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arXiv 2020年

作者： Pan, Shi-Jie Wan, Lin-Chun Liu, Hai-Ling Wang, Qing-Le Qin, Su-Juan Wen, Qiao-Yan Gao, Fei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Dimensionality reduction (DR) algorithms, which reduce the dimensionality of a given data set while preserving the information of original data set as well as possible, play an important role in machine learning and data mining. Duan et al. proposed a quantum version of A-optimal projection algorithm (AOP) for dimensionality reduction [Phys. Rev. A 99, 032311 (2019)] and claimed that the algorithm has exponential speedups on the dimensionality of the original feature space n and the dimensionality of the reduced feature space k over the classical algorithm. In this paper, we correct the complexity of Duan et al.’s algorithm to O(κ4sǫ√sks polylogs(mnǫ )), where κ is the condition number of a matrix that related to the original data set, s is the number of iterations, m is the number of data points and ǫ is the desired precision of the output state. Since the complexity has an exponential dependence on s, the quantum algorithm can only be beneficial for high dimensional problems with a small number of iterations s. To_ get a further speedup, we proposed an improved quantum AOP algorithm with complexity O(sκ6ǫ√kpolylog(nmǫ )+ s2ǫκ4 polylog(κkǫ )). Our algorithm achieves a nearly exponential speedup if s is large and a polynomial speedup even if s is a small constant compared with Duan et al.’s algorithm. Also, our algorithm shows exponential speedups in n and m compared with the classical algorithm when both κ, k and 1/ǫ are O(polylog(nm)). Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

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Fast ORB-SLAM without Keypoint Descriptors

arXiv

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

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A Mobile Ar End-to-End Integrated Registration Method for Intelligent Assisted Assembly of Rail Transit Equipment

SSRN

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SSRN 2024年

作者： Yong, Jiu Wei, Jianguo Lei, Xiaomei Wang, Yangping Dang, Jianwu Lu, Wenhuan College of Intelligence and Computing Tianjin University Tianjin300072 China The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong Univeristy Lanzhou730070 China The School of Electronic and Information Engineering Lanzhou Jiaotong Univeristy Lanzhou730070 China Meteorological Information and Technological Supporting Center Gansu Province Meteorological Bureau Lanzhou730020 China

To improve the efficiency of rail transit equipment assembly operations, augmented reality (AR) technology is used to integrate rich virtual auxiliary guidance information into the assembly process, establish a virtual-real fusion rail transit assembly operation environment, and strengthen the perception and understanding ability of assembly business personnel to their assembly environments. In this paper, an AR end-to-end integration registration network (AR-EIRNet) is proposed for mobile AR intelligent assisted assembly of rail transit equipment. This network includes three main stages: feature extraction, pose estimation, and registration parameter solving. In the feature extraction stage, multiscale features are extracted based on a feature attention mechanism and local contextual information. In the pose estimation stage, the pose of the assembly object is directly inferred based on the aggregation of multiscale features and global features, and the pose is calibrated using a differentiable renderer. The registration parameter solving stage calculates the AR registration alignment matrix integrate virtual information overlay rendering. In addition, AR-EIRNet utilizes RGB image synthesis enhancement strategies to increase data diversity and enhance the learning of AR assembly objects. Finally, assembly application and utility analyses are conducted on the ZD6 switch machine equipment in rail transit. The results indicate that the registration error of the AR assembly is within 5 mm, and AR-EIRNet has strong stability and robustness, which can effectively improve the assembly operation efficiency. © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Joint Face Alignment and 3D Face Reconstruction with Efficie...

引用

International Conference on Pattern Recognition

作者： Keqiang Li Huaiyu Wu Xiuqin Shang Zhen Shen Gang Xiong Xisong Dong Bin Hu Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. Model parameters and complexity of our method are also improved greatly.

关键词： Training Solid modeling Three-dimensional displays Convolution Face recognition Neural networks Complexity theory

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese Automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.

关键词： Automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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Effect of model parameter errors in model predictive control applications

Effect of model parameter errors in model predictive control...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Diego Rojas Marco Rivera Javier Muñoz Patrick Wheeler Pericle Zanchetta Galina Mirzaeva Estudiante del Doctorado en Sistemas de Ingeniería Facultad de Ingeniería Universidad de Talca Curicó Chile Pertenecen a la Facultad de Ingeniería Universidad de Talca Curicó Chile Pertenecen al Power Electronics Machines and Control Group The University of Nottingham Nottingham UK Pertenece a la School of Electrical Engineering and Computing The University of Newcastle Newcastle Australia

ISBN: (纸本)9781665408745

When implementing predictive control strategies for power electronic converters a good model of the system is needed because this model determines the quality of the resulting controller. In this paper a study of the model parameter error effects in predictive control applications is presented. The results show that by considering a short sampling time, there can be a significant impact if there are variations in the resistance and inductance values.

关键词： Resistance Inductance Conferences Predictive models Inverters Information and communication technology Harmonic distortion

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Performance Analysis of Multi-RIS-Aided LoRa Systems With Outdated and Imperfect CSI

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IEEE Transactions on Communications 2024年

作者： Liang, Zhaokun Cai, Guofa He, Jiguang Kaddoum, Georges Huang, Chongwen Guangdong University of Technology School of Information Engineering China Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control Chongqing University of Posts and Telecommunications Chongqing400065 China Great Bay University School of Computing and Information Technology China University of Oulu Centre for Wireless Communications Oulu90014 Finland University of Quebec Electrical Engineering Department École de Technologie Supérieure MontrealQCH2L 2C4 Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

Although LoRa has emerged as the leading technology among the rapidly developing low-power wide-area networks, the performance of the LoRa system severely deteriorates over fading channels. To address this problem, in this paper, we introduce multiple reconfigurable intelligent surfaces (multi-RISs) into the LoRa system to improve its performance. Our specific focus is on the impact of outdated channel state information (CSI), the imperfection of estimated CSI, and the design of RIS discrete phase shifts on the performance. To this end, we first use the moment-matching method to obtain the end-to-end (E2E) channel coefficient of the joint outdated channels and erroneous channels over Nakagami-m fading. Moreover, the closed-form bit error rates (BERs) of the proposed system with non-coherent and coherent detections are derived. The results reveal that, in the high signal-to-noise ratio (SNR) regime, coherent detection encounters the error floor and performs worse than non-coherent detection. Furthermore, we also analyze delay outage rate, throughput, and achievable diversity order of the proposed system. The results show that, despite the presence of outdated CSI and channel estimation errors, the proposed system is still superior to RIS-aided LoRa systems adopting blind transmission and RIS-free ones. Finally, we also thoroughly investigate the effects of various important factors such as the correlation factor, channel estimation errors, the number of RIS reflecting elements, and the number of quantization bits for RIS discrete phase shifts on the performance. © 1972-2012 IEEE.

关键词： Fading channels

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A Resilient Decentralized Learning Approach against Byzantine Attack via Reputation Evaluation

A Resilient Decentralized Learning Approach against Byzantin...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Heng Zhang Wenting Wang Xuguo Jiao Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan P. R. China College of Control Science and Engineering Zhejiang University Hangzhou China State Grid Shandong Electric Power Research Institute Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.

关键词： Training Learning systems Accuracy Network topology Computational modeling Machine learning Classification algorithms

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Performance Analysis of Cooperative Wireless-Powered NOMA System With Energy Buffer

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2024年

作者： Xie, Kengyuan Cai, Guofa Ning, Ting He, Jiguang Kaddoum, Georges Guangdong University of Technology School of Information Engineering Guangzhou510006 China Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China Great Bay University School of Computing and Information Technology China University of Oulu Centre for Wireless Communications Oulu90014 Finland University of Quebec Electrical Engineering Department École de Technologie Supérieure MontrealQCH2L 2C4 Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Lebanon

This paper proposes an energy buffer aided cooperative wireless-powered non-orthogonal multiple access (NOMA) system, which includes a base station (BS), a near user (NU), and a far user (FU). To consider a more general case and improve the system performance as much as possible, both the NU and FU are equipped with energy buffers. Two different energy management policies are considered at the NU and FU, namely the best-effort policy (BEP) and on-off policy (OOP). In the proposed system, the NU and FU first collect and store radio frequency based energy from the BS. Then, the FU uses the stored energy to transmit its signal to both the BS and NU. The NU decodes the received signal from the FU and uses the stored energy to transmit the superimposed signal that consists of its own signal and the decoded FU signal to the BS. The outage probability and throughput expressions of the proposed system with BEP and OOP are derived over Rayleigh fading channels. Results reveal that the proposed system can significantly improve the performance of FU compared to the energy buffer aided non-cooperative wireless-powered NOMA system, and can outperform the buffer-less cooperative wireless-powered NOMA system. © 2017 IEEE.

关键词： Fading channels

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