检索结果-内蒙古大学图书馆

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Xiaoling Liu, Yang Schwartz, Skyler Guo, Ning Karimi, Ebrahim Mantooth, H. Alan Graber, Lukas Cuzner, Robert University of Arkansas Department of Electrical Engineering Fayetteville United States Georgia Institute of Technology School of Electrical & Computer Engineering Atlanta United States University of Wisconsin-Milwaukee College of Engineering and Applied Science Milwaukee United States

ISBN: (纸本)9798350316445

Future all-electric ships will likely use a Medium Voltage DC (MVDC) power distribution system, which has several advantages over MVAC, such as higher power density and higher reliability. As key components for power distribution systems, 10 kV power modules need to meet the insulation requirements for various fault conditions. Grounding faults can cause extremely high electric stress between the module terminals and baseplate hence resulting in surface flashover breakdown. This paper aimed at the insulation challenges of 10 kV power module for the MVDC shipboard application. The maximum voltage stresses of four typical grounding schemes are compared at single line-to-ground faults. The surface flashover tolerance of a 10 kV SiC power module is verified under steady DC voltage and the worst-case transient fault overvoltage. The experimental results can provide a reference for the MV power module insulation design and the module baseplate grounding schemes. © 2023 IEEE.

关键词： 10 kV power module MVDC shipboard power system surface flashover

来源：评论

学校读者我要写书评

暂无评论

The Impact of Data Augmentation on Sentiment Analysis of Translated Textual Data

The Impact of Data Augmentation on Sentiment Analysis of Tra...

引用

2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Omran, Thuraya Sharef, Baraa Grosan, Crina Li, Yongmin Brunel University London Department of Computer Science Uxbridge United Kingdom Ahlia University Department of Information Technology Manama Bahrain King's College London Division of Applied Technologies for Clinical Care Strand United Kingdom

ISBN: (纸本)9781665463720

Sentiment analysis is an application of natural language processing that requires an abundance of data that may not be achieved sometimes for some reason. Data augmentation is one technique that deals with the lack of data by creating synthetic training data without adding new ones. It boosts model performance, especially with deep learning ones. Despite its influential role in boosting the model performance, it attracted very little attention from the researchers of the Arabic NLP community, specifically with scarce language resources such as Arabic and its dialects. In this study, one of the augmentation techniques called random swap was applied with LSTM deep learning model to classify three parallel datasets. The three parallel datasets are Bahraini dialects, Modern Standard Arabic and English. The results show an improvement in the LSTM model by 14.06%, 12.57%, and 11.04% on Bahraini dialects, Modern Standard Arabic, and English datasets, respectively, when applying the augmentation technique over that of no application. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Method for evaluating the monetary added value of the usage of a digital twin for additive manufacturing 16

Method for evaluating the monetary added value of the usage ...

引用

16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Oettl, Fabio Hörbrand, Sebastian Wittmeir, Tobias Schilp, Johannes Chair of Digital Manufacturing Faculty of Applied Computer Science Augsburg University Am Technologiezentrum 8 Augsburg86159 Germany Fraunhofer Research Institution of Casting Composite and Processing Technology Am Technologiezentrum 10 Augsburg86159 Germany

By combining the additive manufacturing (AM)-process with digital concepts, such as the digital twin (DT) or the digital part file (DPF), the competitiveness of additive manufacturing is increased. A quantitative approach to evaluate the usage of a DPF in AM will be introduced within this paper. The focus is set on the production as an early lifecycle-phase, which means that the AM-production process gets analyzed regarding potential advantages of using a DPF. These advantages are transferred into a monetary value with our approach. By calculating the total costs of the DPF, a monetary overall value is obtained. © 2023 Elsevier B.V.. All rights reserved.

关键词： 3D printing

来源：评论

学校读者我要写书评

暂无评论

Automating the Selection of Proxy Variables of Unmeasured Confounders 41

Automating the Selection of Proxy Variables of Unmeasured Co...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Xie, Feng Chen, Zhengming Luo, Shanshan Miao, Wang Cai, Ruichu Geng, Zhi Department of Applied Statistics Beijing Technology and Business University Beijing China School of Computer Science Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Probability and Statistics Peking University Beijing China

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach. Copyright 2024 by the author(s)

关键词： Higher order statistics

来源：评论

学校读者我要写书评

暂无评论

GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

引用

computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

来源：评论

学校读者我要写书评

暂无评论

P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices

P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices

引用

International Conference on Network and Service Management

作者： Fabian Brisch Andreas Kassler Sándor Laki Peter Hudoba Institute of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany Computer Science Department Karlstads Universitet Karlstad Sweden Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

The current P4 programmability model assumes that a P4 programmable device is owned and controlled by a single tenant. However, in typical NFV scenarios, support for multiple tenants is desirable. When each tenant may want to deploy their own P4 pipeline offering different network functions (NF), supporting multiple co-existing tenant pipelines on a single platform is difficult because it requires pipeline merging, control plane support, and resource management of the platform. In this paper, we present P4-MTAGG, a novel framework for flexibly deploying multiple P4 programmable NFs on a programmable match-action pipeline while supporting multiple tenants. P4-MTAGG consists of i) novel compiler-add-ons for automatic merging multiple P4-pipelines, ii) p4runtime-proxy to allow for control plane access of the aggregated pipelines together with policy-based resource management for the P4 target, and iii) orchestrator to automate the provisioning of a network node utilizing aggregation either in a simulated or real hardware environment. In this demo, we show how P4-MTAGG aggregates multiple NFs of varying complexity in Mininet. The user can orchestrate the aggregation process through a GUI. The per-tenant traffic is routed through the set of NFs using segment routing. Through the GUI, the user can instruct the p4runtime-proxy to enforce per-tenant bandwidth limits, which configure the per-tenant available resources in the data plane.

关键词： Aggregates Pipelines Merging Bandwidth Routing Hardware Complexity theory Resource management Noise measurement Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Enabling Deep Reinforcement Learning Autonomous Driving by 3D-LiDAR Point Clouds 14

Enabling Deep Reinforcement Learning Autonomous Driving by 3...

引用

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Chen, Yuhan Tse, Rita Bosello, Michael Aguiari, Davide Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China Department of Computer Science and Engineering - DISI University of Bologna Cesena Italy Samueli Computer Science University of California Los Angeles United States

ISBN: (纸本)9781510657564

Autonomous driving holds the promise of revolutionizing our lives and society. Robot drivers will run errands such as commuting, parking cars, or taking kids to school. It is expected that, by the mid-century, humans will drive only for their pleasure. Autonomous vehicles will increase the efficiency and safety of the transportation system by reducing accidents and increasing the overall system capacity. Current autonomous driving systems are based on supervised learning that relies on massive, labeled data. It takes a lot of time, resources, and manpower to produce such data sets. While this approach is achieving remarkable results, the required effort to produce data becomes a limiting factor for general driving scenarios. This research explores Reinforcement Learning to advance autonomous driving models without labeled data. Reinforcement Learning is a learning paradigm that uses the concept of rewards to autonomously discover, through trial & error, how to solve a task. This work uses the LiDAR sensor as a case study to explore the effectiveness of Reinforcement Learning in interpreting complex data. LiDARs provide a dynamic high time-space definition map of the environment and it could be one of the key sensors for autonomous driving. © 2022 SPIE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

引用

IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

引用

computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major ***,these must be addressed utilizing proper *** on a random search space,optimization algorithms can find acceptable solutions to *** Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this *** fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target *** proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the *** AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective ***,the proposed algorithm’s performance is compared *** approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm *** to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these *** analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

来源：评论

学校读者我要写书评

暂无评论

Strategies of Multi-Step-ahead Forecasting for Chaotic Time Series using Autoencoder and LSTM Neural Networks: A Comparative Study 23

Strategies of Multi-Step-ahead Forecasting for Chaotic Time ...

引用

5th International Conference on Image Processing and Machine Vision, IPMV 2023

作者： Nguyen, Ngoc Phien Duong, Tuan Anh Jan, Platos Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Czech Republic Department of IT Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9781450397926

There has been a lot of research on the use of deep neural networks in forecasting time series and chaotic time series data. However, there exist very few works on multi-step ahead forecasting in chaotic time series using deep neural networks. Several strategies that deal with multi-step-ahead forecasting problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, a combination of both the recursive and direct strategies, called DirRec, the Multiple-Input Multiple-Output (MIMO) strategy, and the fifth strategy, called DirMO which combines Direct and MIMO strategies. This paper aims to propose a new deep learning model for chaotic time series forecasting: LSTM-based stacked autoencoder and answer the research question: which strategy for multi-step ahead forecasting using LSTM-based stacked autoencoder yields the best performance for chaotic time series. We evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean-Absolute-Percentage Error (MAPE). The experimental results on synthetic and real-world chaotic time series datasets reveal that MIMO strategy provides the best predictive accuracy for chaotic time series forecasting using LSTM-based stacked autoencoder. © 2023 ACM.

关键词： Forecasting

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：