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GO/MgO/Mg interface mediated strengthening and electromagnetic interference shielding in AZ31 composite

Journal of Magnesium and Alloys 2023年第10期11卷 3800-3814页

作者： Z.Y.Xu C.F.Fang C.J.Li R.Wang X.P.Zhang J.Tan Y.M.Wang Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China Key Laboratory of Materials Modification by Laser Ion and Electron Beams(Ministry of Education)School of Materials Science and EngineeringDalian University of TechnologyDalian 116023China

More requirements of electromagnetic interference(EMI) shielding performance are put forward for lightweight structural materials due to the development of aerospace and 5G communications. Herein, graphene oxide(GO) decorated with SnO_(2) coating is introduced as reinforcement into AZ31 Mg alloy. During the smelting process, the MgO layer is in situ gernerated at interface between GO and the molten Mg alloy matrix by consuming SnO_(2). In the solid state, such kind of interface structure can improve the GO-Mg interface bonding intensity,also significantly generate stacking faults. The AZ31 composite reinfoced by trace modified GO(0.1 wt%) exhibits high ultimate strength and almost the same elongation with AZ31 alloy. Compared with AZ31 alloy, the yield strength and ultimate tensile strength of composite are increased by 33.5% and 23.7%, respectively. Meanwhile, the multi-level electromagnetic reflection from the multi-layer structure of GO and the interface polarization caused by the MgO mid-layer can significantly improve EMI shielding performance. The appropriate interface design strategy achieves the effect of “two birds with one stone”.

关键词： Metal-matrix composites Mechanical properties EMI shielding Microstructures

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Dual Sparse Variational Autoencoder-driven Interactive Estimation of Distribution Algorithm with User Generated Contents

Dual Sparse Variational Autoencoder-driven Interactive Estim...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Yang, Hang Bao, Lin Sun, XiaoYan Peng, YuMeng Jiangsu University of Science and Technology School of Automation Zhenjiang China China University of Mining and Technology School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798331517021

Personalized search and recommendation tasks in a big data environment have attracted wide attention from researchers while also presenting significant *** paper proposed a dual sparse variational autoencoder-driven interactive estimation of distribution algorithm with user generated contents, which considers digging deeper into user generated contents and combining with intelligent evolutionary optimization methods. Firstly, user generated contents in web are extensively collected to build a user interest preference model based on dual sparse variational autoencoders to extract users' preference features. Secondly, the distribution probability is reconstructed according to the neurons of the output layer of the sparse variational autoencoders in the user interest preference model. Combining with Gaussian probability density function, a estimation of distribution sampling probability model based on the user interest preferences is designed in the framework of an interactive estimation of distribution algorithm to generate new individuals with the user preference information. Meanwhile, a user evaluation surrogate model based on the user interest preferences is established by integrating matrix factorizations to participate in a personalized evolutionary search process. Finally, according to new user generated contents and model management mechanisms, the user interest preference model and related models are dynamically updated to guide the interactive personalized evolutionary search for personalized recommendation task. Extensive experiments on various actual public datasets are executed to validate the feasibility, effectiveness and interpretability of the proposed algorithm. It improves the performance of personalized search and recommendation methods. © 2024 IEEE.

关键词： Matrix factorization

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FPGA-Oriented Target Detection Accelerator Design 5

FPGA-Oriented Target Detection Accelerator Design

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Huang, Weizhe Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin China

ISBN: (纸本)9798350309034

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method. © 2023 IEEE.

关键词： CNN FPGA target detection Winograd algorithm

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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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Computer Modeling in engineering & sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of Management Science and Engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China Department of Industrial Engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation *** fault diagnosis for railway point machines becomes a hot *** the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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DFE-SLAM: Dynamic SLAM based on improved feature extraction

DFE-SLAM: Dynamic SLAM based on improved feature extraction

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2024 China Automation Congress, CAC 2024

作者： Cheng, Guiying Jia, Jianfang Pang, Xiaoqiong Wen, Jie Shi, Yuanhao Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China

ISBN: (纸本)9798350368604

Traditional visual localization algorithms often assume a static world, making them susceptible to inaccuracies and reduced robustness in real environments with dynamic objects. Additionally, these algorithms struggle to maintain high localization accuracy in low-texture settings. To overcome these challenges, we propose a dynamic SLAM system with enhanced feature extraction capabilities, called DFE-SLAM (Dynamic SLAM based on improved feature extraction). DFE-SLAM employs a lightweight YOLOv5s dynamic target detection network to filter out dynamic features from detection results. It also utilizes adaptive thresholding during the feature point detection stage to enhance accuracy. Moreover, the system incorporates a dense map construction thread, which creates dense point cloud maps using keyframes with corresponding bit poses. We evaluated DFE-SLAM using the TUM and Bonn datasets, benchmarking it against several advanced SLAM systems. The results demonstrate that DFE-SLAM excels in dynamic environments, delivering superior accuracy and robustness compared to most existing SLAM systems. © 2024 IEEE.

关键词： dense map Dynamic scenes feature extraction simultaneous localization and map building YOLOv5s

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Scene images and text information‐based object location of robot grasping

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IET Cyber-Systems and Robotics 2022年第2期4卷 116-130页

作者： Zhichao Liu Kaixuan Ding Qingyang Xu Yong Song Xianfeng Yuan Yibin Li School of Mechanical Electrical&Information EngineeringShandong UniversityWeihaiChina School of Control Science and Engineering Shandong UniversityJinanChina

With the rapid development of artificial intelligence(AI),the application of this technology in the medical field is becoming increasingly extensive,along with a gradual increase in the amount of intelligent equipment in *** robots can save human resources and replace nursing staff to achieve some *** view of the phenomenon of mobile service robots'grabbing and distribution of patients'drugs in hospitals,a real‐time object detection and positioning system based on image and text information is proposed,which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object(medicine bottle).The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category,and bounding boxes are ***,the images in the bounding boxes are enhanced to overcome unfavourable factors,such as a small object *** text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text *** object information provided by the two models is fused,and the text recognition result is matched with the object detection box to achieve the precise posi-tioning of the grasping *** kernel correlation filter(KCF)tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's *** deep learning models adopt lightweight networks to facilitate direct *** experiments show that the proposed robot grasping detection system has high reliability,accuracy and real‐time performance.

关键词： dexterous manipulators neural network‐based,robot control robot vision service robots

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Remaining useful life prediction of sodium-ion batteries based on ICEEMDAN-CNN-GRU 2

Remaining useful life prediction of sodium-ion batteries bas...

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2023 2nd International Conference on New Energy, Energy Storage and Power engineering, NESP 2023

作者： Shi, Yongsheng Zhai, Xinran Hu, Yujun School of Electrical and Control Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

A hybrid battery remaining useful life (RUL) prediction model based on ICEEMDAN-CNN-GRU(M1) is proposed to address the nonlinearity and complexity of capacity degradation in sodium-ion batteries. Firstly, capacity attenuation data and some indirect parameters easily obtainable by sensors are experimentally measured. The original capacity sequence is reconstructed into a new one using the ICEEMDAN method to effectively suppress the influence of capacity regeneration and noise signals. Secondly, a hybrid CNN-GRU prediction model is constructed by leveraging the advantages of convolutional neural networks (CNN) in the field of data mining and gated recurrent unit (GRU) in time series prediction. Three sets of indirect parameters are used as inputs, and the reconstructed capacity is used as the output for RUL prediction model training with different starting points. Finally, the effectiveness of the algorithm is verified through data from three different rates, and the predicted indicators are better than those of traditional algorithms such as GRU, LSTM, and SVM. © Published under licence by IOP Publishing Ltd.

关键词： Sodium-ion batteries

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The relationship between drill pipe torque and mining induced stress under constant rotation and drilling speed 4

The relationship between drill pipe torque and mining induce...

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2024 4th International Conference on Measurement control and Instrumentation, MCAI 2024

作者： Tan, Jian Geng, Yanfeng Wang, Yakun College of Control Science and Engineering China University of Petroleum Qingdao266580 China School of Civil Engineering and Architecture Shandong University of Science and Technology Qingdao266590 China

Mining-induced stress strongly influences coal mining safety. The magnitude of coal seamstress is one of the factors for assessing rockburst risk. This study aims to explore the relationship between drill pipe torque and mining-induced stress under the conditions of different constant rotation rates and drilling speed combinations. Through theoretical analysis and numerical simulations, the variation of mining-induced stress and drill pipe torque was systematically recorded in the drilling process, assuming that it maintains a consistent drilling speed and rotation rate. The results indicate that there is a positive correlation between the mining-induced stress and the torque. Thus, the measurement of the stress can be achieved by monitoring the torque. © Published under licence by IOP Publishing Ltd.

关键词： Drill pipe

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Corwdsourced Task Recommendation via Link Prediction 18th

Corwdsourced Task Recommendation via Link Prediction

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Yu, Song Pan, Qingxian Li, Li School of Computer and Information Science Southwest University Chongqing400715 China School of Computer and Control Engineering Yantai University Shandong264005 China

ISBN: (纸本)9789819996391

Mobile crowdsourcing (MCS) can solve problems that are difficult for computers to solve accurately or efficiently. Current crowdsourcing workers face the challenges of overload, task recommendation is presented to deal with the above issue. The existing MCS task recommendation methods only consider the workers themselves, without taking the potential relationships between workers and workers and tasks, which may fail to grasp the long-term behavioral preferences of workers and cannot solve the cold start problem efficiently. To solve the above problems, this paper proposes a task recommendation method based on heterogeneous graph link prediction. First, we try to obtain the connection relationships between crowdsourcing tasks and workers and build a worker-task heterogeneous graph accordingly. Then, majority voting and DeepWalk are used to generate initial node features of workers. Second, we design two efficient message passing mechanisms to aggregate and update node features between task and worker nodes to explore the potential relationships between nodes. Finally, we obtain attention weights among nodes and use Bi-GRU to capture the long-term behavioral preferences of workers and recommend appropriate tasks to workers based on the similarity between workers and tasks to improve perceptual quality. Evaluations are conducted on seven real datasets. Experimental results show that our method is superior to the state-of-the-art baselines. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Message passing

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