检索结果-内蒙古大学图书馆

A comprehensive review and open issues on energy aware resource allocation in cloud

International Journal of Web engineering and technology 2023年第4期17卷 353-371页

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus India Department of Computer Science and Engineering Institute of Technology Nirma University India

Cloud computing is an on-demand service resource that includes applications to data centres on a pay-per-use basis. While allocating resources, the node failure causes the cloud service failures. This reduces the quality of service along with ‘extra’ energy consumption. This survey makes an evaluation on 65 papers regarding energy aware resource allocation (RA) techniques in cloud. The performances and associated highest attainments of each contribution are described in this review. In addition, the tools used for implementing the RA techniques in each paper along with type of workloads were considered. We examine a number of current or developed energy-aware RA approaches for cloud settings in this study. We also provide a thorough analysis of the various energy-conscious RA and selection strategies for cloud-based virtual machines. Lastly, the clear sketch of issues and weakness are determined that may be supportive to resolve the existing problems in this aspect. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

A Novel and Optimized Collaborative Diversity-Driven Routing Mechanism in MANETs

引用

SN computer science 2024年第1期5卷 22页

作者： Malyadri, Neelam Ramakrishna, M. Department of Computer Science and Engineering Vemana Institute of Technology VTU Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering Vemana Institute of Technology Karnataka Bengaluru India

Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In this research, we propose a novel routing mechanism called collaborative diversity-driven routing (CollabNet) that integrates collaborative diversity to enhance the routing process in ad hoc networks. CollabNet utilizes a collaborative approach to address the challenges posed by dynamic network environments. CollabNet incorporates collaborative power control, adaptive power allocation, and energy-efficient routing to automatically adjust the dynamic power of nodes. By promoting collaborative behavior and optimizing power levels. It fosters collaborative behavior among nodes by establishing cooperation groups and leveraging multiple relay nodes. Through collaborative multipath diversity CollabNet facilitates the transmission of packets toward the destination via multiple paths within the transmission range. This approach increases the probability of successful packet delivery by establishing redundant connections. By promoting collaboration among nodes, CollabNet improves the reliability and connectivity of wireless ad hoc networks. The mechanism leverages the strength of collaborative diversity to enhance the overall performance of routing protocols. Extensive simulations demonstrate that CollabNet outperforms traditional routing protocols in terms of network connectivity, reliability, and efficiency. The proposed mechanism offers a promising solution for efficient routing in MANETs contributing to the advancement of wireless communication in dynamic ad hoc network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Collaborative diversity Collaborative diversity-driven routing MANETs Routing protocols Wireless ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

An efficient sentiment analysis technique based on fine-tuned EdBERT for virtual learning environments

引用

Multimedia Tools and Applications 2025年第18期84卷 20161-20175页

作者： Srivastav, Gaurav Kant, Shri Srivastava, Durgesh Sharma, Neha Hu, Yu-Chen Department of Computer Science and Engineering Sharda University Gautam Budh Nagar India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

With artificial intelligence propelling rapid technological advances, many tools and frameworks have surfaced to assist virtual learning settings. They all make unique claims about how best to facilitate distance education, particularly in light of the pandemic. This paper presents a new model called "EdBERT," which is created explicitly for sentiment analysis of evaluations written by virtual learners with unmatched categorization accuracy. With Google BERT, EdBERT improves sentiment analysis specifically for educational feedback datasets. Using a comparison analysis and the "fine-tuned Google BERT," taught at several levels to understand language, context, and emotions, the methodology accurately classifies learners' feedback. The discussion includes thorough testing and analysis of the accuracy of the model, as well as information on its effectiveness and operation. The model and its operation are described in the discussion together with reliable evidence from analyses and accuracy tests. EdBERT is a cutting-edge model in AIEd sentiment analysis, boasting the best evaluation matrix to date with accuracy rates of 87.89%, F1-score of 88%, precision of 89%, and recall of 88%. These evaluation matrix values outperform all other current models covered in the article. In an effort to increase the productivity of students, teachers, and the learning environment, academic academics and scientists are delving into the relatively young field of AIEd. Trustworthy sentiment analysis may be produced using three fundamental sentiment classes: deep learning and natural language processing models. Plutchik's wheel of emotion, which will aid in more accurately recording feelings using artificial intelligence and deep learning, can be used to further this study. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Microstructure Estimation Using Synergistic Dual-path Hybrid Network

Microstructure Estimation Using Synergistic Dual-path Hybrid...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Cao, Jian Yang, Junqing Wang, Siqi Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasing research attention in recent years. To conduct effective learning in the heterogeneous space (i.e., x-space and q-space) of dMRI data, hybrid neural networks were proposed and have shown great potential. To this end, we propose a new hybrid neural network, called synergistic dual-path hybrid convolutional neural network (SDH-Net), for more effective microstructure estimation. In our SDH-Net, we propose a dual-path architecture that takes bidirectional asymmetric learning in x-space and q-space to enhance feature representation in heterogeneous domains. Firstly, 3D patches are extracted from each diffusion gradient as vertices, and then a graph is constructed based on the correlation between diffusion angles. In the x-q learning branch, an effective representation is embedded in x-space to enhance learning in q-space, while vice versa in the q-x learning branch. Extensive experiments on data from the human connectome project demonstrate that our SDH-Net outperforms the existing state-of-the-art models. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

引用

7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data

Extended Multi-scale Feature Fusion and Balanced Generative ...

引用

2024 Workshop on Electronics Communication engineering, WECE 2024

作者： Xiao, Ting Sun, Minqian Chen, Haoyuan Li, Jiaqi Qiu, Yixing Wang, Zhe Lu, Dan Department of Computer Science and Engineering East China University of Science and Technology China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University China School of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Image inpainting has made remarkable progress in recent years due to the availability of sufficient training data. However, when the training data is insufficient, i.e., image inpainting in few-shot regimes, the performance of existing image inpainting methods degrades drastically. Two challenges impede image inpainting under few-shot regimes, adequate information and unstable training. To address the above issues, this paper proposes a new method for image inpainting under limited data. Specifically, an Extended Multi-scale Feature Fusion (EMFF) module is proposed to capture representative contextual information by progressively reducing the number of channels for features with increasing receptive fields, thereby utilizing effective information. Additionally, we leverage a multi-scale discriminator whose cross-layer features are mixed for supplementation to formulate a Balanced Generative Adversarial Network (BGAN) to stabilize the training dynamics. Comprehensive experiments demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Image fusion Image quality Feature fusion Education and training Image restoration Convolution Data modeling Image enhancement Gallium nitride Semantics

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

引用

Tsinghua science and technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

来源：评论

学校读者我要写书评

暂无评论

In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

引用

Journal of Energy Chemistry 2024年第1期88卷 282-292,I0007页

作者： Huanhui Chen Xing Cao Moujie Huang Xiangzhong Ren Yubin Zhao Liang Yu Ya Liu Liubiao Zhong Yejun Qiu Shenzhen Engineering Lab of Flexible Transparent Conductive Films School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen 518055GuangdongChina Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen 518055GuangdongChina College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060GuangdongChina School of Resource and Environmental Sciences Wuhan UniversityWuhan 430072HubeiChina

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li ***,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state ***,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion *** the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li *** expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical *** Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 ***,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical *** present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

关键词： Solid-state lithium batteries Composite solid electrolyte In-situ polymerization Interfacial passivation layer Self-adaptability

来源：评论

学校读者我要写书评

暂无评论

Characterizing and Stage-Wise Differentiation of Coal Spontaneous Combustion in Deep Mines

引用

Energy engineering 2025年第4期122卷 1561-1580页

作者： Haitao Wang Pengxin Zhang Weihao Li Baogang Li Xianghui Xiong School of Resources and Engineering Department Heilongjiang University of TechnologyJixi158100China Intelligent Mine Interdisciplinary Research Institute Heilongjiang University of Science&TechnologyHarbin150022China

Deep mining,characterized by high stress,elevated geothermal gradients,and significant moisture content,significantly increases the risk of Coal Spontaneous Combustion(CSC),posing a major threat to mine *** study delves into the impact of these factors on the self-ignition properties of coal,leveraging data from four distinctmines inHeilongjiang Province,China:ShuangyashanDongrongNo.2 Mine,Hegang JundeCoal Mine,Qitaihe Longhu Coal Mine,and Jixi Ronghua *** have honed the theoretical framework to account for variations in gas content during *** investigation,conducted through programmed temperature rise experiments,scrutinized the generation and temperature-dependent evolution of gases,emphasizing individual indicators such as CO,O_(2),and CxHy,in addition to composite indicators like the ratio of change in CO to change in O_(2) concentration(∂C_(CO)/∂t:−∂C_(O_(2))/∂t)and the ratio of C2H4 to C_(2)H_(6).These insights have catalyzed the development of a CSC state energy level transition model and a precise method for phase-based quantification of combustion *** findings furnish a scientific foundation for the formulation of early warning and prevention strategies in deep mining settings.

关键词： Deep mine coal spontaneous combustion gas change rate index threshold

来源：评论

学校读者我要写书评

暂无评论

Synergistic coupling among Mg_(2)B_(2)O_(5),polycarbonate and N,Ndimethylformamide enhances the electrochemical performance of PVDF-HFP-based solid electrolyte

引用

Journal of Energy Chemistry 2024年第7期94卷 158-168页

作者： Yutong Jing Qiang Lv Yujia Chen Bo Wang Bochen Wu Cheng Li Shengbo Yang Zhipeng He Dianlong Wang Huakun Liu Shixue Dou MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage State Key Laboratory of Urban Water Resource and EnvironmentSchool of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001HeilongjiangChina Institute of Energy Material Science University of Shanghai for Science and TechnologyShanghai 200093China Institute for Superconducting&Electronic Materials Australian Institute of Innovative MaterialsUniversity of WollongongWollongongNSW 2500Australia

Polymer solid electrolytes(SPEs)based on the[solvate-Li+]complex structure have promising prospects in lithium metal batteries(LMBs)due to their unique ion transport ***,the solvation structure may compromise the mechanical performance and safety,hindering practical application of *** this work,a composite solid electrolyte(CSE)is designed through the organic-inorganic syner-gistic interaction among N,N-dimethylformamide(DMF),polycarbonate(PC),and Mg_(2)B_(2)O_(5) in poly(vinylidene fluoride-co-hexafluoropropylene)(PVDF-HFP).Flame-retardant Mg_(2)B_(2)O_(5) nanowires provide non-flammability to the prepared CSEs,and the addition of PC improves the dispersion of Mg_(2)B_(2)O_(5) ***,the organic-inorganic synergistic action of PC plasticizer and Mg_(2)B_(2)O_(5) nanowires pro-motes the dissociation degree of LiTFSI and reduces the crystallinity of PVDF-HFP,enabling rapid Li ion ***,Raman spectroscopy and DFT calculations confirm the coordination between Mg atoms in Mg_(2)B_(2)O_(5) and N atoms in DMF,which exhibits Lewis base-like behavior attacking adjacent C-F and C-H bonds in PVDF-HFP while inducing dehydrofluorination of *** on the syner-gistic coupling of Mg_(2)B_(2)O_(5),PC,and DMF in the PVDF-HFP matrix,the prepared CSE exhibits superior ion conductivity(9.78×10^(-4) s cm^(-1)).The assembled Li symmetric cells cycle stably for 3900 h at a current density of 0.1 mA cm^(-2) without short *** LFP||Li cells assembled with PDL-Mg_(2)B_(2)O_(5)/PC CSEs show excellent rate capability and cycling performance,with a capacity retention of 83.3%after 1000 cycles at 0.5 *** work provides a novel approach for the practical application of organic-inorganic Synergistic CSEs in LMBs.

关键词： Composite solid electrolytes Safe Li metal batteries Synergistic coupling effect Poly(vinylidene fluoride-co-hexafluoropro pylene)

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：