检索结果-内蒙古大学图书馆

Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier

引用

e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 11卷

作者： Jalli, Ravi Kumar Priyadarshini, Lipsa Dash, P.K. Bisoi, Ranjeeta Siksha 'O' Anusandhan Deemed to be University Odisha Bhubaneswar India Department of EEE GMR Institute of Technology Andhra Pradesh Rajam India

In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy sources, in the microgrid network power quality disturbances (PQD) are becoming quite complex and challenging task. Thus to design an effective PQD recognition system, this paper proposes a novel time-frequency analysis method based on adaptively fast complementary ensemble local mean decomposition (AFCELMD) technique that decomposes the multicomponent PQD signal into a series of demodulated product functions (PFs). Out of the several PFs the most sensitive one is selected adaptively and used for feature extraction and classification through a deep stacked auto-encoder (dSAE) hybridized with a time-recursive bi-directional long short term memory (BiLSTM) network classifier. The proposed BILSTM classifier captures the temporal features and their long term dependencies from the processed PF data samples and detects single and simultaneously occurring twenty complex power quality disturbances in the grid connected mode and five PQDs during uncertain PV insolence variation and load and capacitor switching during islanded mode of microgrid operation with significant accuracy of 99.90%. © 2025 The Author(s)

关键词： Bidirectional LSTM Microgrid Multiple power quality events Signal decomposition stacked autoencoder

来源：评论

学校读者我要写书评

暂无评论

Analysis of Extremely Obese Individuals Using Deep Learning stacked autoencoders and Genome-Wide Genetic Data 15th

Analysis of Extremely Obese Individuals Using Deep Learning ...

引用

15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

作者： Montanez, Casimiro A. Curbelo Fergus, Paul Chalmers, Carl Hind, Jade Liverpool John Moores Univ Dept Comp Sci Liverpool L3 3AF Merseyside England

ISBN: (纸本)9783030345853;9783030345846

Genetic predisposition has been identified as one of the components contributing to the obesity epidemic in modern societies. The aetiology of polygenic obesity is multifactorial, which indicates that lifestyle and environmental factors may influence multiples genes to aggravate this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have been associated with BMI. However, identified loci only explain a small proportion of the variation observed for this phenotype. The linear nature of genome wide association studies (GWAS) used to identify associations between genetic variants and the phenotype have had limited success in explaining the heritability variation of BMI and shown low predictive capacity in classification studies. GWAS ignores the epistatic interactions that less significant variants have on the phenotypic outcome. In this paper we utilise a novel deep learning-based methodology to reduce the high dimensional space in GWAS and find epistatic interactions between SNPs for classification purposes. SNPs were filtered based on the effects associations have with BMI. Since Bonferroni adjustment for multiple testing is highly conservative, an important proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore, only SNPs with p-values <1 x 10(-2) were considered for subsequent epistasis analysis using stacked autoencoders (SAE). This allows the nonlinearity present in SNP-SNP interactions to be discovered through progressively smaller hidden layer units and to initialise a multi-layer feedforward artificial neural network (ANN) classifier. The classifier is fine-tuned to classify extremely obese and non-obese individuals. The performance of classifications using progressively smaller compressed layers was compared and the results reported. The best results were obtained with 2,000 compressed units (SE = 0.949153, SP = 0.933014, Gini = 0.949936, Logloss = 0.1956, AUC = 0.97497 and MSE = 0.054057). Using 50 compressed units it was

关键词： Classification Deep learning Dimensionality reduction Epistasis GWAS Polygenic obesity SNPs stacked autoencoder

来源：评论

学校读者我要写书评

暂无评论

Feature Learning Using stacked autoencoders to Predict the Activity of Antimicrobial Peptides 13th

Feature Learning Using Stacked Autoencoders to Predict the A...

引用

13th International Conference on Computational Methods in Systems Biology (CMSB)

作者： Camacho, Francy Torres, Rodrigo Ramos-Pollan, Raul UIS Sch Comp Sci Bucaramanga Colombia UIS Sch Chem Grp Invest Bioquim & Microbiol GIBIM Bucaramanga Colombia UIS Sch Comp Sci Ctr High Performance & Sci Comp Bucaramanga Colombia

ISBN: (纸本)9783319234014;9783319234007

In recent years, pattern recognition methods have been applied to determine the activity of biological molecules, including the prediction of antimicrobial activity of synthetic and natural peptides where Quantitative Structure-Activity Relationship methodologies are widely used. Traditionally, works focused on designing descriptors for sequences to yield better correlations with the biological activity and improve predictors performance. Albeit there have been remarkable results, the small size of available datasets leave large room for improvement. In this work, rather than hand-crafting new descriptors, our approach consists in automatically learning them from existing ones. We use stacked autoencoders (a class of unsupervised neural networks), and the descriptors learnt are fed to a support vector regression task to predict biological activity. This method improves results in existing literature by roughly 12% simultaneously in different metrics, providing interesting insights into the nature of descriptors learnt and suggesting its applicability in other areas in protein properties prediction.

关键词： autoencoder stacked autoencoder Antimicrobial peptides Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Enhanced Deep autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

引用

Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： Thavavel Vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

来源：评论

学校读者我要写书评

暂无评论

Data Analytics of Network Intrusion Based on Deep Neural Networks with Weights Initialized by stacked autoencoders and Deep Belief Networks 13

Data Analytics of Network Intrusion Based on Deep Neural Net...

引用

IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Wang, Lidong Mosher, Reed L. Duett, Patti Falls, Terril C. Mississippi State Univ Inst Syst Engn Res Mississippi State MS 39762 USA

ISBN: (纸本)9798350332865

Data analytics is performed based on the deep neural network (DNN) with weights initialized by the stacked autoencoder (SA), DNN with weights initialized by the deep belief network (DBN), respectively in this paper. False positive rate (FPR), false negative rate (FNR), and accuracy are used as evaluation metrics to assess the performance of the methods. The data analytics mainly includes: 1) the effect of the hidden layer structures on the FPR, FNR, and accuracy of a DNN with weights initialized by the SA;2) the effect of the visible dropout on the accuracy of a DNN with weights initialized by the SA;3) the effect of the combination of the visible dropout and the hidden dropout on the accuracy of a DNN with weights initialized by the SA;4) the effect of hidden layer structures on the FPR, FNR, and accuracy of a DNN with weights initialized by the DBN;and 5) the effect of the batch size on the accuracy of a DNN with weights initialized by the DBN.

关键词： data analytics network intrusion cybersecurity deep neural network stacked autoencoder deep belief network dropout

来源：评论

学校读者我要写书评

暂无评论

An autoencoder-based spectral clustering algorithm

引用

SOFT COMPUTING 2020年第3期24卷 1661-1671页

作者： Li, Xinning Zhao, Xiaoxiao Chu, Derun Zhou, Zhiping Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Spectral clustering algorithm suffers from high computational complexity due to the eigen decomposition of Laplacian matrix and large similarity matrix for large-scale datasets. Some researches explore the possibility of deep learning in spectral clustering and propose to replace the eigen decomposition with autoencoder. K-means clustering is generally used to obtain clustering results on the embedding representation, which can improve efficiency but further increase memory consumption. An efficient spectral algorithm based on stacked autoencoder is proposed to solve this issue. In this paper, we select the representative data points as landmarks and use the similarity of landmarks with all data points as the input of autoencoder instead of similarity matrix of the whole datasets. To further refine clustering result, we combine learning the embedding representation and performing clustering. Clustering loss is used to update the parameters of autoencoder and cluster centers simultaneously. The reconstruction loss is also included to prevent the distortion of embedding space and preserve the local structure of data. Experiments on several large-scale datasets validate the effectiveness of the proposed method.

关键词： Spectral clustering stacked autoencoder Sparse representation KL divergence

来源：评论

学校读者我要写书评

暂无评论

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional autoencoder

引用

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 743426页

作者： An, Yanling Hu, Shaohai Duan, Xiaoying Zhao, Ling Xie, Caiyun Zhao, Yingying Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Northwest Univ Sch Econom & Management Xian Shaanxi Peoples R China Hebei Univ Coll Qual & Syst Supervis Baoding Peoples R China Hebei Univ Coll Elect & Informat Engn Baoding Peoples R China Machine Vis Technol Creat Ctr Hebei Prov Baoding Peoples R China

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

关键词： emotion recognition differential entropy feature fusion convolution neural network stacked autoencoder

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Pain Assessment Approach with stacked autoencoders and Attention-Based CP-LSTM 1

A Hybrid Pain Assessment Approach with Stacked Autoencoders ...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： De, Sagnik Mukherjee, Prithwijit Roy, Anisha Halder Institute of Radio Physics and Electronics University of Calcutta Kolkata India

ISBN: (纸本)9798350316469

Pain assessment is an integral part of healthcare since it enables the optimal management of patient well-being and the prompt administration of therapies. The ability to precisely diagnose pain is essential for ensuring appropriate medical attention and treatment. This study presents a novel pain categorization approach based on EEG (Electroencephalography) signals. A hybrid deep learning-based model is utilized in the study, which combines a stacked autoencoder for automated feature extraction and Chebyshev polynomial Long Short-Term Memory (CP-LSTM) with an attention mechanism for classification. The primary objective is to distinguish between two distinct pain states: 'No Pain' and 'Pain'. The proposed model can detect the 'Pain' and 'No Pain' states of an individual with 99.32% and 98.91% accuracy, respectively. Experimental observations indicate an increase in delta wave power in the frontal and central cortex, as well as an increase in alpha wave power in the frontal, temporal, and occipital lobes during pain. © 2023 IEEE.

关键词： Chebyshev polynomial LSTM EEG Pain Assessment stacked autoencoder

来源：评论

学校读者我要写书评

暂无评论

BROAD autoencoder FEATURES LEARNING FOR PATTERN CLASSIFICATION PROBLEMS 18

BROAD AUTOENCODER FEATURES LEARNING FOR PATTERN CLASSIFICATI...

引用

18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Wang, Ting Ng, Wing W. Y. Li, Wendi Kwong, Sam Li, Jingde South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728114194

Deep Neural Networks (DNNs) demonstrate great performances in pattern classification problems. There are several available activation functions for DNNs while the Sigmoid and the Tanh functions are most widely used choices. In this work, we propose the Broad autoencoder Features (BAF) to better utilize advantages of different activation functions. The BAF consists of four parallel connected stacked autoencoders (SAEs) with different activation functions: the Sigmoid, the Tanh, the ReLu, and the Softplus. With this broad setting, the final learned features merge learn features using diversified nonlinear mappings from the original input features and such that more information is mined from the original input features. Experimental results show that the BAF yields better learned features in comparison with merging four SAEs using the same activation functions.

关键词： pattern classification feature learning stacked autoencoder

来源：评论

学校读者我要写书评

暂无评论

Layerwise Recurrent autoencoder for Real-World Traffic Flow Forecasting 1

引用

9th International Conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Zhao, Junhui Zhu, Tianqi Zhao, Ruidong Zhao, Peize Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (数字)9783030362041

ISBN: (纸本)9783030362041;9783030362034

Accurate spatio-temporal traffic forecasting is a fundamental task for wide applications in city management, transportation area and financial domain. There are many factors that make this significant task also challenging, like: (1) maze-like road network makes the spatial dependency complex;(2) the relationship between traffic flow and time brings non-linear temporal problem;(3) with the larger road network, the difficulty of flow forecasting grows. The prevalent and state-of-the-art methods have mainly been discussed on datasets covering relatively small districts and short time span. To forecast the traffic flow across a wider area and overcome the mentioned challenges, Layerwise Recurrent autoencoder (LRA) is designed and proposed, in which a three-layer stacked autoencoder (SAE) architecture is used to obtain temporal traffic correlations in three different time scales and for each output of different time scales, a dedicate neural network is used for prediction. The convolutional neural networks (CNN) model is also employed to extract spatial traffic information within the road map for more accurate prediction. To the best of our knowledge, there is no effective method for traffic flow prediction which concerns traffic of city group and LRA is the first one. The experiment is completed on a large real-world traffic dataset to show the performance of the proposed. In the end, evaluations show that our model outperforms the state-of-the-art baselines by 6%-15%.

关键词： Traffic forecasting Neural networks stacked autoencoder

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：