检索结果-内蒙古大学图书馆

Investigating Routing in the VANET Network: Review and Classification of Approaches

Algorithms 2023年第8期16卷 381-381页

作者： Sangaiah, Arun Kumar Javadpour, Amir Hsu, Chung-Chian Haldorai, Anandakumar Zeynivand, Ahmad International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Harbin Institute of Technology Shenzhen518057 China ADiT-Lab Electrical and Telecommunications Department Instituto Politécnico de Viana do Castelo Viana do Castelo4900-367 Portugal Department of Information Management International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore642109 India Department of Electrical & Computer Engineering Tarbiat Modares University Tehran14115-111 Iran

Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems in VANET. One of the machine learning categories is reinforcement learning (RL), which uses RL algorithms to find a more optimal path. According to the feedback they get from the environment, these methods can affect the system through learning from previous actions and reactions. This paper provides a comprehensive review of various methods such as reinforcement learning, deep reinforcement learning, and fuzzy learning in the traffic network, to obtain the best method for finding optimal routing in the VANET network. In fact, this paper deals with the advantages, disadvantages and performance of the methods introduced. Finally, we categorize the investigated methods and suggest the proper performance of each of them. © 2023 by the authors.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Pipe-BD: Pipelined Parallel Blockwise Distillation

arXiv

引用

arXiv 2023年

作者： Jang, Hongsun Jung, Jaewon Song, Jaeyong Yu, Joonsang Kim, Youngsok Lee, Jinho Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Artificial Intelligence Yonsei University Korea Republic of Department of Computer Science Yonsei University Korea Republic of CLOVA ImageVision CLOVA AI Lab NAVER Korea Republic of

Training large deep neural network models is highly challenging due to their tremendous computational and memory requirements. Blockwise distillation provides one promising method towards faster convergence by splitting a large model into multiple smaller models. In state-of-the-art blockwise distillation methods, training is performed block-by-block in a data-parallel manner using multiple GPUs. To produce inputs for the student blocks, the teacher model is executed from the beginning until the current block under training. However, this results in a high overhead of redundant teacher execution, low GPU utilization, and extra data loading. To address these problems, we propose Pipe-BD, a novel parallelization method for blockwise distillation. Pipe-BD aggressively utilizes pipeline parallelism for blockwise distillation, eliminating redundant teacher block execution and increasing per-device batch size for better resource utilization. We also extend to hybrid parallelism for efficient workload balancing. As a result, Pipe-BD achieves significant acceleration without modifying the mathematical formulation of blockwise distillation. We implement Pipe-BD on PyTorch, and experiments reveal that Pipe-BD is effective on multiple scenarios, models, and datasets. © 2023, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report 17th

Efficient Single-Image Depth Estimation on Mobile Devices, ...

引用

17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Treszczotko, Lukasz Chang, Xin Ksiazek, Piotr Lopuszynski, Michal Pioro, Maciej Rudnicki, Rafal Smyl, Maciej Ma, Yujie Li, Zhenyu Chen, Zehui Xu, Jialei Liu, Xianming Jiang, Junjun Shi, XueChao Xu, Difan Li, Yanan Wang, Xiaotao Lei, Lei Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Li, Jiaqi Wang, Yiran Huang, Zihao Cao, Zhiguo Conde, Marcos V. Sapozhnikov, Denis Lee, Byeong Hyun Park, Dongwon Hong, Seongmin Lee, Joonhee Lee, Seunggyu Chun, Se Young Computer Vision Lab ETH Zürich Zürich Switzerland AI Witchlabs Zollikerberg Switzerland University of Wuerzburg Wuerzburg Germany TCL Research Europe Warsaw Poland Harbin Institute of Technology Harbin China Xiaomi Inc. Beijing China Tencent GY-Lab Shenzhen China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031250651

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 m. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach

arXiv

引用

arXiv 2024年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In School of Computer and Information Engineering Henan Normal University Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China College of Computing and Data Science Nanyang Technological University Singapore School of Electrical Engineering and Telecommunications University of New South Wales NSW2052 Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem. In the formulated problem, beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices are jointly optimized subject to constraints on total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, to further simplify the design, we adopt the maximum ratio transmission (MRT) and zero-forcing (ZF) as beamforming vectors in the action space. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches in terms of achievable SEE and significantly improves convergence speed compared to the original PPO approach. Moreover, implementing the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2024, CC BY.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

6G Massive Radio Access Networks: Key Applications, Requirements and Challenges

IEEE Open Journal of Vehicular Technology

引用

IEEE Open Journal of Vehicular Technology 2021年 2卷 54-66页

作者： Lee, Ying Loong Qin, Donghong Wang, Li-Chun Sim, Gek Hong Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Selangor Malaysia School of Artificial Intelligence Guangxi University for Nationalities Guangxi China Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan SEEMOO Lab Technische Universität Darmstadt Darmstadt Germany

Driven by the emerging use cases in massive access future networks, technological advancements and evolutions are needed for wireless communications beyond the fifth-generation (5G) networks. In particular, we envisage that the upcoming sixth-generation (6G) networks with numerous devices will demand extremely high-performance interconnections, even under strenuous scenarios such as diverse mobility, extreme density, and dynamic environments. To cater to such a demand, an investigation of flexible and sustainable radio access network (RAN) techniques to support highly diverse requirements and massive connectivity is of utmost importance. To this end, this paper first presents the key driving applications for 6G, including smart cities and factories, unmanned aerial vehicles, and multi-dimensional sensing services, which necessitate the transformation of existing RAN techniques to achieve the key performance indicators required for 6G networks. In particular, we investigate the flexibility, massive interconnectivity, and energy efficiency aspects of 6G RANs and provide an in-depth discussion on the potential key enabling technologies. Last but not least, we present an artificial-intelligence-assisted network architecture for flexible 6G massive RANs with energy harvesting. © 2020 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Generative Physical AI in Vision: A Survey

arXiv

引用

arXiv 2025年

作者： Liu, Daochang Zhang, Junyu Dinh, Anh-Dung Park, Eunbyung Zhang, Shichao Xu, Chang School of Physics Mathematics and Computing The University of Western Australia CrawleyWA6009 Australia School of Computer Science and Engineering Central South University Changsha410083 China School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Department of Artificial Intelligence Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China

Generative Artificial intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. Traditionally, generative models primarily focus on visual fidelity while often neglecting the physical plausibility of generated content. This gap limits their effectiveness in applications requiring adherence to real-world physical laws, such as robotics, autonomous systems, and scientific simulations. As generative AI evolves to increasingly integrate physical realism and dynamic simulation, its potential to function as a "world simulator" expands—enabling the modeling of interactions governed by physics and bridging the divide between virtual and physical realities. This survey systematically reviews this emerging field of physics-aware generative AI in computer vision, categorizing methods based on how they incorporate physical knowledge—either through explicit simulation or implicit learning. We analyze key paradigms, discuss evaluation protocols, and identify future research directions. By offering a comprehensive overview, this survey aims to help future developments in physically grounded generation for vision. The reviewed papers are summarized at https://***/BestJunYu/Awesome-Physics-aware-Generation. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions

arXiv

引用

arXiv 2023年

作者： Hazra, Abhishek Morichetta, Andrea Murturi, Ilir Lovén, Lauri Dehury, Chinmaya Kumar Pujol, Víctor Casamayor Donta, Praveen Kumar Dustdar, Schahram Department of Computer Science and Engineering the Indian Institute of Information Technology Sri City India Communications & Networks Lab Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore Distributed Systems Group TU Wien Vienna1040 Austria Center for Ubiquitous Computing University of Oulu 90014 Finland Institute of Computer Science University of Tartu Tartu51009 Estonia

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines distributed Artificial intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Active Vibration Reduction for the Autonomous Observation Mechanism

Active Vibration Reduction for the Autonomous Observation Me...

引用

Control Technology and Applications (CCTA),

作者： Fatemeh Rekabi-Bana Mahmood Saadat Nahid Kalantaryardebily Martin Stefanec Laurenz A. Fedotoff Honghao Pan Tomáš Krajník Thomas Schmickl Farshad Arvin Department of Computer Science Swarm & Computational Intelligence Laboratory (SwaCIL) Durham University UK Faculty of Engineering Mechanics Virginia Tech Blacksburg Virginia United States of America Artificial Life Lab University of Graz Austria Faculty of Electrical Engineering Czech Technical University Czechia

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

This paper presents a method applied for vibration reduction in an autonomous mechanism developed for robot-insect interaction. The mechanism performs continuous data collection of honeybee behaviour within a living honeybee colony. To ensure sufficient quality of the observations, the system has to be installed in proximity to the hive. However, as honeybees are particularly sensitive to vibrations, it is important to keep the vibrations produced by the mechanism below the values that can disturb the natural behaviour of the bees. Furthermore, avoiding vibrations improves the gathered data quality, decreases the maintenance frequency and prolonges the system service life. In this paper, we present the robotic observation mechanism’s development process of vibration control. The subspace identification method is applied to determine a model utilised to design a controller to actively reduce the resulting vibration while preserving the position tracking accuracy. The results of the experiments demonstrate a reduction of about 10dB in the vibration after applying the suggested controller.

关键词： Vibrations Power measurement Mechatronics Process control Vibration measurement Aerospace electronics Position measurement

来源：评论

学校读者我要写书评

暂无评论

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

arXiv

引用

arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

引用

IEEE Symposium Series on Computational intelligence (SSCI)

作者： Ehsan Rokhsatyazdi Shahryar Rahnamayan Sevil Zanjani Miyandoab Azam Asilian Bidgoli H.R. Tizhoosh Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Artificial Intelligence and Informatics Mayo Clinic Rhazes Lab Rochester MN USA

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词：

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：