检索结果-内蒙古大学图书馆

IEEE International Symposium on Information Theory

作者： Michael Kohler Benjamin Walter Adam Krzyżak Fachbereich Mathematik Technische Universität Darmstadt Darmstadt Germany Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Image classifiers based on over-parametrized deep convolutional neural networks with an average-pooling are proposed. The weights of the network are learned by gradient descent. We present the bound on the rate of convergence of the difference between the expected misclassification risk of the plug-in classifier and the Bayes risk. The obtained rate of convergence is independent of image dimension under appropriate constraints on the image distribution.

关键词： Transformers Planning Bayes methods Convolutional neural networks Convergence Information theory

来源：评论

学校读者我要写书评

暂无评论

Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

引用

npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

来源：评论

学校读者我要写书评

暂无评论

Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

引用

computers, Materials & Continua 2021年第2期66卷 1613-1629页

作者： G.Arun Sampaul Thomas Y.Harold Robinson E.Golden Julie Vimal Shanmuganathan Seungmin Rho Yunyoung Nam CSE Department J.B.Institute of Engineering and TechnologyHyderabad500075India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Anna University Regional CampusTirunelveli627007India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Software Sejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision *** of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked *** this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate *** CNN model is trained by different images of eyes that have retinopathy and those which do not have *** fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent *** feature loss factor increases the label value to identify the patterns with the kernel-based *** performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and *** results show that the proposed CNN performs better.

关键词： Convolutional neural networks dental diagnosis image recognition diabetic retinopathy detection

来源：评论

学校读者我要写书评

暂无评论

Neural network-driven parallel accelerated selective image encryption with semantic understanding

引用

The European Physical Journal Special Topics 2025年 1-16页

作者： Song, Wei Fu, Chong Zheng, Yu Chen, Junxin Zhang, Yanfeng School of Computer Science and Engineering Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China University of California Irvine Irvine USA School of Software Dalian University of Technology Dalian China

Existing batch image encryption schemes often directly encrypt images without considering their source. However, in real-life scenarios, these images are typically part of a larger collection stored in a directory on a computer or in the cloud. To efficiently protect those sensitive images distributed within the collection, we rethink selective image encryption (SIE) from the perspective of batch image encryption and proposes a novel SIE scheme using deep neural networks (DNN) and parallel computing. First, a DNN method is employed to identify sensitive images by analyzing their semantics. Next, a parallel cross-bitplane permutation algorithm is introduced to shuffle the retrieved images in bit-level. Then, a bi-directional diffusion strategy is employed to spread the encryption influence of a pixel to all images. Experimental results demonstrate the proposed scheme effectively understands the semantic of images, identifies sensitive images, and encrypts them with high permutation and diffusion efficiency. Further security analyses show that the proposed algorithm successfully resists various attacks with desired performance, including achieving high information entropy and low correlation coefficients in statistical analysis, and ideal values of the number of pixels changing rate (NPCR) and the unified averaged changing intensity (UACI) to resist differential attack, as well as high key sensitivity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

引用

International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shehu Mohammed Arun Singh Md Abu Hanif Department of Computer Applications Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science Graduate Center The City of New York (CUNY) New York NY USA

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices..

关键词： Precision agriculture Technological innovation Scalability Decision making Transforms Soil Big Data Quality assessment Environmental management Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Radiology Report Generation via Structured Knowledge-Enhanced Multi-modal Attention and Contrastive Learning

Radiology Report Generation via Structured Knowledge-Enhance...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dexuan Xu Yanyuan Chen Jiayu Zhang Yiwei Lou Hanpin Wang Jing He Yu Huang National Engineering Research Center for S.E. School of Software & Micro. Peking University China School of Computer Science Peking University China Key Laboratory of High Confidece Software Technologies (MOE) School of Computer Science Peking University China Department of Rheumatology and Immunology Peking University People’s Hospital China National Engineering Research Center for Software Engineering Peking University China

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks

Energy-Efficient Client Sampling for Federated Learning in H...

引用

IEEE International Conference on Communications (ICC)

作者： Jian Tang Xiuhua Li Hui Li Min Xiong Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Training Energy consumption Multi-access edge computing Federated learning Computational modeling Simulation Sampling methods

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Blockchain-Based Cooperative Demand Response and Energy Trading Model for Smart Grid

A Lightweight Blockchain-Based Cooperative Demand Response a...

引用

2023 Power Electronics and Power System Conference, PEPSC 2023

作者： Ayenew, Melak Lei, Hang Li, Xiaoyu Kekeba, Kulla Abeje, Eyerusalem Tegene, Abebe Alemu, Habtamu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Addis Ababa Science and Technology University Center of Big Data Analytics and Hpc Addis Ababa Ethiopia Kotebe Education University Collage of Natural Department of Mathematic Science Shared Campus Addis Ababa Ethiopia

ISBN: (纸本)9798350341294

Public blockchain networks have been used for peer-to-peer energy trading;however, most of these networks are inefficient. The power-intensive consensus algorithm, Proof-of-Work (PoW), is one of the reasons for power inefficiency. This article proposes a lightweight consensus algorithm for demand response and energy trading models to improve efficiency in the smart grid. A cooperative approach encompassing all energy entities is also necessary for demand response management strategies to be more efficient. In the proposed model, we redesign the Proof-of-Fit (PoF) consensus algorithm for a smaller number of message exchanges and computations. We also propose a service-based use of grid infrastructure for energy trading and exchange by all energy trading entities in a cooperative approach while free energy price negotiation is exercised. The simulation results show that the redesigned consensus algorithm, Energy-Proof-Fit, reduces the average number of message broadcasts required to complete a consensus by 35 percent. Furthermore, the peak-to-average ratio of peak hour demand is reduced in the proposed model. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Robust Vietnam’s Motorcycle License Plate Detection and Recognition Using Deep Learning Model 10th

Robust Vietnam’s Motorcycle License Plate Detection and Re...

引用

10th International Conference on Future Data and Security engineering, FDSE 2023

作者： Le, Duc Hoa Mazumder, Debarshi Quach, Luyl-Da Banerjee, Shreya Nguyen, Vinh Dinh Bosch Global Software Technologies Company Limited Ho Chi Minh city Viet Nam School of Computing and Information Technology Eastern International University Thu Dau Mot Viet Nam Department of Software Engineering FPT University Can Tho City Can Tho Viet Nam Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819982950

Nowadays, driving fast is an emergency state in many countries in the world and Vietnam in particular, and the consequence is really cruel when an accident occurs. Therefore, it is very important to control the speed of vehicles. Motorcycle license plate recognition (MLPR) is one of many methods. If any motorcycle drives above the speed limit on the road that has a speed camera, a ticket will be sent to your living address. This research proposes a method that can automatically detect license plates (LP) and extract their data. The highest mAP achieved after three hundred epochs is 93% with Yolov8. There are three stages to extracting the LP’s data, the first is detecting the motorcycle, the second is detecting the LP in the motorcycle’s bounding box, and the third is LP recognition using Yolov8 also. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Digital storage

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：