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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Hybrid6D: A Dual-Stream Transformer-CNN Approach for 6D Object Pose Estimation from RGB-D Images

Hybrid6D: A Dual-Stream Transformer-CNN Approach for 6D Obje...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Fu, Songhao Zhang, Qiang Sun, Xueying Liu, Mingmin Fan, Zhiwei Jiangsu University of Science and Technology College of Automation No. 666 Changhui Road Zhenjiang212100 China Central Research Institute Siasun Robot & Automation CO. Ltd. No. 33 Quanyun Road Hunnan District Shenyang110167 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350325706

Estimating the 6D pose presents a formidable challenge due to the intricate nature of world objects and the myriad of issues encountered when acquiring data from real-world scenes. This task is compounded by occlusions, fluctuating lighting conditions, and data noise. The conventional CNN or Transformer architectures primarily focus on either local or global information, and relying solely on RGB or depth maps proves inadequate for extracting the most robust features. To address these challenges, we introduce Hybird6D, a dual-stream fusion deep learning approach that leverages a Transformer-CNN structure and attention mechanism to regress the 6D pose of objects using RGB-D images as input. Our methodology capitalizes on the strengths of both Transformers and CNNs, incorporating global attention and local attention, while simultaneously harnessing both appearance and geometric information. This comprehensive approach enhances robustness to noise and occlusions. Experimental evaluation on the YCB-Video dataset demonstrates that our method outperforms existing Transformer-based approaches and surpasses advanced CNN-based methods. Moreover, our approach exhibits superior performance on symmetry objects, textureless objects, and small objects. © 2023 IEEE.

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Green Recycling And Regeneration Of Lini0.5co0.2mn0.3o2 From Spent Lithium-Ion Batteries Assisted by Sodium Sulfate Electrolysis

SSRN

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SSRN 2022年

作者： Fang, Jiahu Ding, Zhengping Ling, Yang Li, Junpeng Zhuge, Xiangqun Luo, Zhihong Ren, Yurong Luo, Kun Changzhou Key Laboratory of Intelligent Manufacturing and Advanced Technology for Power Battery School of Materials Science and Engineering Changzhou University Changzhou213164 China Guangxi Key Laboratory of Optical and Electronic Materials and Devices Guilin University of Technology Guilin541004 China

Considering the necessity of green and sustainable recycling of spent lithium-ion batteries (LIBs). Herein, a green closed-loop leaching process based on electrolysis of sodium sulfate solution for recycling spent LIBs is demonstrated. The sulfuric acid and sodium hydroxide obtained by electrolyzing sodium sulfate solution are used as leaching agent. Under the optimal condition of that 2 vol% H2O2, 300 rpm stirring speed, 20 g L-1 pulp density and leaching temperature is 60 ℃, the leaching efficiencies for Ni, Co, Mn, and Li all reached more than 99 %. Spherical LiNi0.5Co0.2Mn0.3O2 particles are synthesized from the recycled Ni, Co, Mn, and Li by the carbonate co-precipitation method. The regenerated LiNi0.5Co0.2Mn0.3O2 has excellent electrochemical performance with the highest discharge specific capacity of 167.041 mAh g-1 in the first cycle, and stable rate performance and cycle performance (it retains 87% initial capacity after 100 cycles). The remained acid-based solution in the recycling process can be neutralized to form sodium sulfate and reused for electrolysis, the high-purity oxygen and hydrogen produced during the electrolysis have high added value. Finally, it is calculated that an economic outcome of $6992 is obtained by recycling one ton spent lithium-ion batteries. © 2022, The Authors. All rights reserved.

关键词： Sodium sulfate

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Lower Limb Trajectory Prediction for Exoskeleton with AutoEncoder-Deep Gaussian Process

Lower Limb Trajectory Prediction for Exoskeleton with AutoEn...

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IEEE Conference on Industrial electronics and Applications (ICIEA)

作者： Pingping Lv Chi Zhang Feng Yi Ting Yuan Shupei Li Meitong Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Gait trajectory prediction is a crucial component in compensating control system delays, reducing interactive resistance, and achieving real-time assistance in exoskeleton robotics. In this paper, an adaptive gait prediction model based on the autoencoder deep Gaussian process (AE-DGP) is proposed for joint angle estimation using measurement data from inertial measurement units (IMUs). The DGP process involves stochastic gradient Hamiltonian Monte Carlo (SGHMC) calculations and Monte Carlo Expectation-maximization (MCEM) optimization at a lower computational cost. The kernel of the model is the multi-feature fusion input based on autoencoder and the construction of the DGP model with an adaptive sliding window. To suppress spikes in predicted deviations, a weighted averaging strategy is employed to estimate future stable gait trajectories. Benefiting from the design, the proposed model can enhance the accuracy and adaptability of gait prediction at different speeds. Experiments involving four participants at four speeds indicate that the proposed method achieves hip and knee joint prediction with less than 0.16° and 0.35° root mean squared errors respectively. Compared to the Gaussian process (GP) and long short-term memory (LSTM) models, the proposed method exhibits superior performance in adaptive gait trajectory prediction at different speeds.

关键词： Adaptation models Monte Carlo methods Computational modeling Exoskeletons Gaussian processes Predictive models Real-time systems

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on electronic Information Engineering and Computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Dual-loop joint torque control based on lower limb exoskeleton robots 18

Dual-loop joint torque control based on lower limb exoskelet...

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18th IEEE Conference on Industrial electronics and Applications, ICIEA 2023

作者： Zhang, Meitong Zhang, Chi Yi, Feng Shu, Xindong Yuan, Ting Li, Zheng Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science and Technology School of Electrical Engineering Shijiazhuang050000 China

ISBN: (纸本)9798350312201

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control. © 2023 IEEE.

关键词： Torque control

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Optimization of Simulation Parameters of Input Shaper Based on Genetic Algorithm 2

Optimization of Simulation Parameters of Input Shaper Based ...

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2nd IEEE International Conference on Advanced Technologies in intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Yi, Tao Pei, Quanshen Li, Deying Wei, Shugong Jia, Danping Zhang, Hualiang Hunan University of Information Changsha China School of Information Science and Engineering Shenyang University of Technology Shenyang China College of Physics and Electronic Engineering Guangxi Normal University for Nationalities Chongzuo China Chinese Academy of Sciences Shenyang Institute of Automation Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (纸本)9781665493963

In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor;Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor;In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation. © 2022 IEEE.

关键词： Genetic algorithms

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基于摩擦伏特效应的超滑表征技术

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Science Bulletin 2024年第9期69卷 1197-1201页

作者：王昭政高凯冯源曹杰宫礼坤范贝贝张毅解国新张弛 CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China State Key Laboratory of Tribology in Advanced Equipment Tsinghua UniversityBeijing 100084China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Electrical&Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Center on Nanoenergy Research School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Institute of Intelligent Flexible Mechatronics Jiangsu UniversityZhenjiang 212013China Institute of New Materials and Advanced Manufacturing Beijing Academy of Science and TechnologyBeijing 100084China

Friction occurs in our daily lives, and statistics have shown that it consumes approximately 1/3 of the primary energy. In addition,mechanical interface friction significantly impacts the efficiency,noise, accuracy, corrosion, reliability, and operational lifespans of equipment. Several studies have revealed various methods for reducing the coefficient of friction (COF) to the superlubricity state(COF <0.01) through efficient lubrication technology.

关键词： friction corrosion energy.

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TEA-IHPCR: Temporal Augmented-Invariant Vehicle-Infrastructure Cooperative Heterogeneous Point Cloud Registration

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Zhang, Shiyan Zhang, Xu Zhang, Xinyu Chen, Yuxi Zhu, Dongwei Wu, Fan Jin, Dafeng Li, Jun Tsinghua University State Key Laboratory of Automotive Safety and Energy School Vehicles and Mobility Beijing China Tsinghua University Suzhou Automotive Research Institute Suzhou China Nanyang Technological University School of Electrical and Electronic Engineering Singapore Singapore Tsinghua University State Key Laboratory of Automotive Safety and Energy Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Forestry University School of Technology Beijing China Shanghai Polytechnic University School Faculty of Intelligent Manufacturing and Control Engineering Shanghai China Tsinghua University State Key Laboratory of Automotive Safety and Energy The School of Vehicle and Mobility Beijing China

In the field of autonomous driving, relying solely on environmental information captured by single vehicle's LiDAR often falls short of perception requirements in complex scenarios. Adopting a vehicle-infrastructure cooperation approach can effectively address information limitations. Therefore, the development of an algorithm that can register heterogeneous LiDAR point clouds is crucial. This paper proposes a vehicle-infrastructure cooperative heterogeneous point cloud registration network (TEA-IHPCR) based on temporal augmented information and rotation invariance. By capturing temporal augmented information between continuous frames of point clouds, data augmentation is performed on the point cloud data from both vehicle and infrastructure sides, mitigating issues of poor heterogeneous point cloud registration caused by perspective differences. The rotation invariance property can address the issue of aligning coordinate systems between the vehicle and infrastructure sides that have not been collaboratively calibrated. Simultaneously, the use of a local-level encoder capable of extracting point cloud features with rotation invariance, combined with a global-level attention mechanism that can learn cross-frame features, improves the model's performance in low-overlap rate point cloud registration. The experiments were conducted on a vehicle-infrastructure collaborative dataset while the results demonstrate the effectiveness of the proposed module in the network, as well as its superiority and efficiency compared to other networks. © 1963-2012 IEEE.

关键词： Spatio-temporal data

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Method for constructing multi-dimensional feature map of malicious code 5

Method for constructing multi-dimensional feature map of mal...

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2020 5th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Ma, Haocong Zhang, Ji Zhou, Junhua Zhai, Xiang Xue, Junjie Ji, Hang State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing100854 China

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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