检索结果-南通市图书馆

Deep Learning-Driven Data Curation and model interpretation for Smart Manufacturing

Chinese Journal of Mechanical Engineering 2021年第3期34卷 52-72页

作者： Jianjing Zhang Robert X.Gao Department of Mechanical and Aerospace Engineering Case Wester Reserve UniversityClevelandOH 44106-7222USA

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the *** the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world *** has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the *** paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated *** highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.

关键词： Deep learning Data curation model interpretation

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

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

引用

Fundamental Research 2024年第4期4卷 972-978页

作者： Jinghua Geng Yimeng Ding Wenjun Xie Wen Fang Miaomiao Liu Zongwei Ma Jianxun Yang Jun Bi State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210023 China

With the soaring generation of hazardous waste(HW)during industrialization and urbanization,HW illegal dumping continues to be an intractable global *** in developing regions with lax regulations,it has become a major source of soil and groundwater *** dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites,which makes HW illegal dumping difficult to be found,thereby causing a long-term adverse impact on the *** to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be *** this study,a novel machine learning model based on the positive-unlabeled(PU)learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic *** of the random forest-based PU model showed that the predicted top 30%of high-risk areas could cover 68.1%of newly reported cases in the studied region,indicating the reliability of the model *** novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

关键词： Hazardous waste Illegal dumping site Positive-unlabeled machine learning Probability prediction model interpretation

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

Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

引用

China Communications 2024年第8期21卷 211-235页

作者： Wang Zixuan Miao Cheng Xu Yuhua Li Zeyi Sun Zhixin Wang Pan School of Modern Posts Nanjing University of Posts&TelecommunicationsNanjing 210003China School of Computer Science Nanjing University of Posts&TelecommunicationsNanjing 210003China

With the rapid development of the Internet,network security and data privacy are increasingly *** classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy ***,the training of supervised deep learning models requires a large number of labeled samples,which is usually ***“black-box”problem also makes the DL models of NIDS *** this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned *** FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked *** the same time,we use FL for model training to protect data *** addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained *** conducted several experiments to evaluate the proposed *** first determine experimentally the structure and the number of neurons of the unsupervised AE ***,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 *** exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and ***,federated learning is used to train the intrusion detection *** experimental results indicate that the model is more accurate than the local learning ***,we use an improved SHAP explainability method based on Chi-square test to analyze the *** analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

关键词： autoencoder federated learning intrusion detection model interpretation unsupervised learning

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

Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation

引用

Journal of Materials Science & Technology 2022年第26期121卷 99-104页

作者： Jie Xiong Tong-Yi Zhang School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518000China Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511400China Material Genome Institute Shanghai UniversityShanghai 200444China

A data augmentation technique is employed in the current work on a training dataset of 610 bulk metallic glasses(BMGs),which are randomly selected from 762 collected *** ensemble machine learning(ML)model is developed on augmented training dataset and tested by the rest 152 *** result shows that ML model has the ability to predict the maximal diameter Dmaxof BMGs more accurate than all reported ML *** addition,the novel ML model gives the glass forming ability(GFA)rules:average atomic radius ranging from 140 pm to 165 pm,the value of TT/(T-T)(T-T)being higher than 2.5,the entropy of mixing being higher than 10 J/K/mol,and the enthalpy of mixing ranging from-32 k J/mol to-26 k J/*** model is interpretative,thereby deepening the understanding of GFA.

关键词： Materials informatics Glass-forming ability Data augmentation model interpretation Meta-ensemble model

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

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

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

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：