检索结果-内蒙古大学图书馆

3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin School of Computer and Communication Engineering Changsha University Of Science and Technology Changsha China College of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350365443

'Memory hot pages' usually refer to memory pages that are frequently accessed within a period of time. Using faster memory pages for these hot pages can optimize application performance. Using 'fast memory' for hot pages involves data placement and migration issues on heterogeneous memory systems, and the synthesis and splitting of huge pages in ordinary systems. The premise for implementing these operations is to detect hot pages. This article briefly describes several advanced memory hot page detection methods and the principles of the corresponding memory management mechanism, and discusses multiple aspects such as hot page detection accuracy, overhead, and performance improvement of the corresponding memory management mechanism. © 2024 IEEE.

关键词： Memory management

来源：评论

学校读者我要写书评

暂无评论

A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

引用

清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

来源：评论

学校读者我要写书评

暂无评论

Multi-dimensional Resource Allocation in HAP-assisted UAV Wireless Networks for IoRT Data Collection

Multi-dimensional Resource Allocation in HAP-assisted UAV Wi...

引用

2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Zhao, Hui Zhang, Xinran Chen, Weilong Xu, Xiaobin Wang, Li Chang, Zheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Beijing University of Posts and Telecommunications School of Computer Science Beijing China Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350351255

In this paper, we propose a multi-dimensional resource allocation scheme for Internet of Remote Things (IoRT) data collection in a high altitude platform (HAP)-assisted unmanned aerial vehicle (UAV) network. Considering the quality of service (QoS) requirements of delay-sensitive IoRT data, we propose a UAV-HAP double relay data transmission mode to reduce the transmission delay for delay-sensitive data. Since the resources of the UAV are limited, we jointly optimize communications, computing and storage resources to maximize the utility of the considered system. Due to the high dimensionality of the solution space, we design a Twin Delayed Deep Deterministic policy gradient-based multi-dimensional resource allocation (TD3-MDRA) algorithm to find the optimal resource allocation strategy. Extensive simulation results are presented to demonstrate the superior performance of TD3-MDRA for IoRT data collection with delay and resource constraints. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Monitoring Utility of Unmanned Aerial Vehicles Considering Adverse Effects

引用

IEEE Transactions on Mobile Computing 2025年第7期24卷 5996-6013页

作者： Zhang, Haihan Dai, Haipeng Qiu, Yu Yu, Enze Zhou, Ruiben Wang, Weijun Wang, Jingwu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China South China University of Technology School of Computer Science and Engineering Guangzhou China Beijing China

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact that UAV monitoring may cause a huge risk/annoyance on living objects. In this paper, we investigate the novel problem of oPtimizing unmanned aErial vehicles plAcement by Considering both monitoring utility and adverse Effects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a 1-1/e- approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a 1-1/e approximation and O(n\log n) time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

引用

5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in Tennis Match Analysis: Predicting Score Point Victor and Momentum Shift 5

Machine Learning in Tennis Match Analysis: Predicting Score ...

引用

5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Hu, Jinming Yang, Xiaohua Huang, Zixuan Xie, Jinqi School of Computer and Communication Engineering Changsha University of Science & Technology Changsha China School of Mathematics and Computer Science Shanxi Normal University Taiyuan China

ISBN: (纸本)9798331530334

In tennis and other competitive sports, momentum plays a pivotal role, where actions like consecutive scoring can enhance a player's momentum and increase his chances of winning the match. Quantifying momentum and predicting match outcomes in real-time are critical for both scientific training and strategic decision-making in the sports industry. However, traditional forecasting approaches often overlook the cumulative nature of time-series data and lack quantitative analysis of the dynamic properties influencing match outcomes. In this study, we compare seven machine learning models across two key tasks: predicting the winner of the next point and identifying the critical moment of momentum transition. We introduce a novel momentum formula to quantify the win/loss trends throughout a match. Our experiments using data from the 2023 Wimbledon Championships, demonstrate up to 70% accuracy in predicting point winners using logistic regression (LR) and up to 97% accuracy in detecting momentum shifts via a light gradient boosting machine (LGBM). These findings underscore the significance of momentum in tennis and offer valuable insights for performance optimization and competitive strategy development. © 2024 IEEE.

关键词： Tennis

来源：评论

学校读者我要写书评

暂无评论

Learning a Detail-Oriented Transformer for Image Denoising 1

引用

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Long, Xuan Liu, Mei-Qin Yao, Chao Jin, Shuo Lin, Chun-Yu Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

CNN-based methods have achieved significant success in image denoising with powerful and generous local priors. However, these methods are not good at exploring non-local self-similarity which is also an effective prior for image denoising. To overcome this weakness, we propose a detail-oriented transformer with Dense Network for Image Denoising (DDNet), exploiting global and local self-similarity for better reconstruction quality. Specifically, DDNet consists of a global feature extraction branch and a detail-maintained graph attentive branch. The global feature extraction branch utilizes a number of densely connected transformer blocks for contextual information. The detail-maintained graph attentive branch employs a dynamic graph attentive network to distinguish the image structure and noises for retaining the texture details. Numerous experiments show the superiority of DDNet on real image denoising, synthetic image denoising, and image deraining. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

A survey on graph neural network-based next POI recommendation for smart cities

引用

Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

来源：评论

学校读者我要写书评

暂无评论

SIGVIC: Spatial Importance Guided Variable-Rate Image Compression 48

SIGVIC: Spatial Importance Guided Variable-Rate Image Compre...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liang, Jiaming Liu, Meiqin Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

ISBN: (纸本)9781728163277

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. However, the diversity of spatial importance is instructive for bit allocation of image compression. In this paper, we introduce a Spatial Importance Guided Variable-rate Image Compression (SigVIC), in which a spatial gating unit (SGU) is designed for adaptively learning a spatial importance mask. Then, a spatial scaling network (SSN) takes the spatial importance mask to guide the feature scaling and bit allocation for variablerate. Moreover, to improve the quality of decoded image, Top-K shallow features are selected to refine the decoded features through a shallow feature fusion module (SFFM). Experiments show that our method outperforms other learning- based methods (whether variable-rate or not) and traditional codecs, with storage saving and high flexibility. © 2023 IEEE.

关键词： image compression scale factor shallow feature spatial importance variable-rate

来源：评论

学校读者我要写书评

暂无评论

Multi-frame Correlated Representation Network for Video Super-Resolution

Multi-frame Correlated Representation Network for Video Supe...

引用

2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Jin, Shuo Liu, Meiqin Guo, Yu Yao, Chao Obaidat, Mohammad S. Institute of Information Science Beijing Jiaotong University Beijing China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Amity University UP Noida201301 India

ISBN: (纸本)9798350336092

Video super-resolution aims to reconstruct the high-resolution video from its degraded observation. Existing methods primarily utilize explicit alignment strategies, including optical flow and motion compensation, and implicit alignment strategies, including deformable convolution and non-local attention, to model the temporal information. However, these alignment strategies with motion estimation and motion compensation (MEMC) introduce inaccurate inter-frame information to some extent, which largely affects the reconstruction performance. To avoid the error compensation information, we propose to model the temporal information from the perspective of the self-similarity between frames and design a multi-frame correlated representation network (MCRNet) for video super-resolution. To address the issue of temporal information distortion caused by large-scale pixel displacement and object occlusion, MCRNet extracts temporal information through similar regions on multiple frames, which are aggregated with allocated weights for information compensation. Moreover, we design a multi-scale non-local information fusion module for non-local correlation matching of spatio-temporal features in the multi-scale space, which maintains the scale consistency of spatio-temporal features. Experimental results indicate that MCRNet achieves promising gains over other competing methods which employ explicit and implicit alignment strategies on different datasets. © 2023 IEEE.

关键词： Alignment

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：