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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

arXiv

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arXiv 2021年

作者： Li, Yue Zhou, Xuyang Colnaghi, Timoteo Wei, Ye Marek, Andreas Li, Hongxiang Bauer, Stefan Rampp, Markus Stephenson, Leigh T. Max-Planck Institut für Eisenforschung GmbH Max-Planck-Straße 1 Düsseldorf40237 Germany Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany

Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyse the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type d¢–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Swimming characteristics of soft robot with magnetoelastic material

Swimming characteristics of soft robot with magnetoelastic m...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Xiang, Hongbiao Li, Mengwei Zhang, Tilei Wang, Shoujun Yang, Xiuping Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781728163215

Cable-less micro-robots have exhibit great capabilities for remote applications in small and constrained environments, especially in biotechnology and healthcare. This paper presents a swimmer propelled by its undulatory deformation using magnetoelastic composite material. The robot can move freely on water surface driven through a function of magnetic field, such as forward, backward and steering with different velocity. The magnetic field is generated by 3D Helmholtz coils, and the locomotion of the micro swimming robot is controlled by different applied magnetic field. To understand the characteristics of the swimmer, the force balance equation is established, and the morphologies of swimmer with different magnetic field are analyzed and described through finite element analysis method. Furthermore, we discuss and analyze relationship between the parameters of magnetic field and the speed of the swimmer. The experimental results verified the speed model of the swimming robot, which is influenced by the frequency and strength of magnetic field. © 2019 IEEE.

关键词： Magnetic fields

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Visual servoing of magnetic swimming robot based on mean shift and fast template matching algorithm

Visual servoing of magnetic swimming robot based on mean shi...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Xiang, Hongbiao Zhang, Tilei Li, Mengwei Wang, Shoujun Yang, Xiuping Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781728163215

The actuation and control of mini-scale swimming robots have exhibit promising potential in limited and constrained environments, such as biomedical, micro-assembly and microfluidic applications. However, closed-loop control is still a challenging for the precision and repeatability in those applications. In this paper, a new type of soft-micro swimming robot based on magnetoelastic composite material is designed, and the swimmer can move freely on the water surface controlled by a rotating magnetic field with visual servoing. The visual feedback control strategy is proposed based on Mean Shift Algorithm and Fast Template Matching. The algorithm includes three parts: prediction, template matching, target positioning and template updating. To understand the characteristics of the swimmer, the relationship between swimming speed and magnetic field strength and frequency is verified by the algorithm in experiments. Furthermore, the proposed path following method with visual servoing applied in this control system. The experimental results show that the algorithm runs fast, has high accuracy and good real-time performance, and can provide great potential in mini-scale robot applications. © 2019 IEEE.

关键词： Visual servoing

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Graphene quantum dots induced performance enhancement in memristors

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Nanoscale 2025年第23期17卷 2025 May 28页

作者： Jintao He Guangdong Zhou Bai Sun Lingpeng Yan Xiaochen Lang Yongzhen Yang Haotian Hao MOE Key Laboratory of Interface Science and Engineering in Advanced Materials College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 China. yangyongzhen@***. College of Artificial Intelligence Brain-Inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing 400715 China. Frontier Institute of Science and Technology (FIST) Xi'an Jiaotong University Xi'an Shaanxi 710049 China. College of Artificial Intelligence Institute of Biomedical Engineering Taiyuan University of Technology Taiyuan 030024 China. haohaotian@***.

With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.

关键词： Quantum computers

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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False news detection on social media

arXiv

引用

arXiv 2019年

作者： Cao, Juan Sheng, Qiang Qi, Peng Zhong, Lei Wang, Yanyan Zhang, Xueyao Key Laboratory of Intelligent Information Processing Center for Advanced Computing Research Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

Social media has become an important information platform where people consume and share news. However, it has also enabled the wide dissemination of false news, i.e., news posts published on social media that are verifiably false, causing significant negative effects on society. To help prevent further propagation of false news on social media, we set up this competition to motivate the development of automated real-time false news detection approaches. Specifically, this competition includes three subtasks: false-news text detection, false-news image detection, and falsenews multi-modal detection, which aims to motivate participants to further explore the efficiency of multiple modalities in detecting false news and effective fusion approaches of multi-modal contents. To better support this competition, we also construct and release a multi-modal data repository about False News on Weibo Social platform(MCG-FNeWS) to help evaluate the performance of different approaches from participants. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

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The influence of process parameters and building orientation on fracture toughness and fatigue crack growth behavior of laser powder bed fused tantalum

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International Journal of Fatigue 2025年 199卷

作者： Rongzheng Huang Wenling Shi Kuixue Xu Rong Wang Haiqiong Xie Kai Wei Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment Ministry of Education Hunan University Changsha 410082 PR China Beijing Chunlizhengda Medical Instruments Co. Ltd. Beijing 101100 PR China Defense Innovation Institute Academy of Military Sciences Beijing 100071 PR China School of Integrated Circuits Chongqing University of Posts and Telecommunications Chongqing 400065 PR China Chongqing Institute of Bio-Intelligent Manufacturing Chongqing 401336 PR China

Laser powder bed fused tantalum (LPBF-Ta) is a burgeoning biometal for application in personalized orthopedic implants. However, the effects of process parameters and building orientation on fracture toughness and fatigue crack growth behavior remain to be elucidated. In this study, LPBF-Ta with superior crack resistance was fabricated by optimizing laser energy density and building orientation, and the underlying strengthening and failure mechanisms were revealed. The experimental results indicate that the optimal fracture toughness of 77.4 MPa∙m 1/2 and the lowest fatigue crack growth rate were obtained in samples built along the XY orientation at a laser energy density of 154.16 J/mm 3 . Superior plastic deformation capabilities lead to crack tip blunting, and localized fracture regions along the boundaries of cellular structures facilitate crack branching and deflection, synergistically contributing to the excellent fracture toughness. Besides, high-angle grain boundaries, triple junctions, cellular blocks, and dislocation networks collectively impede dislocation mobility, thereby resulting in a reduced fatigue crack growth rate. Conversely, samples built along the ZX orientation at identical laser energy density demonstrated superior fatigue crack growth threshold. The elevated threshold is primarily attributed to the slip mismatch between adjacent grains and the roughness-dominated crack closure effect.

关键词： Crack tips

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A Novel Spatial-Temporal Convolutional Neural Network for Remote Photoplethysmography

A Novel Spatial-Temporal Convolutional Neural Network for Re...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Min Hu Dong Guo Xiaohua Wang Peng Ge Qian Chu School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China

Remote photoplethysmography (rPPG) using a simple consumer-level webcam has great potential for healthcare, human-computer interaction, affective computing, and many other fields. However, traditional methods based on the skin reflection model and signal processing achieved limited success. Thus, this paper presents a novel end-to-end spatial-temporal convolutional neural network for the rPPG recovering task: To preserve the details, the pooling layer is moved to the branch network, and the attention mechanism is used to enhance the backbone network performance. Moreover, the ConvLSTM was used to learn the relationship between frames. The manual features extraction and signal post-processing steps can be omitted through our end-to-end neural network. The experimental results conducted on PURE and COHFACE datasets show that the proposed method significantly improved the quality of the recovered rPPG signal.

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