autonomous spatial perception capability is essential for ultra-low altitude UAVs with limited loads and computing resources to avoid obstacles. To solve the difficult problem of spatial perception for ultra-low altitude obstacle avoidance, a motion parallax estimation algorithm is proposed by combing the deep learning and the epipolar geometry in this paper. By utilizing the deep neural network, the end-to-end mapping relationship from sequence images to depth values of pixels is constructed for spatial perception. At the same time, to overcome the interference caused by the image consistency hypothesis, the loss function is constructed based on the 3D geometric information of the scene rather than the re-projection error between images. Finally, the proposed algorithm is evaluated on the KITTI dataset and infrared image dataset to verify its effectiveness and generalization. © 2019 IEEE.

关键词： Deep neural networks

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Two points are enough

arXiv

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arXiv 2024年

作者： Liu, Hao Zhao, Yanbin Zheng, Huarong Fan, Xiulin Deng, Zhihua Chen, Mengchi Wang, Xingkai Liu, Zhiyang Lu, Jianguo Chen, Jian State Key Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Ocean College Zhejiang University Hangzhou China State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Hangzhou China Nanyang Technological University Singapore School of Control Science and Engineering Zhejiang University Hangzhou China School of Materials Science and Engineering Tianjin University Tianjin China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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A Multi-branch Parallel Simulation Method based on Symbiotic Potential

A Multi-branch Parallel Simulation Method based on Symbiotic...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhao, Lu Zhen-Biao, Tu Jia-Ning, Wei Yu-Tong, Zhu Tong-Lin, Liu Qing-Hua, Liu Beijing Electro-Mechanical Engineering Insititute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

A multi-branch parallel simulation method and an architecture of application system are proposed in this paper based on symbiotic potential, and method of construction and calibration of parallel battlefield based on real data, as well as generation of simulation branch based on situation interpretation etc. are proposed. Fast building and adjustment of situation of real time simulation are realized based on simulation to meet the requirements of consistency and parallel operation between virtual simulation and real systems. Multibranch operation management, multi-branch highperformance operation support and other methods are proposed to support the dynamic generation of massive parallel simulation copies based on real-time intelligence and ultra-realtime and large-sample operation to meet the requirements of advanced evaluation of real-time situation. At the same time, a set of simulation application verification frameworks for typical task situation is presented to support the construction of multibranch simulation operation environment, and provide technical verification means for real-time situation prediction and evaluation. © 2019 IEEE.

关键词： Real time systems

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High-performance chiral all-optical logic gate based on topological edge states of valley photonic crystal

arXiv

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arXiv 2022年

作者： Wang, Xiaorong Fei, Hongming Lin, Han Wu, Min Kang, Lijuan Zhang, Mingda Liu, Xin Yang, Yibiao Xiao, Liantuan College of Physics Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China School of Science RMIT University MelbourneVIC3000 Australia Swinburne University of Technology HawthornVIC3122 Australia

For all-optical communication and information processing, it is necessary to develop all-optical logic gates based on photonic structures that can directly perform logic operations. All-optical logic gates have been demonstrated based on conventional waveguides and interferometry, as well as photonic crystal structures. Nonetheless, any defects in those structures will introduce high scattering loss, which compromises the fidelity and contrast ratio of the information process. Based on the spin-valley locking effect that can achieve defect-immune unidirectional transmission of topological edge states in valley photonic crystals (VPCs), we propose a high-performance all-optical logic OR gate based on a VPC structure. By tuning the working bandwidth of the two input channels, we prevent interference between the two channels to achieve a stable and high-fidelity output. The transmittance of both channels is higher than 0.8, and a high contrast ratio of 28.8 dB is achieved. Moreover, the chirality of the logic gate originated from the spin-valley locking effect allows using different circularly polarized light as inputs, representing "1" or "0", which is highly desired in quantum computing. The device's footprint is 18 × 12 μm 2, allowing high-density on-chip integration. In addition, this design can be experimentally fabricated using current nanofabrication techniques and will have potential applications in optical communication, information processing, and quantum computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Logic gates

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Parameter adaptive terminal sliding mode control of flexible coupling air-breathing hypersonic vehicle

Parameter adaptive terminal sliding mode control of flexible...

引用

AIAA Scitech Forum, 2020

作者： Jin, Hengxian Chen, Haibing Lin, Wei Xu, Cheng School of Energy And Power Engineering Beihang University Beijing100191 China Airworthiness Technologies Research Center School of Transportation Science and Engineering Beihang University Beijing100191 China School of Transportation Science and Engineering Beihang University Beijing100191 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Flexible air-breathing hypersonic vehicles have very wide range of flight and show significant and complex aerodynamic, scramjet propulsion system, flight dynamic and elastic coupling characteristics, which usually are highly nonlinear. These result in huge uncertainties, parameter perturbation and internal disturbance, which can no longer be ignored, and therefore, extreme difficulties for flight control design. A unique parameter adaptive nonsingular terminal sliding mode method is proposed for longitudinal control law design of a flexible coupling air-breathing hypersonic vehicle. Other than conventional adaptive terminal sliding mode control, this method uses adaptive reaching law gain instead of additional adaptive compensation term to handle the uncertainty to improve robustness. The stability of the close loop system is proved via a Lyapunov way. The longitudinal tracking control law for velocity and angle of attack is designed based on a rigid dynamic model of a flexible air-breathing hypersonic vehicle. A strong coupling model of this vehicle, considering aerodynamic-scramjet engine-flight dynamic-elastic couplings, is established as the verification platform of control law designed. The remarkable differences of flight dynamics characteristics between this strong coupling model and the rigid body model can be seen, which mean the controller needs to endure very great uncertainty, unmodeled dynamics and other internal disturbance. Simulation based on the coupling model is carried on and results demonstrate that the control law designed has good performance and acceptable robustness. © 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hypersonic vehicles

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Achieving Peta-Ohm Resistance for Semi-Insulating 4H-SiC Devices by Atomic Layer Deposition

arXiv

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arXiv 2024年

作者： Xi, Yuying Li, Helios Y. Li, Guohui Su, Qingmei Mao, Kaili Xu, Bingshe Hao, Yuying Fang, Nicholas X. Cui, Yanxia College of Electronic Information and Optical Engineering Key Lab of Interface Science and Engineering in Advanced Materials Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan030024 China Department of Mechanical Engineering University of Hong Kong Hong Kong999077 Hong Kong School of Materials Science and Engineering Materials Institute of Atomic and Molecular Science Shaanxi University of Science and Technology Xi’an710021 China Shanxi Semisic Crystal Co. Ltd. Taiyuan030000 China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan030032 China

Growing demands for precise current measurements, such as atto-ampere-level measurement of cross-cellular biological current transduction, have spotlighted a pressing need for low-noise resistors with ultra-high resistance immune to voltage fluctuations. Traditional semi-insulating materials, however, struggle to provide consistent resistance across varying voltages. To bridge this gap, we introduce a design that integrates semi-insulating 4H-SiC with atomic-level metal oxide interlayers and electrodes. The strategic adjustment of surface states via atomic-scale metal oxide layers optimizes the work functions on 4H-SiC surfaces, validated through density functional theory simulations. This design transcends conventional limitations, establishing an ideal Ohmic behavior and maintains Peta-Ohm-level resistance, unaffected by voltage variations. These on-chip devices with fine-tuned resistance are compatible with integrated circuit manufacturing processes, making them ideally suited for applications in precision electronics. © 2024, CC BY-NC-ND.

关键词： Silicon carbide

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Robust cubature Kalman filter based on generalized hyperbolic distribution for SLAM under colored heavy-tailed measurement noise

引用

Digital Signal Processing: A Review Journal 2025年 166卷

作者： Zhao, Jiaxiang Wang, Guoqing School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

The effectiveness of existing filter-based simultaneous localization and mapping (SLAM) algorithms will deteriorate under non-Gaussian measurement noise, especially when dealing with the colored heavy-tailed characteristics. In this paper, we present a novel robust cubature Kalman filter based on the generalized hyperbolic distribution for SLAM under colored heavy-tailed measurement noise. Within the proposed algorithm, the measurement differencing method is adopted to whiten the colored noise, and subsequently, the additive heavy-tailed noise is modeled by the generalized hyperbolic distribution, which contains several typical heavy-tailed distributions as special cases. We utilize the variational Bayesian inference method to jointly estimate the system state together with the measurement noise parameters, and the one-step smoothing estimation method is utilized to estimate the state vector at the previous moment to enhance the estimation accuracy. To address the varying number of landmark points observed at different moments, we perform sequential processing during the measurement update process, and this method also reduces the computational complexity. The proposed algorithm creates a flexible framework for the state estimation of nonlinear systems under colored and variable heavy-tailed noise, with several existing algorithms serving as special cases. The superior performance of the proposed algorithm in terms of estimation accuracy and robustness is validated through simulations and experiments. © 2025 Elsevier Inc.

关键词： Higher order statistics

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fMRI-based Brain Disease Diagnosis: A Graph Network Approach

TechRxiv

引用

TechRxiv 2023年

作者： Chen, Qi-Qi Gao, Yubing Tong, Wei Li, Ping Wu, Edmond Q. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Nanjing University of Science and Technology Jiangsu210008 China The School of Biological Science and Medical Engineering Beihang University Beijing100191 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Deep Learning-Based Dimension Measurement system for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

引用

Chinese Automation Congress (CAC)

作者： Xiao-Di Li Xing-Rong Fan Xian-Ming Zhang Han-Zhi Zhang Chun-Jie Huang Cheng Xu Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) - yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm.

关键词： Shafts Image segmentation Accuracy Measurement uncertainty Noise reduction Manuals Size measurement Stability analysis Robustness Standards

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Cross-cycle iterative unmanned aerial vehicle reentry guidance based on reinforcement learning

Cross-cycle iterative unmanned aerial vehicle reentry guidan...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Cheng, Yang Shui, Zunshi Xu, Cheng Feng, Tianyu Jiang, Yiyang Beijing Aerospace Technology Research Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance. © 2019 IEEE.

关键词： Reinforcement learning

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