检索结果-内蒙古大学图书馆

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

引用

IEEE ACCESS 2022年 10卷 29069-29088页

作者： Zhang, Xiaotian Hu, Yihua Deng, Jiamei Xu, Hui Wen, Huiqing Univ York Dept Elect Engn York YO10 5DD N Yorkshire England Leeds Beckett Univ Sch Built Environm Engn & Comp Leeds LS1 3HE W Yorkshire England Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215000 Peoples R China

Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

关键词： Feature extraction Mechanical power transmission Artificial intelligence Fault diagnosis Monitoring Data mining Wavelet analysis Artificial intelligence feature extraction fault diagnosis neural networks machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT)

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第5期8卷 3554-3566页

作者： An, Yufei Yu, F. Richard Li, Jianqiang Chen, Jianyong Leung, Victor C. M. Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518060 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada Univ British Columbia Dept ECE Vancouver BC V6T 1Z4 Canada

In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, the hypertext transfer protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence (EI) for IoT. In this framework, both clustering and classification methods are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in EI, the proposed framework distributes the entire detection process to different nodes. Moreover, a data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of an HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.

关键词： Anomaly detection Internet of Things Feature extraction Image edge detection Security machine learning algorithms Protocols Anomaly detection edge intelligence (EI) hypertext transfer protocol (HTTP) Internet of Things (IoT)

来源：评论

学校读者我要写书评

暂无评论

Dual Consensus Proximal Algorithm for Multi-Agent Sharing Problems

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5568-5579页

作者： Alghunaim, Sulaiman A. Lyu, Qi Yan, Ming Sayed, Ali H. Kuwait Univ Dept Elect Engn Kuwait 13060 Kuwait Michigan State Univ MSU Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ MSU Dept ofMathemat E Lansing MI 48824 USA Ecole Polytech Fed Lausanne Sch Engn CH-1015 Lausanne Switzerland

This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non-smooth). For this non-smooth setting, centralized algorithms are known to converge linearly under certain conditions. On the other hand, decentralized algorithms have not been shown to achieve linear convergence under the same conditions. In this work, we propose a decentralized proximal primal-dual algorithm and establish its linear convergence under weaker conditions than existing decentralized works. Our result shows that decentralized algorithms match the linear rate of centralized algorithms without any extra condition. Finally, we provide numerical simulations that illustrate the theoretical findings and show the advantages of the proposed method.

关键词： Convergence Signal processing algorithms Couplings Sparse matrices machine learning algorithms Topology Symmetric matrices Multi-agent optimization sharing problem dual consensus proximal algorithm linear convergence

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network

引用

IEEE ACCESS 2022年 10卷 29731-29740页

作者： Rizwan, Muhammad Shabbir, Aysha Javed, Abdul Rehman Shabbir, Maryam Baker, Thar Obe, Dhiya Al-Jumeily Kinnaird Coll Women Dept Comp Sci Lahore 54000 Pakistan Air Univ Dept Cyber Secur Islamabad 44000 Pakistan Univ Management & Technol Sch Profess Adv Lahore 54770 Pakistan Univ Sharjah Coll Comp & Informat Dept Comp Sci Sharjah U Arab Emirates Liverpool John Moores Univ Sch Comp Sci & Math Liverpool L3 5UX Merseyside England

Understanding brain diseases such as categorizing Brain-Tumor (BT) is critical to assess the tumors and facilitate the patient with proper cure as per their categorizations. Numerous imaging schemes exist for BT detection, such as Magnetic Resonance Imaging (MRI), generally utilized because of the better quality of images and the reality of depending on non-ionizing radiation. This paper proposes an approach to detect distinctive BT types using Gaussian Convolutional Neural Network (GCNN) on two datasets. One of the datasets is used to classify tumors into pituitary, glioma, and meningioma. The other one separates the three grades of glioma, i.e., Grade-two, Grade-three, and Grade-four. These datasets have '233' and '73' victims with a total of '3064' and '516' images on T1-weighted complexity improved pictures for the first and second datasets, separately. The proposed approach achieves an accuracy of 99.8% and 97.14% for the two datasets. The experimental results highlight the efficiency of the proposed approach for BT multi-class categorization.

关键词： Tumors Feature extraction Training Magnetic resonance imaging machine learning algorithms Transforms Licenses Deep learning brain tumor classification Gaussian convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Seeking the Truth in a Decentralized Manner

引用

IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第5期29卷 2296-2312页

作者： Fu, Luoyi Xu, Jiasheng Qu, Shan Xu, Zhiying Wang, Xinbing Chen, Guihai Shanghai Jiao Tong Univ Dept Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In networks where massive sources make observations of same entities, we intend to seek the truth - the most trustworthy value of each entity from conflicting information claimed by multiple sources. Various methods are proposed for accurately inferring both source reliability and truths, yet relying heavily on centralized settings that incur tremendous overhead to source side. In this paper, we offer a decentralized design of truth discovery task that can fit favorably to the environments with limited resources. Considering that sources forming the connected network and making individual observations, we undertake the joint maximum likelihood estimation (MLE) of truth and source reliability. Our decentralization framework simply allows each source to maintain local information exchange at a time, and computes very basic functions of data observations. To this end, we facilitate the decentralization by simplifying the MLE problem into optimizing an objective function. Upon the proof of NP-hardness, two proposed decentralized algorithms (exact and approximation) are decentralized and randomized via a combination of algorithms from their centralized counterparts that ensure performance guarantee. The derived time complexity features explicit data/network dependent terms, which leads to further acceleration in truth finding. Remarkably, in two well connected networks like random geometric and preferential attachment graphs, the accelerated approximation method enjoys logarithmic time complexity while preserving comparable accuracy to the centralized counterparts. The effectiveness of the proposed decentralizations are further empirically confirmed.

关键词： Approximation algorithms Reliability machine learning algorithms Task analysis machine learning Maximum likelihood estimation Data mining Truth discovery source reliability maximum likelihood estimation decentralization

来源：评论

学校读者我要写书评

暂无评论

PLP-SSAF: Improved Online Rumor Detection Combining Propagation Link Prediction and Semantic-Structure Adaptive Fusion

引用

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年

作者： Zhang, Chengxin Wang, Liang Lin, Yaguang Wang, Xiaoming Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The spread of rumors on social networks can diminish public interests, and even pose a threat to social security. The first prerequisite for effectively suppressing rumors in social networks is the precise detection of rumors. However, factors such as hidden social relationships between users, various social contents, and diverse application scenarios in the vast social network bring severe challenges to the timely and accurate detection of rumors. Therefore, for the sake of exploring the influence of the above factors, we propose an adaptive rumor detection model combining propagation link prediction (PLP) and semantic-structure adaptive fusion, PLP and semantic-structure adaptive fusion (PLP-SSAF). First, we investigate in depth the impact of hidden social relationships on the propagation structure of posts. We leverage the hidden social relationships between users, and utilize the graph attention neural network to extract propagation structure features that simultaneously include both current and future propagation structure. Second, in order to overcome regional and cultural differences among users in multidialect environment, we enhance the text in the original dataset and merge it with the source text. We use a language representation model to extract semantic features from the joint text, getting joint semantic features that can more comprehensively represent the text. Finally, we propose a parallel features adaptive fusion mechanism that can dynamically update the weights between propagating structure features and semantic features. This enables the obtained post features to better adapt to rumor detection scenarios in social networks under massive data environments. Extensive experiments on three real-world datasets show that our PLP-SSAF significantly improving rumor detection performance in accuracy and generalization over existing methods, and demonstrates superior rumor detection capabilities at early stages.

关键词： Feature extraction Social networking (online) Semantics Accuracy Manuals Deep learning machine learning algorithms Heuristic algorithms Data mining Computational modeling Feature adaptive fusion propagation link prediction (PLP) rumor detection social networks

来源：评论

学校读者我要写书评

暂无评论

Rational Design of Two-Dimensional MA2Z4 Monolayers as Effective Anchoring Materials for Lithium-Sulfur Batteries

引用

ACS APPLIED MATERIALS & INTERFACES 2024年第45期16卷 62213-62221页

作者： Zhou, Dingyanyan Jin, Lujie Ji, Yujin Li, Youyong Soochow Univ Inst Funct Nano & Soft Mat FUNSOM Jiangsu Key Lab Carbon Based Funct Mat & Devices Suzhou 215123 Jiangsu Peoples R China Macau Univ Sci & Technol Macao Inst Mat Sci & Engn MIMSE Taipa 999078 Macau Peoples R China

Advances in lithium-sulfur batteries (LSBs) are impeded by the inefficiency of anchoring materials in facilitating long-term cycling and rate performance. To address this challenge, an exploration of two-dimensional MA(2)Z(4) monolayers as potential anchoring materials for LSBs is proposed based on density functional theory calculations and machine learning (ML) techniques. Adsorption features, sulfur reduction reaction behaviors, and solvent interactions are assessed and analyzed;and MoGe2N4 and WGe2N4 are identified as the most promising candidates because they have optimal adsorption energies for lithium polysulfides to suppress the shuttle effect and exhibit enhanced catalytic activity. Meanwhile, ML analysis highlights the critical influence of the electronegativity of element Z in MA(2)Z(4) on anchoring properties, providing valuable insights into future anchoring material design for high-performance LSBs.

关键词： lithium-sulfur batteries 2D materials MA(2)Z(4) monolayers anchoring materials first-principles simulation machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第8期43卷 5288-5308页

作者： Sharma, Ramnivas Meena, Hemant Kumar Malaviya Natl Inst Technol Dept Elect Engn Jaipur 302017 Rajasthan India

Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a novel real-time method to improve the detection of seizures using the spectral features of non-stationary electroencephalogram (EEG) signals. It is observed that the discrete wavelet transform (DWT)-based features do not consider the interrelationship among EEG signal components. This interrelationship has been well captured by the novel representation of EEG in the form of graph signals. Here, the spectral analysis of the graph signals is investigated by the graph-based Fourier transform (GFT). Then, GFT-based features have been selected and fed into different classifiers for analysis. The seizure detection rate in two publicly available EEG-based datasets, the University of Bonn (UB) and the Neurology Sleep Clinic New Delhi (NSC-ND), have been achieved with accuracy of 98.68% and 96.84%, respectively. The accuracy achieved is significantly better than the existing state-of-the-art techniques. This approach demonstrates the impact of utilizing the interrelationship among the EEG components, followed by enhanced feature selection based on GFT for the improved detection of seizures.

关键词： EEG signals Discrete wavelet transform Graph Fourier transform machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

引用

ENERGY & FUELS 2018年第5期32卷 6309-6329页

作者： Jameel, Abdul Gani Abdul Van Oudenhoven, Vincent Emwas, Abdul-Hamid Sarathy, S. Mani KAUST CCRC Thuwal 239556900 Saudi Arabia Univ Waterloo Dept Comp & Elect Engn Waterloo ON N2L 3G1 Canada KAUST Imaging & Characterizat Core Lab Thuwal 239556900 Saudi Arabia

machine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends, and gasoline ethanol blends has been developed using artificial neural networks (ANNs) and molecular parameters from H-1 nuclear magnetic resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon ethanol blends of known composition, and 30 FACE (fuels for advanced combustion engines) gasoline ethanol blends were utilized as a data set to develop the ANN model. The effect of weight percent of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular weight (MW) were included as inputs along with the seven functional groups to predict RON and MON. The developed ANN models for RON (9-540-314-1) and MON (9-340-603-1) have two hidden layers and a large number of nodes, and was validated against experimentally measured RON and MON of pure hydrocarbons, hydrocarbon-ethanol, and gasoline-ethanol blends;a good correlation (R-2 = 0.99) between the predicted and the experimental data was obtained. The average error of prediction for both RON and MON was found to be 1.2 which is close to the range of experimental uncertainty. This shows that the functional groups in a molecule or fuel can be used to predict its ON, and the complex relationship between them can be captured by tools such as ANNs.

关键词： Octane number Monoceros error forecasting Hydrocarbons chemical group nuclear magnetic resonance spectroscopy Blends machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 4859-4874页

作者： Lu, Songtao Lee, Jason D. Razaviyayn, Meisam Hong, Mingyi IBM Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Princeton Univ Dept Elect Engn Princeton NJ 08540 USA Univ Southern Calif Dept Ind & Syst Engn Los Angeles CA 90089 USA Univ Minnesota Twin Cities Dept Elect & Comp Engn Minneapolis MN 55455 USA

In this work, a gradient-based primal-dual method of multipliers is proposed for solving a class of linearly constrained non-convex problems. We show that with random initialization of the primal and dual variables, the algorithm is able to compute second-order stationary points (SOSPs) with probability one. Further, we present applications of the proposed method in popular signal processing and machine learning problems such as decentralized matrix factorization and decentralized training of overparameterized neural networks. One of the key steps in the analysis is to construct a new loss function for these problems such that the required convergence conditions (especially the gradient Lipschitz conditions) can be satisfied without changing the global optimal points.

关键词： Signal processing algorithms Convex functions Convergence machine learning algorithms Training machine learning Linear programming First-order stationary points (FOSPs) second-order stationary points (SOSPs) alternating direction method of multipliers (ADMM) non-convex optimization neural networks

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

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

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：