检索结果-南通市图书馆

Journal of Intelligent and Connected Vehicles 2020年第1期3卷 17-29页

作者： Jun Liu Asad Khattak Lee Han Quan Yuan Department of Civil Construction and Environmental EngineeringThe University of AlabamaTuscaloosaAlabamaUSA Department of Civil and Environmental Engineering The University of TennesseeKnoxvilleTennesseeUSA State Key Laboratory of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijingChina

Purpose–Individuals’driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems.The incoming data can be sampled at rates ranging from one Hertz(or even lower)to hundreds of Hertz.Failing to capture substantial changes in vehicle movements over time by“undersampling”can cause loss of information and misinterpretations of the data,but“oversampling”can waste storage and processing resources.The purpose of this study is to empirically explore how micro-driving decisions to maintain speed,accelerate or decelerate,can be best captured,without substantial loss of information.Design/methodology/approach–This study creates a set of indicators to quantify the magnitude of information loss(MIL).Each indicator is calculated as a percentage to index the extent of information loss(EIL)in different situations.An overall information loss index named EIL is created to combine the MIL indicators.Data from a driving simulator study collected at 20 Hertz are analyzed(N=718,481 data points from 35,924 s of driving tests).The study quantiﬁes the relationship between information loss indicators and sampling rates.Findings–The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz,but the relationship is not linear.With four indicators of MILs,the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data.If sampling rates are higher than 2 Hz,all MILs are under 5 per cent for importation loss.Originality/value–This study contributes by developing a framework for quantifying the relationship between sampling rates,and information loss and depending on the objective of their study,researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

关键词： Driver behaviours and assistance Sensor data processing information loss Instantaneous driving decisions Sampling rate Undersampling

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Residual Convolutional Graph Neural Network with Subgraph Attention Pooling

引用

Tsinghua Science and Technology 2022年第4期27卷 653-663页

作者： Yutai Duan Jianming Wang Haoran Ma Yukuan Sun information and Communication Engineering Department Tiangong UniversityTianjin 300387China Computer Science Department Tiangong UniversityTianjin 300387China Software Engineering Department Tiangong UniversityTianjin 300387China Center for Engineering Intership and Training Tiangong UniversityTianjin 300387China

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational complexity.However,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification features.In this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features losing.Particularly,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph classification.Our strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual connection.By feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level representation.The messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for classification.Our experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

关键词： graph neural network graph pooling information loss

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

SOME　ASYMPTOTIC　INFERENCE　IN　MULTINOMIAL　NONLINEAR　MODELS（A　GEOMETRIC　APPROACH）

引用

Applied Mathematics(A Journal of Chinese Universities) 1996年第3期11卷 273-284页

作者： WEI BOCHENG(Department of Mathematics, Southeast University, Nanjing 210096.) Department of Mathematics Southeast University Nanjing

A geometric framework is proposed for multinomial nonlinear modelsbased on a modified version of the geometric structure presented by Bates & Watts[4]. We use this geometric framework to study some asymptotic inference in terms ofcurvatures for multinomial nonlinear models. Our previous results [15] for ordinarynonlinear regression models are extended to multinomial nonlinear models.

关键词： Curvature array multinomial nonlinear models information loss observed information stochastic expansion variance.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Redundancy Reduction:A New Criterion for Blind Separation of signals

Redundancy Reduction:A New Criterion for Blind Separation of...

引用

Electronic Communication Systems--The 2000 IEEE Asia-Pacific Conference on Circuits and Systems

作者： Ju Liu Kaibao Nie Zhenya He Department of Electronic Engineering Shandong University Department of Radio Engineering Southeast University

ISBN: (纸本)0780362535

＜正＞In this paper,we proposed an information theory criterion for blird separation of signals.Based on the criterion,we aeveloped an algorithm to maximize transferred information.A Hebb term is introduced in the algorithm to approximate the derivative of information loss ih information transmission.The mutual information of outputs is minimized to make the outputs mutually independent.Computer simulations with generated mixtures show the validity of the proposed algorithm.

关键词： signal processing redundancy information theory redundancy reduction blind separation signals separation information theory criterion Hebb term information loss information transmission

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

An assessment framework and the efficiency validation on the discretization of the belief rule base system 25th

An assessment framework and the efficiency validation on the...

引用

第25届中国控制与决策会议

作者： Jianbin Sun Leilei Chang Yuejin Tan College of information System ＆Management National University of Defense Technology

ISBN: (纸本)9781467355339

Due to the inability of the belief rule base in handling continuous attributes with multi-values, the discretization of the continuous distribution function should be done. The information loss rate （ILR） and the information loss probability mass （ILPM） are calculated to represent the information loss in the discretization process. And a framework to assess continuous attributes based on RIMER is proposed. In the end, a numerical case is studied to validate the efficiency of the proposed framework and measurements.

关键词： assessment discretization information loss RIMER

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

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：