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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Jian Zhang Zhenjie Hou Zhuoran Wu Yongkang Chen Weikang Li College of Information Mathematics and Physics Changzhou University

ISBN: (纸本)9781509017829

This electronic Due to the fact that the 3D face depth data have more information,the 3D face recognition is attracting more and more attention in the machine learning ***,this paper selects 30 feature points from the 113feature points of Candide-3 face model to characterize face,which improves the efficiency of recognition algorithm obviously without affecting the recognition *** the significant advantage of the characterization of essential features by learning a deep nonlinear network,this paper presents a stacked denoising autoencoder algorithm model based on deep learning which improves neural networks *** algorithm conducts the unsupervised preliminary training of face depth data and the supervised training to fine-tuning the network which is better than neural network’s random *** experiment indicates that compared with real face data,the reconstruction face model has a small matching error by using SDAE algorithm and it achieves an excellent face recognition effect.

关键词： 3D face depth data deep learning neural networks stacked denoising autoencoder unsupervised preliminary training

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A Robust WiFi Localization Algorithm Using Data Augmentation and stacked denoising autoencoder

A Robust WiFi Localization Algorithm Using Data Augmentation...

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第35届中国控制与决策会议

作者： Changsheng Zhuang Dengyin Zhang College of Internet of Things Nanjing University of Posts and Telecommunications

Wi Fi-based fingerprint localization technology has become one of the most practical methods for localizing mobile users due to its non-intrusive nature,low cost and no additional equipment ***,the fluctuation of Wi Fi signal seriously affects the accuracy of Wi Fi fingerprint *** address this problem,this paper proposes a solution using data augmentation combined with stacked denoising autoencoder(SDAE).Data augmentation can facilitate the neural network to learn the mapping relationship between the fluctuating WiFi signals and *** the SDAE can obtain a robust and time-independent feature from the dynamic WiFi signal.A convolutional neural network is also used to build a floor classification model to determine the height,and a multilayer perceptron(MLP) is used to build a regression model to determine the relative *** results on public datasets show that the method improves system robustness and localization accuracy.

关键词： indoor positioning data augmentation stacked denoising autoencoder

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A Power System Transient Stability Assessment Model Based on stacked denoising autoencoder

A Power System Transient Stability Assessment Model Based on...

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2018 3rd International Conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2018)

作者： Mei FU Shu-fang LI Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing

Since the grid data is not always "perfect", there are often cases where data is missing or contains noise. In order to solve the above problems and further improve the generalization ability of the power system transient stability assessment model, we introduce denoising autoencoder(DAE) structure and propose a model based on stacked denoising autoencoder(SDAE). Firstly, construct the original input features that can reflect the transient stability characteristics of the power system. And then use the input features layer-by-layer unsupervised learning to get the encoder of DAE. Stacking all the trained encoders constitutes a SDAE as an advanced feature extraction model. Next, using the advanced features and labels has a supervised training classifier to obtain a complete transient stability assessment model. The model proposed in this paper has been tested on 10-machine New-England Power System. The simulation results show that the proposed model is robust to the "imperfection" of the input data, and the generalization ability is stronger.

关键词： stacked denoising autoencoder Logistic regression Classification Transient stability assessment

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stacked denoising autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data 17

Stacked Denoising Autoencoders for Mortality Risk Prediction...

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17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Alhassan, Zakhriya Budgen, David Alshammari, Riyad Daghstani, Tahani McGough, A. Stephen Al Moubayed, Noura Univ Durham Dept Comp Sci Durham England KSAU Hlth Sci Publ Hlth & Hlth Informat Riyadh Saudi Arabia KSAU Hlth Sci Coll Publ Hlth & Hlth Informat Riyadh Saudi Arabia Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781538668054

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an invaluable source of information that can ultimately help with improving healthcare services. In particular, quick and accurate predictions of mortality can be valuable for physicians who are making decisions about interventions. In this work we introduce the use of a predictive Deep Learning model to help evaluate the mortality risk for in-hospital patients. stacked denoising autoencoder (SDA) has been trained using a unique time-stamped dataset (King Abdullah International Research Center -KAIMRC) which is naturally imbalanced. The results are compared to those from common deep learning approaches, using different methods for data balancing. The proposed model demonstrated here aims to overcome the problem of imbalanced data, and outperforms common deep learning approaches with an accuracy of 77.13% for the Recall macro

关键词： Mortality risk Deep Learning stacked denoising autoencoder King Abdullah International Research Center Data Imbalance Recall Macro

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Bilinear Interpolation Augmented Deep Feature Extraction with an Improved Remora Optimization-Based Deep Convolutional Neural Network for Skin Lesion Classification

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2025年第0期

作者： Sahoo, Madhusmita Priyadarshini Sridhar, Rajeswari Natl Inst Technol Comp Sci & Engn Tiruchirappalli 620015 Tamil Nadu India

The study introduces a novel approach for skin lesion classification that leverages bilinear interpolation-based feature extraction and an enhanced Remora Optimization Algorithm (ROA). This research is motivated by the high prevalence of skin cancer globally and the necessity for early detection, particularly in underserved areas where access to dermatological expertise is limited. Traditional dermoscopic datasets, typically captured in controlled clinical environments, do not adequately represent the variability of real-world smartphone images, which often suffer from issues like varied lighting and motion blur. The proposed method refines visual details by integrating multi-scale spatial information, significantly enhancing classification accuracy. The enhanced ROA optimizes parameter selection, addressing challenges of high-dimensional data and hyper-parameter tuning in deep learning models. This innovative combination surpasses existing classification methods, offering a promising early screening tool, especially beneficial in rural areas. The results demonstrate the proposed architecture's effectiveness, achieving accuracies of 83.71%, 97.21%, and 95.6% on the PAD-UFES-20, ISIC 2016, and ISIC 2017 datasets, respectively, highlighting the potential of advanced computational techniques in augmenting the diagnostic process for skin cancer.

关键词： Skin-lesion bilinear interpolation Remora optimization opposition-based learning stacked denoising autoencoder PAD-UFES-20

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Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging

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FOOD CHEMISTRY 2025年 479卷 143799页

作者： Zhou, Xin Liu, Yang Zhao, Chunjiang Sun, Jun Shi, Lei Cong, Sunli Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Beijing Acad Agr & Forestry Sci Informat Technol Res Ctr Beijing 100097 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Jiangsu Univ Key Lab Theory & Technol Intelligent Agr Machinery Zhenjiang 212013 Peoples R China Jiangsu Prov & Educ Minist Cosponsored Synergist I Zhenjiang 212013 Peoples R China

In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. stacked denoising autoencoder (SDAE) algorithm was used to reduce dimensionality, and the most effective SDAE deep learning network was transferred to create the T-SDAE model. The results showed that SVR model using SDAE to extract depth features had the best prediction effect on Cd content in silicon-free, low-silicon and higher-silicon environments. Moreover, the coefficient of determination of prediction set (Rp2) were 0.9127, 0.9829 and 0.9606, respectively. Specifically, the Rp2 value of the T-SDAE-SVR optimal prediction set under different silicon environments is 0.9273, RMSEP is 0.01465 mg/kg, and RPD is 3.237. By integrating hyperspectral imaging technology with a deep transfer learning algorithm, accurate detection of various Cd contents in oilseed rape leaves is feasible under different silicon environments.

关键词： stacked denoising autoencoder Deep learning Transfer learning Silicon environment Cadmium hyperspectral imaging

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Daily Activity Recognition and Tremor Quantification from Accelerometer Data for Patients with Essential Tremor Using stacked denoising autoencoders

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-13页

作者： Ni, Qin Fan, Zhuo Zhang, Lei Zhang, Bo Zheng, Xiaochen Zhang, Yuping Shanghai Normal Univ Coll Informat Mech & Elect Engn 100 Haisi Rd Shanghai 201418 Peoples R China Donghua Univ Coll Informat Sci & Technol 2999 North Renmin Rd Shanghai 201620 Peoples R China Univ Politecn Madrid Escuela Tecn Super Ingn Ind Madrid Spain

Human activity recognition (HAR) has received more and more attention, which is able to play an important role in many fields, such as healthcare and intelligent home. Thus, we have discussed an application of activity recognition in the healthcare field in this paper. Essential tremor (ET) is a common neurological disorder that can make people with this disease rise involuntary tremor. Nowadays, the disease is easy to be misdiagnosed as other diseases. We have combined the essential tremor and activity recognition to recognize ET patients' activities and evaluate the degree of ET for providing an auxiliary analysis toward disease diagnosis by utilizing stacked denoising autoencoder (SDAE) model. Meanwhile, it is difficult for model to learn enough useful features due to the small behavior dataset from ET patients. Thus, resampling techniques are proposed to alleviate small sample size and imbalanced samples problems. In our experiment, 20 patients with ET and 5 healthy people have been chosen to collect their acceleration data for activity recognition. The experimental results show the significant result on ET patients activity recognition and the SDAE model has achieved an overall accuracy of 93.33%. What's more, this model is also used to evaluate the degree of ET and has achieved the accuracy of 95.74%. According to a set of experiments, the model we used is able to acquire significant performance on ET patients activity recognition and degree of tremor assessment.

关键词： Essential tremor Activity recognition stacked denoising autoencoder Acceleration data Deep learning

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Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第5期23卷 3084-3104页

作者： Wang, Lei Rao, Hang Dong, Zhengcheng Zeng, Wenhui Xu, Fan Jiang, Li Zhou, Chao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Wuhan Univ Technol Sch Automat East Campus Mafangshan CampusLuoshi Rd 205 Wuhan 430070 Hubei Peoples R China CASIC Inst Laser & Photon Engn Wuhan Peoples R China

In practical engineering, data often lack labels, resulting in difficulty in fault diagnosis. Because stack-denoising autoencoders possess robust feature extraction capabilities and resistance to interference, an automatic and unsupervised bearing fault diagnosis method based on the stack-denoising autoencoder without an output layer was proposed in this study. As the stacked denoising autoencoder is an unsupervised algorithm, this approach can reduce reliance on manually labeled data labels. Therefore, this study proposed a new method for automatic fault diagnosis. First, the bearing fault features of the rolling bearing were extracted using the stack denoising autoencoder without an output layer. Meanwhile, the dimensions of the features were directly reduced to two or three dimensions by several hidden layers, thereby reducing manual experience. Second, the labels extracted from the clustering model were selected as inputs for different classifier models to automatically identify different types of faults. Two open-source rolling bearing datasets under various conditions were used to validate the classification performance of the proposed method. Finally, its effectiveness was verified using the experimental results. Various indicators were used to evaluate the performance of the proposed method, and the results showed an automatic bearing fault diagnosis accuracy of up to 90% when using different models and working conditions. Among the two datasets, the classification model achieved the highest accuracies of 0.99667 and 0.97143 and the lowest accuracies of 0.98000 and 0.90476, respectively.

关键词： stacked denoising autoencoder bearing fault diagnosis deep learning automatic

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A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis

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COMPUTERS IN INDUSTRY 2019年 108卷 62-72页

作者： Yu, Jianbo Tongji Univ Sch Mech Engn 4800 CaoAn Rd Shanghai 201804 Peoples R China

Vibration signals are widely used as an effective way to fulfill gearbox fault diagnosis. However, it is quite challenging to extract effective fault features from noisy vibration signals and then to construct a reliable fault diagnosis model. This paper proposes a selective stacked denoising autoencoders (SDAE) with negative correlation learning (NCL) (SSDAE-NCL) for gearbox fault diagnosis. The component SDAEs are firstly constructed to extract effective fault features from vibration signals in the unsupervised-learning phase of SSDAE-NCL. Based on the extracted features, NCL is used to fine-tune the SDAE components to construct component classifiers in the supervised-learning phase of SSDAE-NCL. Finally, a selective ensemble is finished based on these divers and accurate component SDAEs for gearbox fault diagnosis. The motivation for developing ensemble of deep neural networks (DNNs) is that they can achieve higher accuracy and applicability than single component in machinery fault diagnosis. Furthermore, it can make an overall ensemble model easy to use in real cases for users, because it does not need too much prior knowledge about setup of a DNN model. The effectiveness of this SSDAE-NCL-based fault diagnosis method has been verified by experimental results on the vibration signal data from a gearbox test rig. The results illustrate that SSDAE-NCL learns effective discriminative features from vibration signals and achieves the better diagnosis accuracy in comparison with those typical DNNs (e.g.;SDAE, deep belief network (DBN)). (C) 2019 The Author. Published by Elsevier B.V.

关键词： Gearbox fault diagnosis Deep learning Selective ensemble learning stacked denoising autoencoder Negative correlation learning

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A stacked denoising autoencoders Based Collaborative Approach for Recommender System 8th

A Stacked Denoising Autoencoders Based Collaborative Approac...

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8th International Symposium on Parallel Architectures, Algorithms, and Programming (PAAP)

作者： Niu, Baojun Zou, Dongsheng Niu, Yafeng Chongqing Univ Chongqing 400044 Peoples R China

ISBN: (纸本)9789811064425;9789811064418

This paper uses an autoencoder neural network as user feature learning component for collaborative filtering task. We propose a stacked denoising autoencoder (SDAE) based model to alleviate the sparseness issues in recommendation system. Our model also extends the scalability of CF-based methods in the Top-N recommendation task. Experiments on MovieLens datasets and the result confirmed the effectiveness and potential of our model.

关键词： Collaborative filtering Recommender system stacked denoising autoencoder

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