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A DQN-Based Cache Strategy for Mobile Edge Networks

Computers, Materials & Continua 2022年第5期71卷 3277-3291页

作者： Siyuan Sun Junhua Zhou Jiuxing Wen Yifei Wei Xiaojun Wang School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing100854China Ningbo Sunny Intelligent Technology Co. LTDYuyao315400China Dublin City University Dublin 9Ireland

The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay and system throughput can be improved *** the duplicate content transmissions between edge network and remote cloud can be reduced,the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great *** paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning *** deep neural network(DNN)and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network(DQN),in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning *** parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method,so the DQN algorithm could converge to the optimal solution quickly,and the network performance of the content caching policy can be *** simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.

关键词： Mobile edge network edge caching energy efficiency

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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A New Approach to the Position of Acupoints on the Back of Humans

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Recent Patents on Mechanical Engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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Molecular dynamics simulation and experimental study of laser-assisted machining of SiCp/Al composites 20

Molecular dynamics simulation and experimental study of lase...

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20th CIRP Conference on Modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Cui, Feijie Zhang, Hang Yang, Minghui Deng, Ben Lv, Jiawei Yan, Rong Peng, Fangyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China

High-quality machining of SiCp/Al composite faces a number of challenges due to the presence of hard and brittle SiC particles, making it a typical difficult-to-machine material. Conventional machining (CM) inevitably leads to high cutting forces and poor surface quality, which significantly limit the potential applications of SiCp/Al composites. Laser-assisted machining (LAM) has been evidenced that it can remarkably improve the machinability of SiCp/Al composites, however, the microscopic removal mechanism of the material under the effect of laser remains to be investigated in depth. In this paper, the molecular dynamics (MD) simulation and Transmission Electron Microscope (TEM) experiments are employed to carry out further research. A MD model for LAM of SiCp/Al composites is established, which is applied to analyze the dynamic evolution of the cutting forces and the dislocations of the material during the machining. The results show that with the rise of laser power, the dislocation density inside the Al matrix decreases gradually. The coordinated deformation ability of SiCp/Al composites is enhanced, and the cutting force tends to decrease. The high consistency between experimental and simulation results verifies the validity of the MD model. © 2025 Elsevier B.V.. All rights reserved.

关键词： Molecular docking

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Two-Stage Hand-Eye Calibration based on Variance Minimization Principle

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ding, Tao Tian, Dazhuang Wu, Hao Zhu, Dahu Li, Wenlog Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive Engineering Wuhan430070 China Wuhan University of Technology Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Provincial Engineering Research Center of Robot and Intelligent Manufacturing Wuhan430070 China Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan430074 China

Hand-eye calibration in industrial scenarios greatly determines the efficiency and effectiveness of 3D vision-guided robotic tasks. Most of the existing hand-eye calibration methods, however, rely on collecting large-scale point cloud samples to guarantee high measurement accuracy, which is time-consuming and particularly influenced by measurement defects such as local missing, noise, and uneven density. To overcome this challenging problem, we present a novel two-stage hand-eye calibration method based on the variance minimization principle. In the first stage, a robust function weighted variance minimization (RFWVM) registration algorithm is proposed for fine registration of defective point clouds, aiming at solving the coarse hand-eye calibration matrix with few-shot point clouds. In the second stage, an objective function of the calibration error correction algorithm is established via the point-to-plane distance variance minimization, which is intended to suppress the influence of measurement defects. Through these two stages, the globally optimal hand-eye matrix is solved by iteratively correcting the coarse hand-eye calibration matrix. A series of experiments validate the universality, accuracy, and robustness of the proposed method. © 1963-2012 IEEE.

关键词： Error correction

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Fusing Vehicle Trajectories and GNSS Measurements to improve GNSS Positioning Correction based on Actor-Critic Learning

Fusing Vehicle Trajectories and GNSS Measurements to improve...

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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Zhao, Haoli Li, Zhenni Chen, Ci Wang, Lujia Xie, Kan Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

Obtaining high-accuracy vehicle localization is essential for safe navigation in automated vehicles, however, complex urban environments result in inaccurate positioning because of multipath and non-line-of-sight errors, making precise positioning in complex environments an important and unsolved problem. Different from conventional model-based approaches which require rigid assumptions on sensors and noise models, data-driven artificial intelligent approaches can provide new solutions for Global Navigation Satellite System (GNSS) positioning problems in multipath environments. Recent learning-based works employ the generalization ability of the deep neural network to model these complex errors in urban environments, and provide effective positioning corrections for rough GNSS localizations by learning from a vehicle trajectory-based Reinforcement Learning (RL) environment. However, existing RL-based approaches employ insufficient features as input, and cannot take advantage of the vehicle trajectory *** tackle these issues, we propose a novel Deep Reinforcement Learning-based (DRL) positioning correction framework with fused features considering the vehicle trajectory information and GNSS features in the multi-input *** help the agent learn the correction policy efficiently with comprehensive inputs, we develop a new positioning correction RL environment with multi-input observations, which augments GNSS device readings of different satellites as complementary to the vehicle trajectory, and moreover employ a correction advantage reward to help the agent learn an efficient policy in positioning correction. To fully utilize comprehensive multi-input observations in the proposed environment, we employ the LSTM module to separately extract features to fuse brief states from input time series data. Finally, to address the long-term positioning accuracy, we construct the learning model based on actor-critic DRL structure with the cumulative reward

关键词： Statistical tests

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Design and Analyses of a Heavy-Load and Large-Stroke Micro-Positioning Tip-Tilt Stage with Flexure Levitation

Design and Analyses of a Heavy-Load and Large-Stroke Micro-P...

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International Conference on Manipulation, manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Huaxian Wei Zhaoyin Cai Junqiang Chen Xinjie Pan Tingting Liang Ministry of Education Key Laboratory of Intelligent Manufacturing (Shantou University) Shantou China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Shantou China

ISBN: (数字)9798350362107

ISBN: (纸本)9798350362114

The tip-tilt micro-positioning stage designed for laser-induced massive transfer of microLED needs to withstand external loads up to 20 kg and provide motion range up to millimeter-scale. This paper introduces a flexure-levitated large-stroke, heavy-load tip-tilt micro-positioning stage specifically designed for microLED laser-based massive transfer. It employs a compliant constant-force mechanism to design flexure levitation units that provide nearly constant support to the moving platform within the millimeter-scale travel range, achieving a quasi-levitated effect. Utilizing finite element analysis, the paper conducts parameter analysis to design a compliant constant-force mechanism based on positive and negative stiffness combinations. A analytical model of the flexure-levitated micro-stage is established to analyze its stiffness characteristics and constant-force properties. The system’s equivalent stiffness of the flexure-levitated tip-tilt micro-positioning significantly decreases, resulting in reduced driving forces and simplified force control methods. However, this approach also lowers the inherent natural frequency, necessitating the implementation of flexible supports for adjustment of dynamic characteristics.

关键词： Analytical models Force Dynamics Piezoelectric actuators Levitation Nanoscale devices Finite element analysis Structural engineering Motion measurement Payloads

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Flexible Strain Sensors with Ultra‑High Sensitivity and Wide Range Enabled by Crack‑Modulated Electrical Pathways

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Nano-Micro Letters 2025年第3期17卷 246-264页

作者： Yunzhao Bai Yunlei Zhou Xuanyu Wu Mengfei Yin Liting Yin Shiyuan Qu Fan Zhang Kan Li YongAn Huang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074People’s Republic of China Flexible Electronics Research Center Huazhong University of Science and TechnologyWuhan 430074People’s Republic of China Hangzhou Institute of Technology Xidian UniversityHangzhou 311200People’s Republic of China School of Mechano‑Electronic Engineering Xidian UniversityXi’an 710071People’s Republic of China Department of Aerospace and Mechanical Engineering University of Southern CaliforniaLos AngelesCA 90089USA

This study presents a breakthrough in flexible strain sensor technology with the development of an ultrahigh sensitivity and wide-range sensor,addressing the critical challenge of reconciling sensitivity with measurement *** by the structure of bamboo slips,we introduce a novel approach that utilises liquid metal to modulate the electrical pathways within a cracked platinum fabric *** resulting sensor demonstrates a gauge factor greater than 108 and a strain measurement capability exceeding 100%.The integration of patterned liquid metal enables customisable tuning of the sensor’s response,while the porous fabric structure ensures superior comfort and air permeability for the *** design not only optimises the sensor’s performance but also enhances the electrical stability that is essential for practical *** systematic investigation,we reveal the intrinsic mechanisms governing the sensor’s response,offering valuable insights for the design of wearable strain *** sensor’s exceptional performance across a spectrum of applications,from micro-strain to large-strain detection,highlights its potential for a wide range of real-world uses,demonstrating a significant advancement in the field of flexible electronics.

关键词： Flexible strain sensor Fabric Crack Response regulation Epidermal device

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Mobilenetv2-SSD Target Detection Method Based on Multi-scale Feature Fusion 8th

Mobilenetv2-SSD Target Detection Method Based on Multi-scale...

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Li, Boao Jiang, Du Tang, Xinjie Sun, Ying Weng, Yaoqing Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Longzhong Laboratory Hubei Xiangyang441000 China

ISBN: (纸本)9789819980208

Most of the deep learning networks used in target detection algorithms are very complex, which poses difficulties for edge devices with limited storage capacity and computing power, such as mobile terminals. A MobileNetv2-SSD target detection algorithm based on multi-scale feature fusion is proposed in this paper. We use MobileNetv2 to improve the SSD algorithm by replacing the original network’s backbone network VGG16 with the lightweight network MobileNetv2 to perform the model streamlining. The problem of accuracy loss due to model compression is improved by the designed feature fusion module and finally tested on the dataset. The experimental results show that the mAP and FPS are 90.37% and 50FPS, respectively, and the detection accuracy is improved by 3.24% compared with the original SSD network, the detection speed is 1.9 times of the original one, and the size of the model is only 28.5M, which is only 30.5% of the original model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Shifting Technique for Cogging Torque Suppression in Yokeless and Segmented Armature Machines 3

Shifting Technique for Cogging Torque Suppression in Yokeles...

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3rd International Conference on Power Systems and Electrical technology, PSET 2024

作者： Yi, Feng Zhang, Chi Qiu, Shuheng Chen, Jinhua Liu, Wei Shu, Xindong Ningbo Institute of Materials & Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo China Ningbo Institute of Materials & Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China

ISBN: (纸本)9798350353204

This paper proposed the shifting technique for cogging torque suppression in yokeless and segmented armature (YASA) machines. This shifting technique includes the rotor, stator, and hybrid shifting. The bilateral cogging torques produced from the lower and upper air gaps can be compensated by regulating the rotor and stator shifting values. Subsequently, an analytical model is formulated based on the superstition principle, which yields the optimal shifting value for cogging torque minimization. The impact of the rotor, stator, and hybrid shifting techniques on cogging torque suppression is analyzed, respectively. In addition, comprehensive comparisons of electromagnetic performance are conducted, in terms of the air-gap flux density, back-EMF, cogging torque, and electromagnetic torque. The comparative results demonstrate a reduction in cogging torque by 15.8%, 17.1%, and 11.1% with the rotor, stator, and hybrid shifting, respectively, without compromising output performance. © 2024 IEEE.

关键词： Stators

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