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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Research on Restraining Low Frequency Oscillation with Flywheel Energy Storage system

Research on Restraining Low Frequency Oscillation with Flywh...

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International Conference on Power system Technology (POWERCON)

作者： Yun Zhong Jiancheng Zhang Gengyin Li Zhiyuan Chen School of Information Engineering University of Nanchang Baoding Jiangxi China Department of Electrical power Engineering North China Electric Power University Baoding Hebei China Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education North China Electric Power University Baoding Hebei China Baoding Zhongli Power Science and Technology Development Company Limited Baoding Hebei China

Low frequency oscillations in the interconnected power systems are observed all around the world. Besides conventional PSS to suppress Low frequency oscillation, there are other modern FACTS devices to damp oscillation, among which flywheel energy storage system (FESS) is a comparative valid way. The theory on restraining low frequency oscillation used by flywheel energy storage system is described through transfer function diagram and complex torque coefficient. A numerical model of flywheel energy storage asynchronous motor controlled by VVVF inverter is derived from its mathematical model in alpha-beta coordinate system. Then the total model eigenvalue is analyzed. Based on analysis, attach control strategy using two parallel independent parts for active power control and reactive power control is designed in FESS to restrain low frequency oscillation. The calculation result shows that flywheel energy storage system is an effective way to restrain low frequency oscillation and enhance power system dynamic performance.

关键词： Frequency Flywheels Energy storage Power system interconnection Transfer functions Torque Numerical models Inverters Mathematical model Power system modeling

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A Service-Environment-Oriented Shape Reconstruction Method of Vehicle-borne Active Phased Array Antenna

A Service-Environment-Oriented Shape Reconstruction Method o...

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IEEE International Conference on Radar

作者： Pengying Xu Xiaoxian Xu Yan Wang Yucheng Teng Congsi Wang Dongming Ge Mingkui Kang Shuai Yuan Xuezi Wang Key Laboratory of Electronic Equipment Structure Design Ministry of Education Xidian University Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Beijing Institute of Spacecraft System Engineering Beijing China No.771 Institute of China Aerospace Science and Technology Corporation Xi'an China China Electronics Technology Group Corporation No.38 Research Institute Hefei China

ISBN: (纸本)9781665468893

Modern electronic equipment has been moving towards large scale and increased complexity. Affected by such factors as complex working environments, the performance of a large number of structures in engineering is degraded or even destroyed. Therefore, structure shape estimation is of great significance. The complexity of the structure, however, makes it impossible for a single deformation evaluation algorithm to evaluate the structure comprehensively and accurately. In this paper, a modal-approximate curve (M-AC) method, using a small amount of strain data, is proposed for block shape estimation of structures. Three typical wind load experiments are carried out on the finite element model (FEM) of the vehicle-borne active phased array antenna, and the effectiveness of the algorithm is verified. Under the wind speed of 28 (m/s), the deformation of antenna front is 104.70 mm and the estimation accuracy of the M-AC method is 87.43%, which is 16.47% higher than that of the modal approach. The results indicate that the estimated displacement is consistent well with the simulation results of the finite element method.

关键词： Phased arrays Shape Estimation Radar Radar antennas Finite element analysis Loaded antennas

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Jseun: Joint Semantic-Instance Segmentation of Point Cloud Via Neighbor Features for Indoor Scenes

SSRN

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SSRN 2023年

作者： Chen, Hui Jiang, Jinlong Chen, Wanlou Liu, Wanquan Liu, Peter Xiaoping College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Nanovision Medical Technology Co. Ltd Shanghai200000 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Efficient and accurate segmentation of the point cloud for indoor scenes is an important task for 3D scene understanding. Due to the complexity of indoor environments. It is difficult to fully extract the details of point cloud, resulting in poor recognizability in existing models. In this paper, a novel framework called joint semantic-instance segmentation via enhancedutilization of neighbor features is proposed to achieve semantic and instance segmentation of indoor scenes. This framework mines point cloud information from a local scale, but it is different from other models in that it focuses on the full mining of point cloud neighbor information. In this regard, a new neighbor feature extraction module is developed to enrich the expression of neighborhood information and enlarge the construction of the neighborhood neighbor scope. Besides, semantic and instance segmentations take advantages of each other by constructing a mutual promotion model of semantic and instance, which can obtain more accurate semantic and instance predictions for indoor scenes. The proposed scheme is evaluated on the S3DIS dataset and compared with existing methods. Experimental results demonstrate that this method is more effective and efficient than previous approaches in joint 3D semantic and instance segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator system

Switched High-Gain Based Interval Observer Design for Harmon...

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Chinese control Conference (CCC)

作者： Tianhang Ming Xiaoling Wang Bo Liu Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing China College of Information Engineering Minzu University of China Beijing P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results.

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End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization

arXiv

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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

arXiv

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arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams

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A new method for measuring complex relative permittivity of dielectric material by reflection method

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Journal of Physics: Conference Series 2021年第1期1871卷

作者： Yao Qin Bin Wei Shuo Liu Mingxing Li Chengxin Cai Tiangang Hou College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China

In this paper, a new lossless free space method for measuring the complex relative permittivity of dielectric material by reflection method is presented. When this method is used to measure the complex relative permittivity of dielectric material, there is no multi-value problem. The measurement system used in this method consists of a horn lens antenna and a vector network analyzer(VNA). In this paper, HFSS microwave simulation software is used to simulate the complex relative permittivity measurement method proposed in this paper. The simulation results show that this method can accurately measure the complex relative permittivity of dielectric material.

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Numerical simulation study of free space method for measuring sample permittivity

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Journal of Physics: Conference Series 2021年第1期1871卷

作者： Yao Qin Shuo Liu Bin Wei Mingxing Li Chengxin Cai Tiangang Hou College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou 450001 China

The composition and performance of the material can be expressed by the permittivity. The paper introduces the measurement principle of free space method and TRL calibration principle, the calculation formula of the sample permittivity is theoretically analysed, a horn lens antenna is designed to simulate the emission of plane waves, and numerical simulation software is used for simulation and optimization. In the working frequency range, the voltage standing wave ratio of the antenna is less than 1.49, and the maximum gain is 21.8dB. A free space measurement system is established in the software to measure the sample permittivity with different thicknesses, and compare the calculated value with the actual value. The results show that the root mean square error of the real part is less than 5.3%, and the root mean square error of the imaginary part is less than 1.3%. The correctness and applicability of the formula for calculating the permittivity are also proved.

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