检索结果-内蒙古大学图书馆

Multi-action self-healing coatings with simultaneous recovery of corrosion resistance and adhesion strength

Journal of Materials Science & Technology 2022年第6期101卷 18-27页

作者： Chenhao Ren Yao Huang Wenkui Hao Dawei Zhang Xiejing Luo Lingwei Ma Jinke Wang Thee Chowwanonthapunya Chaofang Dong Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Advanced Power Transmission Technology(Global Energy Interconnection Research Institute Co.Ltd.) Beijing 100083China Faculty of International Maritime Studies Kasetsart UniversitySriracha Campus199 TungsuklaSrirachaChonburi 20230Thailand BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528300China

In this study, a new self-healing strategy that can simultaneously recover the corrosion resistance and the adhesion strength of coatings was introduced. The coating was based on a shape memory epoxy resin containing ethylene vinyl acetate(EVA) microspheres loaded with Ce(NO_(3))_(3)inhibitors, and was cured at a relatively high temperature to facilitate the fusion of adjacent microspheres for a strengthened self-healing effect. The electrochemical impedance spectroscopy(EIS) and scanning electrochemical microscopy(SECM) results demonstrated that the shape memory effect of epoxy matrix, the filling of molten EVA microspheres as well as the release of Ce(NO_(3))_(3)inhibitors contributed synergistically to suppress the corrosion reaction at the coating damage. After healing, the low frequency impedance modulus of the coatings containing Ce(NO_(3))_(3)-EVA microspheres was three orders of magnitude higher than that of the blank epoxy coating. The adhesion strength of the coatings containing Ce(NO_(3))_(3)-EVA microspheres on the metal substrate was also largely repaired thanks to the strong bonding effect of the EVA microspheres.

关键词： Self-healing coating Corrosion protection Adhesion strength Shape memory effect Bonding effect

来源：评论

学校读者我要写书评

暂无评论

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

Adversarial Driving Behavior Generation Incorporating Human ...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhen Liu Hang Gao Hao Ma Shuo Cai Yunfeng Hu Ting Qu Hong Chen Xun Gong School of Artificial Intelligence Jilin University China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China College of Communication Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China School of Electronic and Information Engineering Tongji University Shanghai China

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Distributed bandit learning: near-optimal regret with efficient communication

arXiv

引用

arXiv 2019年

作者： Wang, Yuanhao Hu, Jiachen Chen, Xiaoyu Wang, Liwei Institute for Interdisciplinary Information Sciences Tsinghua University School of Electronics Engineering and Computer Science Peking University Key Laboratory of Machine Perception MOE School of EECS Peking University Center for Data Science Peking University Beijing Institute of Big Data Research

We study the problem of regret minimization for distributed bandits learning, in which M agents work collaboratively to minimize their total regret under the coordination of a central server. Our goal is to design communication protocols with near-optimal regret and little communication cost, which is measured by the total amount of transmitted data. For distributed multi-armed bandits, we propose a protocol with near-optimal regret and only O(M log(MK)) communication cost, where K is the number of arms. The communication cost is independent of the time horizon T , has only logarithmic dependence on the number of arms, and matches the lower bound except for a logarithmic factor. For distributed d-dimensional linear bandits, we propose a protocol that achieves near-optimal regret and has communication cost of order O (Md), which has only logarithmic dependence on T . Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

引用

IEEE Transactions on Multimedia 2021年 23卷 2019-2032页

作者： Lei Sang Min Xu Shengsheng Qian Matt Martin Peter Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Automation Chinese Academy of Sciences Beijing China INTERACT Technology Sydney NSW Australia Mininglamp Academy of Sciences Mininglamp Technology Beijing China

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.

关键词： Semantics Collaboration YouTube Australia Visualization Context modeling

来源：评论

学校读者我要写书评

暂无评论

Near-optimal representation learning for linear bandits and linear RL

arXiv

引用

arXiv 2021年

作者： Hu, Jiachen Chen, Xiaoyu Jin, Chi Li, Lihong Wang, Liwei Key Laboratory of Machine Perception MOE School of EECS Peking University China Department of Electrical and Computer Engineering Princeton University United States Amazon United States Center for Data Science Peking University Beijing Institute of Big Data Research China

This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we play M linear bandits with dimension d concurrently, and these bandits share a common k-dimensional linear representation so that k d and k M. We propose a sample-efficient algorithm, MTLR-OFUL, which leverages the shared representation to achieve Õ(M√dkT + d√kMT) regret, with T being the number of total steps. Our regret significantly improves upon the baseline Õ(Md√T) achieved by solving each task independently. We further develop a lower bound that shows our regret is near-optimal when d > M. Furthermore, we extend the algorithm and analysis to multi-task episodic RL with linear value function approximation under low inherent Bellman error (Zanette et al., 2020a). To the best of our knowledge, this is the first theoretical result that characterizes the benefits of multi-task representation learning for exploration in RL with function approximation. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

TC-GAT: Graph Attention Network for Temporal Causality Discovery

arXiv

引用

arXiv 2023年

作者： Yuan, Xiaosong Chen, Ke Zuo, Wanli Zhang, Yijia College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering MOE Changchun China *** Inc Beijing China College of Electronic Countermeasures National University of Defense Technology Hefei China

The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge. Causality is frequently intertwined with temporal elements, as the progression from cause to effect is not instantaneous but rather ensconced in a temporal dimension. Thus, the extraction of temporal causality holds paramount significance in the field. In light of this, we propose a method for extracting causality from the text that integrates both temporal and causal relations, with a particular focus on the time aspect. To this end, we first compile a dataset that encompasses temporal relationships. Subsequently, we present a novel model, TC-GAT, which employs a graph attention mechanism to assign weights to the temporal relationships and leverages a causal knowledge graph to determine the adjacency matrix. Additionally, we implement an equilibrium mechanism to regulate the interplay between temporal and causal relations. Our experiments demonstrate that our proposed method significantly surpasses baseline models in the task of causality extraction. Copyright © 2023, The Authors. All rights reserved.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

TC-GAT: Graph Attention Network for Temporal Causality Discovery

TC-GAT: Graph Attention Network for Temporal Causality Disco...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaosong Yuan Ke Chen Wanli Zuo Yijia Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering MOE Changchun China *** Inc Beijing China College of Electronic Countermeasures National University of Defense Technology Hefei China

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Personality Traits for Enhanced Graph Convolutional Networks-Based Social Recommendation Systems

SSRN

引用

SSRN 2023年

作者： Guo, Xiangxing Zhou, Wei Yang, Zhengyi Fu, Cheng Wen, Junhao School of Big Data and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chognqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society MoE Chognqing400030 China

Social recommendation has emerged as a crucial technology for online service platforms, with a growing body of research in recent years. People's varying personalities influence their preferences for items and friends, and neglecting these personalities can result in impure representations that affect recommendation outcomes. In this paper, we propose a Personality-Aware Graph Convolutional Network (PGCN-SRec) model to capture users' preferences based on their personalities. The model first classifies users and their neighbors into personality subgraphs using their representations, and then graph convolution is performed within these subgraphs. User representations from social and interaction subgraphs are fused through a novel fusion module, and layer representations are generated by weighted summation. Experimental results demonstrate that our proposed method significantly improves recommendation performance on the Lastfm and Ciao datasets (by 1\% to 5\%). Furthermore, PGCN-SRec effectively alleviate the oversmoothing problem associated with stacked graph neural networks. Our PyTorch implementation is available at https://***/hsianghsing/PGCN-SRec. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Generalized Category Discovery with Large Language Models in the Loop

arXiv

引用

arXiv 2023年

作者： An, Wenbin Shi, Wenkai Tian, Feng Lin, Haonan Wang, QianYing Wu, Yaqiang Cai, Mingxiang Wang, Luyan Chen, Yan Zhu, Haiping Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China Lenovo Research China University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) 1 into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://***/Lackel/LOOP. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Weighted partial order oriented three-way decisions under score-based common voting rules

引用

International Journal of Approximate Reasoning 2020年 123卷 41-54页

作者： Li, Lei Wu, Xindong Chen, Huanhuan Zhou, Chuan Liu, Guanfeng Jiang, Yongkang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technologies Beijing 100084 China School of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Computing Dept Macquarie University Sydney A1C 5S7 Australia

Recently, with the trisecting and acting models of human cognitive behaviors, three-way decisions are introduced to deal with uncertain decisions in many applications. To define and conduct the three-way decisions on the possible or necessary loser or co-loser and/or winner or co-winner, we first define the score-based common voting rules, where a score-based function is introduced to evaluate certain utility, including positional scoring rules, maximin, Bucklin, and so on. Then on one hand, it has been proved that under certain conditions three-way decisions on the possible loser or co-loser and/or winner or co-winner are NP-complete with positional scoring rules, maximin, and Bucklin, respectively. On the other hand, we have presented algorithms whose time complexity is polynomial to conduct three-way decisions on the necessary winner or co-winner and/or necessary loser or co-loser with positional scoring rules, maximin, and Bucklin in time O(nm²), O(nm³), and O(nm²), respectively, which has been validated experimentally. © 2020 Elsevier Inc.

关键词： Common voting rules Three-way decisions Weighted partial orders

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：