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First-principles investigation of adhesion strength and interfacial bonding in Mg/X(X=Ti,Zr,Hf,V,Nd,Cr,Mo,Mn,and Fe)interface

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Journal of Magnesium and Alloys 2025年第1期13卷 83-89页

作者： Longke Bao Zhifu Yao Yuhui Zhang Cuiping Wang Kaihong Zheng Rongpei Shi Xingjun Liu Fusheng Pan State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650China College of Materials Science and Engineering Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

Magnesium alloys have gained extensive applications across various industries,including aerospace,transportation,and civil construction,owing to their excellent combinations of high specific strength and stiffness[1].However,their lim-ited strength due to the lack of effective strengthening phases has hindered their broader industrial applications[2].Never-theless,it has been challenging to achieve significant strength-ening due to the restricted solubility of alloying elements in magnesium[3].Thus,more and more efforts have been made to explore the concept of secondary phase-reinforced magne-sium alloys[2,4,5],where the secondary phase acts as re-inforcing agents within the magnesium matrix,resembling a composite material.

关键词： adhesion strength interfacial bonding aluminum alloys industrial applications solubility strengthening phases alloying elements Mg X interfaces

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3D Target Detection of Tractor in Agricultural Scene Based on Lidar 8

3D Target Detection of Tractor in Agricultural Scene Based o...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Zhibo, Chen Caicong, Wu Banghao, Lin College of Information and Electrical Engineering China Agricultural University Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing China

ISBN: (纸本)9798350305838

Real time and accurate detection of agricultural machinery in the working scene is an important basis to improve the safety of agricultural machinery driverless and autonomous operation. This paper presents a 3D target detection method of tractor based on GNSS/INS/Lidar/camera data. Firstly, an on-board data acquisition platform based on multi-source sensors is constructed to obtain the tractor image, point cloud and position and attitude data of the acquisition vehicle in the common working scenes of agricultural machinery (machinery depot, tractor road and farmland), the data are time synchronized and space calibrated. Then, distortion correction of point cloud, the point cloud and image are jointly labelled, and the tractor test data set of images, point cloud and GNSS/IMU information is constructed. 643 frames of machinery depot scene data, 321 frames of tractor track scene data, and 320 frames of farmland scene data are obtained. The number of tractors selected is 6250. Finally, according to the characteristics of tractor driving and operation, the region of interest of tractor target detection is defined, the point cloud within its range is extracted, and the ground point cloud is filtered by RANSAC method. Then the data set is input into PVRCNN++ network for training according to the proportion of 5:1 between training set and test set, and finally the tractor 3D target detection model is obtained. The test results show that the detection accuracy of the model for tractors in machinery depot, tractor road and farmland are 90.21%, 87.59%, and 99.85% respectively, the recall rates are 86.72%, 95.13%, and 98.89% respectively, and the F1 scores are 88.43%, 91.20%, and 99.37% respectively. The model can better adapt to the agricultural scene. © 2023 IEEE.

关键词： Tractors (agricultural)

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Towards Exploratory Query Optimization for Template-Based SQL Workloads 40

Towards Exploratory Query Optimization for Template-Based SQ...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Feng, Jieming Li, Zhanhuai Chen, Qun School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798350317152

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives. © 2024 IEEE.

关键词： Dynamic programming

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A Social recommendation model based on hypergraph deep representation learning 5

A Social recommendation model based on hypergraph deep repre...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Liu, Yanan Wu, Yufan Wang, Jinlan Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

In social recommendation, user's interest preference is influenced by multiple factors. It is a challenging problem to mine data information effectively in multiple types of interaction behaviors between users and items to obtain more accurate embedding of user items. Existing social recommendation models tend to take a homogeneous approach to the interaction effects of neighbourhood nodes on target nodes. We propose a social recommendation model based on deep representation learning of hypergraphs. The model aims to learn embedded representations of users and items from multi-type user-item interaction data efficiently. An attention mechanism that integrating time interval information is introduced for computing the preference weights of each item to the user. Messages are generated with time-interval attention weights the inter-user and inter-item hypergraph information is combined to update the messages and aggregate the information from multiple layers. Extensive experiments on two real datasets show that the proposed models are superior to other advanced recommendation methods in improving the recommendation performance. © 2024 SPIE.

关键词： Federated learning

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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Calibration of Car-Following Behavior Based on Monocular Camera 11th

Calibration of Car-Following Behavior Based on Monocular Cam...

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11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Liu, Yufei Wei, Chong MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Mechanical and Electrical Engineering Hainan University Haikou China

ISBN: (纸本)9789819796434

In the past, most of the research on car-following models in China focused on physical statistics, with relatively little collection and analysis of car-following data. Therefore, it is essential to establish a car-following model suitable for analyzing the car-following characteristics of Chinese drivers based on real driving data. This paper utilizes a vehicle-mounted monocular camera to capture traffic flow images on the highway and uses the YOLOv5 algorithm to detect vehicles. Through the homography transformation between the image plane in the front view and the spatial plane in the top view, the pixel coordinates are converted to 3D spatial coordinates. Then, the ranging model is established, and its effectiveness is verified using the length of the lane dividing lines. The factor analysis is used to determine the main influencing factors of car-following behavior. The appropriate linear car-following models are selected and its parameters are calibrated using multiple linear regression methods. This paper realizes the collection and subsequent processing of car-following behavior data under natural driving conditions, and transforms generalized car-following models into specific research theories for application in practical situations. This method of collecting car-following driving data has strong practicality and can provide effective data support for studying car-following models that accurately reflect the operation conditions of road traffic. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Behavioral research

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Research on an intelligent encryption method for remote sensing images with variable dimension employing dual-chaotic system

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Optoelectronics Letters 2024年

作者： YU Jia-hui MAN Zhen-long ZHANG Fan MENG Xiang-fu School of Electronic and Information Engineering， Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications

The rapid advancement of remote sensing technology has heightened concerns over the security of sensitive information. This paper presents an intelligent encryption scheme for remote sensing images using dimensionality variation. The scheme employs two high-dimensional chaotic systems to generate keys for simultaneous row-column scrambling and diffusion. By mapping a 2D plain-image to a 3D space， pixels are rearranged within a 3D cube using a chaotic key， followed by auto-correlation cyclic diffusion. Experimental results demonstrate that this approach significantly enhances encryption security， making it suitable for secure remote sensing image communication.

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Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention

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Computer Modeling in engineering & Sciences 2022年第9期132卷 909-927页

作者： Jianming Zhang Kai Wang Yaoqi He Lidan Kuang School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China

Recently,Siamese-based trackers have achieved excellent performance in object ***,the high speed and deformation of objects in the movement process make tracking ***,we have incorporated cascaded region-proposal-network(RPN)fusion and coordinate attention into Siamese *** proposed network framework consists of three parts:a feature-extraction sub-network,coordinate attention block,and cascaded RPN *** exploit the coordinate attention block,which can embed location information into channel attention,to establish long-term spatial location dependence while maintaining channel ***,the features of different layers are enhanced by the coordinate attention *** then send these features separately into the cascaded RPN for classification and *** to the two classification and regression results,the final position of the target is *** verify the effectiveness of the proposed method,we conducted comprehensive experiments on the OTB100,VOT2016,UAV123,and GOT-10k *** with other state-of-the-art trackers,the proposed tracker achieved good performance and can run at real-time speed.

关键词： Object tracking deep learning coordinate attention cascaded RPN

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