检索结果-内蒙古大学图书馆

34th Chinese Control Conference (CCC)

作者： Tao, Siqin Zhang, Tao Yang, Jun Wang, Xueqian Lu, Weining Tsinghua Univ Dept Automat Beijing 100191 Peoples R China Tsinghua Univ Shenzhen Ctr Intelligent Control & Telesci Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9789881563897

As bearings are the most common components of mechanical structure, it will be helpful to research bearing fault and diagnose the fault as early as possible in case of suffering greater losses. This paper proposes a deep neural network algorithm framework for bearing fault diagnosis based on stacked autoencoder and softmax regression. The simulation results verify the feasibility of the algorithm and show the excellent classification performance. In addition, this deep neural network represents strong robustness and eliminates the impact of noise remarkably. Last but not least, an integrated deep neural network method consisting of ten different structure parameter networks is proposed and it has better generalization capability.

关键词： Fault Diagnosis stacked autoencoder Softmax Regression Classification Robustness

来源：评论

学校读者我要写书评

暂无评论

Classification of Human Activity by Using a stacked autoencoder

Classification of Human Activity by Using a Stacked Autoenco...

引用

Medical Technologies National Conference (TIPTEKNO)

作者： Badem, Hasan Caliskan, Abdullah Basturk, Alper Yuksel, Mehmet Emin Erciyes Univ Bilgisayar Muhendisligi Bolumu Kayseri Turkey Erciyes Univ Biyomed Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781509023868

This paper investigates the application of a deep neural network architecture that consists of stackted autoencoder with two autoencoders and a softmax layer for the purpose of human activity classification. Th performance of the proposed architecture is tested on a commonly used data set known as Human Activity Recognition Using Smartphones. It is observed that the proposed method yields better classification results than the representative state-of-the-art methods provided that the parameters of the deep network are suitably optimized.

关键词： Deep Neural Network stacked autoencoder Softmax Human Activity Recognition

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of COVID-19 Using a stacked Denoising Convolutional autoencoder

引用

Computers, Materials & Continua 2021年第12期69卷 3259-3274页

作者： Habib Dhahri Besma Rabhi Slaheddine Chelbi Omar Almutiry Awais Mahmood Adel M.Alimi College of Applied Computer Sciences(CACS) King Saud UniversityRiyadh11543Saudi Arabia Faculty of Sciences and Technology of Sidi Bouzid University of KairouanKairouanTunisia National School of Engineers(ENIS) University of SfaxTunisia Department of Computer Sciences College of Sciences and Arts in UnaizahQassim UniversityAl-QassimKingdom of Saudi Arabia

The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many ***,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and *** SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy *** proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax *** proposed model was evaluated with 6356 images from the datasets from different *** experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,*** metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both *** results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis.

关键词： stacked autoencoder augmentation multiclassification COVID-19 convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

A Novel Prediction Method for Blast Furnace Gas Utilization Rate Based on Dynamic Weighted stacked Output-Relevant autoencoder

引用

STEEL RESEARCH INTERNATIONAL 2023年第5期94卷

作者： Jiang, Zhaohui Zhu, Jicheng Pan, Dong Yu, Haoyang Zhou, Ke Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

In the blast furnace (BF) ironmaking process, the gas utilization rate (GUR) is a crucial indicator for reflecting the energy consumption and operating status of BF. However, due to the complex and harsh environment in the BF top, accurately obtaining GUR online is not an effortless task. Although many studies have been carried out to predict GUR through data-driven methods, some challenges still exist: 1) limited feature extraction capability for complex data patterns;2) prediction accuracy is sensitive to the fluctuation of BF working conditions. Therefore, a novel deep learning method is proposed based on dynamic weighted stacked output-relevant autoencoder (DW-SOAE) for GUR online prediction. First, the input layer variables for each AE are weighted according to their importance, which will reduce output-unrelated features. Then, the output variable is also reconstructed at the output layer of each AE, which ensures extracted features can largely predict GUR. Next, considering that the fluctuation of BF working conditions may affect prediction accuracy, the density peak clustering algorithm is used to cluster the process variables, and several DW-SOAE-based submodels are built for GUR prediction. Finally, the effectiveness and superiority of the proposed GUR prediction method are verified in industrial experiments.

关键词： blast furnace density peak clustering gas utilization rate prediction stacked autoencoder

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

Global dynamic features and information of adjacent hidden layer enhancement based on autoencoder for industrial process soft sensor application

引用

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第5期103卷 2218-2233页

作者： Guo, Xiaoping Pan, Sulei Li, Yuan Shenyang Univ Chem Technol Coll Informat Engn Shenyang Liaoning Peoples R China

There is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD-ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD-ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi-head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target-related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.

关键词： global dynamic feature soft sensor stacked autoencoder UMAP

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

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Singh, Deepali Gupta, Rinki Kumar, Arun Bahl, Rajendar Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi India Amity Univ Uttar Pradesh Elect & Commun Engn Dept Noida India Amity Univ Dept Mech & Automat Engn Noida Uttar Pradesh India

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered -x least mean square (FxLMS) technique.

关键词： Active noise control stacked autoencoder Nonlinear distortion Deep learning Anechoic room

来源：评论

学校读者我要写书评

暂无评论

Effective Insect Recognition Using a stacked autoencoder with Maximum Correntropy Criterion

Effective Insect Recognition Using a Stacked Autoencoder wit...

引用

International Joint Conference on Neural Networks

作者： Yu Qi Goktug T. Cinar Vinicius M. A. Souza Gustavo E. A. P. A. Batista Yueming Wang Jose C. Principe Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (纸本)9781479919611

Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent trap that uses laser sensors was presented to control the population of target insects. The device could record and analyze sensor signals when an insect passes through the trap and make quick decisions whether to catch it or not. The effectiveness of the trap relies on the correct choice of classification algorithm to perform the insect detection. In this paper, we propose to use a deep neural network with maximum correntropy criterion (MCC) for reliable classification of insects in real-time. Experimental results show that, deep networks are effective for learning stable features from brief insect passage signals. By replacing the mean square error cost with MCC, the robustness of autoencoders against noise is improved significantly and robust features could be learned. The method is tested on five species of insects and a total of 5325 passages. High classification accuracy of 92.1 % is achieved. Compared with previously applied methods, better classification performance is obtained using only 10% of the computation time. Therefore, our method is efficient and reliable for online insect detection.

关键词： Effective Insect Recognition stacked autoencoder Maximum Correntropy Criterion Insecta Traps pest insects Laser sensors Sorting algorithm Humanities beneficial insect MCC Genes computational time PASSAGE

来源：评论

学校读者我要写书评

暂无评论

Analog Circuit Fault Diagnosis Model Based on WOA and Improved SDAE

引用

IEICE ELECTRONICS EXPRESS 2025年第1期22卷 20240633-20240633页

作者： Lu, Xinmiao Zou, Yixin Wu, Qiong Yang, Longyue Zhu, Yuna Harbin Univ Sci & Technol Sch Measurement Control Technol & Commun Engn Harbin 150000 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150000 Peoples R China Heilongjiang Network Space Res Ctr Harbin 150000 Peoples R China

With the advancement of integrated circuit technology, the stable operation of electronic devices is crucial. Targeting the issue that traditional analog circuit fault diagnosis models cannot simultaneously satisfy noise resistance, stability, and accuracy in real circuit environments, this research represents an analog circuit fault diagnosis model relied on the whale optimization algorithm and an improved SDAE. The model transfor ms fault signals into 2D time-frequency representations using VMD and CWT to achieve initial denoising;Utilizing DSDAE for further denoising and dimensionality reduction of feature vectors;finally, RF is used for classification. The results of the simulation demonstrate that even in noisy conditions, the model can maintain excellent diagnostic accuracy and stability. making certain improvements in enhancing the operational reliability of electronic devices.

关键词： Variational Mode Decomposition Continuous Wavelet Trans for m stacked autoencoder Whale Optimization Algorithm

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：