检索结果-内蒙古大学图书馆

IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Liu, Chunhong Zhao, Mengdi Wang, Huaichen Cheng, Bo Liu, Jialei Yuan, Peiyan Engineering Lab of Intelligence Business & Internet of Things and School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility. IEEE

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Image proce bing with deep-learning and transfer learning for cutting tool degradation monitoring 1

Image proce bing with deep-learning and transfer learning fo...

引用

1st International Conference on Digital Sovereignty, ICDS 2024

作者： Colantonio, Lorenzo Equeter, Lucas Giovannelli, Hugo Dehombreux, Pierre Mahmoudi, Saïd Ducobu, François Machine Design and Production Engineering Lab University of Mom Place du pare 20 Mons7000 Belgium Computer Science Unit Place du parc 20 Mons7000 Belgium

Monitoring the degradation of cutting tools is of utmost importance in the manufacturing world. Tools with substantial wear fail to produce high-quality parts in terms of geometry, residual stre b, and surface finish. Furthermore, replacing tools in a non-optimal manner can lead to increased production costs and downtime. Therefore, monitoring the condition of the tool is e bential to avoid these additional costs and ensure good production quality. This article explores various cla bification models, specifically VGG19, EfficientNetV2, and Vision Transformers. These models cla bify the state of tools using their images. Using transfer learning, a comparison of the best-performing artificial intelligence-based image analysis models is conducted to identify those most suitable for monitoring cutting tools. A comparative analysis of their generalizability, performance and explainability is realized. The model with the best performance is VGG19 with an accuracy of 94%, followed by EfficientNetV2 and ViT with an accuracy of 87%. A full comparison of these results is carried out. © 2025 The Author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems 41

DPN: Decoupling Partition and Navigation for Neural Solvers ...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zheng, Zhi Yao, Shunyu Wang, Zhenkun Tong, Xialiang Yuan, Mingxuan Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China China Huawei Noah's Ark Lab Shenzhen China

The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest ***, reinforcement learning (RL)-based sequential planning methods have exhibited advantages in solving efficiency and ***, these methods fail to exploit the problem-specific properties in learning representations, resulting in less effective features for decoding optimal *** paper considers the sequential planning process of min-max VRPs as two coupled optimization tasks: customer partition for different routes and customer navigation in each route (i.e., partition and navigation).To effectively process min-max VRP instances, we present a novel attention-based Partition-and-Navigation encoder (P&N Encoder) that learns distinct embeddings for partition and ***, we utilize an inherent symmetry in decoding routes and develop an effective agent-permutation-symmetric (APS) loss *** results demonstrate that the proposed Decoupling-Partition-Navigation (DPN) method significantly surpasses existing learning-based methods in both single-depot and multi-depot minmax *** code is available at. Copyright 2024 by the author(s)

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model 27th

Automated Maternal Fetal Ultrasound Image Identification Usi...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Krishna, Thunakala Bala Poreddy, Ajay Kumar Reddy Sindhu, Kolla Gnapika Kokil, Priyanka Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031781940

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. The accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. This paper proposes a novel approach using a Hybrid Vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. The proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. The effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. The experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Image processing with deep-learning and transfer learning for cutting tool degradation monitoring 12

Image processing with deep-learning and transfer learning fo...

引用

12th CIRP Global Web Conference, CIRPe 2024

Monitoring the degradation of cutting tools is of utmost importance in the manufacturing world. Tools with substantial wear fail to produce high-quality parts in terms of geometry, residual stress, and surface finish. Furthermore, replacing tools in a non-optimal manner can lead to increased production costs and downtime. Therefore, monitoring the condition of the tool is essential to avoid these additional costs and ensure good production quality. This article explores various classification models, specifically VGG19, EfficientNetV2, and Vision Transformers. These models classify the state of tools using their images. Using transfer learning, a comparison of the best-performing artificial intelligence-based image analysis models is conducted to identify those most suitable for monitoring cutting tools. A comparative analysis of their generalizability, performance and explainability is realized. The model with the best performance is VGG19 with an accuracy of 94%, followed by EfficientNetV2 and ViT with an accuracy of 87%. A full comparison of these results is carried out. © 2025 The Author(s).

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Visual-Language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification 24

Visual-Language Collaborative Representation Network for Bro...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qianyu Ren, Jieji Wang, Haofen Wu, Tianxing Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798400706868

Visual-language models based on CLIP have shown remarkable abilities in general few-shot image classification. However, their performance drops in specialized fields such as healthcare or agriculture, because CLIP's pre-training does not cover all category data. Existing methods excessively depend on the multi-modal information representation and alignment capabilities acquired from CLIP pre-training, which hinders accurate generalization to unfamiliar domains. To address this issue, this paper introduces a novel visual-language collaborative representation network (MCRNet), aiming at acquiring a generalized capability for collaborative fusion and representation of multi-modal information. Specifically, MCRNet learns to generate relational matrices from an information fusion perspective to acquire aligned multi-modal features. This relationship generation strategy is category-agnostic, so it can be generalized to new domains. A class-adaptive fine-tuning inference technique is also introduced to help MCRNet efficiently learn alignment knowledge for new categories using limited data. Additionally, the paper establishes a new broad-domain few-shot image classification benchmark containing seven evaluation datasets from five domains. Comparative experiments demonstrate that MCRNet outperforms current state-of-the-art models, achieving an average improvement of 13.06% and 13.73% in the 1-shot and 5-shot settings, highlighting the superior performance and applicability of MCRNet across various domains. © 2024 ACM.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context on Comfort, Reliance, Satisfaction, and Driving Confidence 25

What Did My Car Say? Impact of Autonomous Vehicle Explanatio...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Kaufman, Robert A Broukhim, Aaron Kirsh, David Weibel, Nadir Cognitive Science University of California San Diego CA United States Computer Science & Engineering University of California San Diego San Diego CA United States Computer Science and Engineering The Design Lab UC San Diego San Diego CA United States

ISBN: (纸本)9798400713941

Explanations for autonomous vehicle (AV) decisions may build trust, however, explanations can contain errors. In a simulated driving study (n = 232), we tested how AV explanation errors, driving context characteristics (perceived harm and driving difficulty), and personal traits (prior trust and expertise) affected a passenger's comfort in relying on an AV, preference for control, confidence in the AV's ability, and explanation satisfaction. Errors negatively affected all outcomes. Surprisingly, despite identical driving, explanation errors reduced ratings of the AV's driving ability. Severity and potential harm amplified the negative impact of errors. Contextual harm and driving difficulty directly impacted outcome ratings and influenced the relationship between errors and outcomes. Prior trust and expertise were positively associated with outcome ratings. Results emphasize the need for accurate, contextually adaptive, and personalized AV explanations to foster trust, reliance, satisfaction, and confidence. We conclude with design, research, and deployment recommendations for trustworthy AV explanation systems. © 2025 Copyright held by the owner/author(s).

关键词： AI Errors Autonomous Vehicles Explainable AI

来源：评论

学校读者我要写书评

暂无评论

Towards Dialogic and On-Demand Metaphors for Interdisciplinary Reading 25

Towards Dialogic and On-Demand Metaphors for Interdisciplina...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Yarmand, Matin Reed, Courtney N. Tandon, Udayan Hekler, Eric B. Weibel, Nadir Wang, April Yi Computer Science and Engineering University of California San Diego La Jolla CA United States Institute for Digital Technologies Loughborough University London London United Kingdom Cwphs Fmph QI & Design Lab University of California San Diego La Jolla CA United States Computer Science and Engineering & the Design Lab University of California San Diego La Jolla CA United States Computer Science Eth Zurich Zurich Switzerland

ISBN: (纸本)9798400713941

The interdisciplinary field of Human-computer Interaction (HCI) thrives on productive engagement with different domains, yet this engagement often breaks due to idiosyncratic writing styles and unfamiliar concepts. Inspired by the dialogic model of abstract metaphors, as well as the potential of Large Language Models (LLMs) to produce on-demand support, we investigate the use of metaphors to facilitate engagement between science and Technology Studies (STS) and System HCI. Our reflective-style survey with early-career HCI researchers (N=48) reported that limited prior exposure to STS research can hinder perceived openness of the work, and ultimately interest in reading. The survey also revealed that metaphors enhance likelihood to continue reading STS papers, and alternative perspectives can build critical thinking skills to mitigate potential risks of LLM-generated metaphors. We lastly offer a specified model of metaphor exchange (within this generative context) that incorporates alternative perspectives to construct shared understanding in interdisciplinary engagement. © 2025 Copyright held by the owner/author(s).

关键词： Large Language Models Metaphor Exchange Reflective Survey

来源：评论

学校读者我要写书评

暂无评论

LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication 38

LibAMM: Empirical Insights into Approximate Computing for Ac...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Xianzhi Jiang, Wenchao Zhang, Shuhao National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Nanyang Technological University Singapore Singapore University of Technology and Design Singapore

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM's effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM's performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM's effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM -a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms 8

Analysis of Sleep apnea Considering Electrocardiogram Data U...

引用

8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Rajabrundha, A. Lakshmisangeetha, A. Balajiganesh, A. Electronic System Design Lab Velammal Engineering College India Department of Computer Science Engineering Velammal Engineering College India Velammal Engineering College Chennai India

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier's ability to distinguish between normal and apnea events is *** Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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：