检索结果-内蒙古大学图书馆

19th International Conference on Aviation and Cosmonautics, AviaSpace 2020

作者： Chemodanov, V. Gavrilin, K. Klimov, I. Kotelnikova, A. Faculty of Control Systems Computer Science and Power Engineering Moscow Aviation Institute 4 Volokolamskoe highway Moscow125993 Russia

This paper discusses the main approaches to the development and construction of hybrid flying multi-rotor platforms with separation of lift control functions and angular stabilization functions, called by the authors "distributed load". A significant factor that limits flight time for majority of multi-rotor platforms is the energy density limit of lithium polymer batteries, which are most commonly used. To solve this problem we analyzed possible ways to increase flight time: replacing the main type of energy carrier for a more energy-intensive one and use of hybrid systems with combination of popular lithium-polymer batteries and a more energy-intensive energy carrier. The analysis was supported by experimental data. © Published under licence by IOP Publishing Ltd.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

A Review of Gateway Load Balancing Methods in Connecting MANET into Internet 23

A Review of Gateway Load Balancing Methods in Connecting MAN...

引用

23rd International Conference on Advanced Communication Technology, ICACT 2021

作者： Hui, Zhang Lingli, Zhang Yonghang, Yan Linlin, Ci School of Computer Science Technology Beijing Institute of Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9791188428069

Interconnection of wired Internet and Mobile Ad hoc Network (MANET), called Integrated Internet-MANET can expand the network coverages and services. In this type of networks, the gateway acts as a bridge between MANET and wired Internet. As all data packets must pass through gateway, which is likely to be congested due to uneven traffic distribution. Thus, gateway load balancing become one of the most important issues in MANET. When the gateway is transmitting a large number of packets, load Balancing is the process of improving the performance of a network through a distribution of load among different gateways. If a gateway is heavily loaded without any load balancing capability, it degrades the performance by causing congestion, delay and resource loss in network. Recently, the key issue of gateway load balancing has been addressed by researchers in various ways, but now there aren't systematic analysis and comparison among different strategies. This paper provides the complete survey on load balancing solutions and proposes a gateway load balancing strategy comparison framework. © 2021 Global IT Research institute (GiRI).

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

引用

2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A heuristic mixed real-time task allocation of virtual utilization in multi-core processor

引用

Journal of Information and Intelligence 2023年第2期1卷 156-177页

作者： Hongbiao Liu Mengfei Yang Tingyu Wang Chenghao Song Shenghui Zhu Xi Chen School of Computer Science and Technology Xidian UniversityXi'an 710071China China Academy of Space Technology Beijing 100094China Beijing Institute of Control Engineering Beijing 100190China

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft,and various types of real-time tasks are dynamically added at *** order to improve the utilization of multi-core processors and ensure the real-time performance of the system,it is necessary to adopt a reasonable real-time task allocation method,but the existing methods are only for single-core processors or the performance is too low to be *** at the task allocation problem when mixed real-time tasks are dynamically added,we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF(Virtual Utilization Worst Fit)in multi-core ***,a 4-tuple task model is established to describe the fixedpoint task and the sporadic task in a unified ***,a VDS(Virtual Deferral Server)for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is ***,combined with the analysis of VDS's capacity,VU-WF is proposed,which selects cores in ascending order of virtual utilization for the schedulability *** show that the overall performance of VU-WF is better than available algorithms,not only has a good schedulable ratio and load balancing but also has the lowest runtime *** a 4-core processor,compared with available algorithms of the same schedulability ratio,the load balancing is improved by 73.9%,and the runtime overhead is reduced by 38.3%.In addition,we also develop a visual multi-core mixed task scheduling simulator RT-MCSS(open source)to facilitate the design and verification of multi-core scheduling for *** the high performance,VU-WF can be widely used in resource-constrained and safety-critical real-time systems,such as spacecraft,self-driving cars,industrial robots,etc.

关键词： Mixed real-time task set Multi-core scheduling Task dynamical adding Virtual utilization Scheduling simulator

来源：评论

学校读者我要写书评

暂无评论

Differentially Private Online Federated Learning with Correlated Noise

arXiv

引用

arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (ϵ, δ)DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Distance-Based IoU Loss for Accurate and Efficient Bounding Box Regression

SSRN

引用

SSRN 2024年

作者： Gupta, Himanshu Verma, Sourabh Verma, Om Prakash Department of Computer Engineering-Artificial Intelligence Marwadi University Gujrat Rajkot360003 India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India

Bounding box regression (BBR) has been considered the most decisive step in object detection, continues to make breakthroughs in wide real-time applications of computer vision and directly affects the localisation performance of deep learning models. Usually, existing BBR employs Intersection over Union (IoU)-based loss function, especially Complete IoU (CIoU). However, it shows inefficiency in depicting the objective of BBR, which leads to slow convergence and inaccurate regression performance due to poor aspect ratio penalty and normalised Euclidean distance in the denominator. To overcome these limitations, the present study proposes an Adaptive distance-based IoU (ADIoU) loss function by excluding the distance term in backpropagation and expanding the penalty in CIoU, which further improves the object detection accuracy. The effectiveness of the proposed ADIoU has been verified on the simulated data. Also, ADIoU has been incorporated into YOLOv5, YOLOv4, and RetinaNet to estimate its effectiveness on popular ground-view (COCO) and aerial-view (VisDrone, AU-AIR, and Airbus) object detection datasets. The experimental results reveal that relative to CIoU, ADIoU improves the mAPcoco by 0.68% to 1.66% for ground-view and 0.39% to 5.53% for aerial-view datasets, which demonstrates that ADIoU has strong robustness and can be considered a general BBR loss function. © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network 5th

Towards Efficient and Privacy-Preserving Hierarchical Federa...

引用

5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： An, Ningyu Liang, Xiao Zhou, Fei Wang, Xiaohui Li, Zihan Feng, Jia Guan, Zhitao State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd Beijing100209 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Zhejiang Electric Power Company.Ltd Hangzhou310007 China

ISBN: (纸本)9789819981007

Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge devices, existing federated learning schemes still suffer from heterogeneity challenge. Hierarchical federated learning divides devices into clusters based on their resources to enhance training efficiency. In addition, there are also security concerns such as the risk of privacy leakage during the interactions intra and cross the layers. In this paper, we propose PHFL, a Privacy-Preserving Hierarchical Federated Learning Scheme, which includes a personalized task assignment protocol and a heterogeneous-friendly aggregation scheme, so as to achieve an efficient and low-cost training for resource-constrained edge devices. To address the heterogeneity challenge, we combine synchronous and asynchronous aggregation, and design different aggregation weights for device differences. On top of that, a privacy-preserving scheme based on threshold homomorphic encryption is designed to accommodate the privacy requirements of the proposed hierarchical federated learning scenario. The security analysis and performance evaluation demonstrate that this approach improves model generalizability and learning efficiency with acceptable overhead, while also providing privacy protection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging protein-protein interactions in phenotype prediction through graph neural networks 21

Leveraging protein-protein interactions in phenotype predict...

引用

21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Smeriglio, Riccardo Rosell-Mirmi, Joana Radeva, Petia Abante, Jordi Universitat de Barcelona Dpt. of Physics Spain Politecnico di Torino Control and Computer Engineering Dpt. Turin Italy Headache Research Group Vall d'Hebron Institut de Recerca Catalonia Barcelona Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Biomedical Sciences Spain Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain

ISBN: (纸本)9798350356632

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases. © 2024 IEEE.

关键词： Prediction models

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：