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10th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Zhou, Xi Guo, Junqi Wang, Shenling Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9783319218373;9783319218366

Due to the increasing machine learning applications in mobile health and security monitoring scenarios, human motion recognition by sensor devices has received remarkable attention from both academic and engineering fields. In this paper, we propose a motion recognition scheme by using a stacked autoencoder based deep learning algorithm with smart phones. Since common sensors such as gravity sensors, accelerometers, gyroscopes, linear accelerometers and magnetometers have been already equipped in Android or iOS based smart phones, the sensor data can be easily recorded by the smart phone that an experimenter carries around. A stacked autoencoder based deep learning algorithm is employed here for data classification so as to precisely recognize several basic motions that are standing, walking, sitting, running, going upstairs and going downstairs, respectively. Experimental results indicate that the stacked autoencoder based deep learning algorithm achieves higher accuracy for human motion recognition than traditional neural network methods.

关键词： Motion recognition stacked autoencoder Deep learning Smart phones

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Virtual Battery Parameter Identification using Transfer Learning based stacked autoencoder 17

Virtual Battery Parameter Identification using Transfer Lear...

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

作者： Chakraborty, Indrasis Nandanoori, Sai Pushpak Kundu, Soumya Pacific Northwest Natl Lab Optimizat & Control Grp Richland WA 99354 USA

ISBN: (纸本)9781538668054

Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a transfer learning based deep network framework for calculating virtual battery state of a given ensemble of flexible thermostatic loads, from the available end-use measurements. This proposed framework extracts first order virtual battery model parameters for the given ensemble. We illustrate the effectiveness of this novel framework on different ensembles of ACs and WHs.

关键词： stacked autoencoder Convolution Neural Network LSTM Virtual Battery

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Probabilistic Prediction of Solar Generation Based on stacked autoencoder and Lower Upper Bound Estimation Method

Probabilistic Prediction of Solar Generation Based on Stacke...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pan, Cheng Tan, Jie Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781728169262

The lower upper bound estimation method is an important probabilistic prediction method and has been applied to the solar generation forecasting. However, when the input dimension of the lower upper bound estimation method is large, its performance will be seriously affected. To overcome this challenge, a novel probabilistic prediction of solar generation based on stacked autoencoder and lower upper bound estimation method is proposed. In this method, stacked autoencoder is first used to obtain highly compressed features, which are utilized as the input of the lower upper bound estimation method. Besides, to make the target value in the center of the prediction interval as much as possible, inspired by the idea of support vector machine, the mean squared error of prediction interval is introduced to the loss function, which keeps the target value as far as possible from the lower and upper bounds of the prediction interval. To verify the performance of the proposed method, a large number of experiments have been carried out on the freely available dataset. The results show that the proposed method has better forecasting performance.

关键词： stacked autoencoder lower upper bound estimation solar generation forecasting probabilistic prediction

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Anomaly detection of organic pollution in drinking water based on fluorescence spectroscopy and stacked autoencoder

Anomaly detection of organic pollution in drinking water bas...

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Chinese Automation Congress (CAC)

作者： Shi, Jiegen Cao, Yitong Yu, Jie Hou, Dibo Ren, Yongqin Zhang, Guangxin Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Natl Ocean Technol Ctr Tianjin Peoples R China

ISBN: (纸本)9781728176871

In this paper, a three-dimensional fluorescence anomaly detection method for organic pollutants of drinking water is studied, and a normal water sample reconstruction model based on stacked autoencoder is proposed. The organic contamination events are detected by the residual of the reconstructed output and the original input of samples with the 3 sigma criterion, and the algorithm is validated on the common organic pollutants such as phenol, salicylic acid and rhodamine B in drinking water. The results show that the algorithm can effectively detect both high-concentration and low-concentration organic pollutants under the premise of ensuring low false alarm rate when affected by fluctuations in water quality background. The method proposed is proved to have the potential to improve the sensitivity and robustness of anomaly detection of organic pollution in drinking water.

关键词： 3D fluorescence spectroscopy organic pollutant anomaly detection stacked autoencoder

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A Novel Bearing Health Indicator Construction Method Based on Ensemble stacked autoencoder

A Novel Bearing Health Indicator Construction Method Based o...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Lin, Pengfei Tao, Jizhong China Acad Engn Phys Institude Machinery Mfg Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9781538683576

In the area of data-driven bearing prognostic, the construction of health indictor from condition monitoring data is important. This paper presents a novel bearing health indicator construction method based on ensemble stacked autoencoders. Firstly, the proposed ensemble stacked autoencoders extract features directly from the FFT results of raw vibration signals. Then, a deep neural network which serves as a non-linear transformation is trained to map the multi-dimensional learned features to a one-dimensional health indicator. Finally, the proposed method is validated using the IEEE PHM2012 Challenge dataset. To show the superiority of the proposed method, its performance is evaluated and compared with other methods. The results demonstrate that the proposed method can automatically and effectively build high-quality health indictor from raw data without any signal processing and manual feature engineering.

关键词： health indicator construction deep learning ensemble learning stacked autoencoder bearing

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Semisupervised Classification for Industrial Process Faults Using Modified Fisher Discriminant Analysis Assisted by stacked autoencoder

Semisupervised Classification for Industrial Process Faults ...

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Chinese Automation Congress (CAC)

作者： Deng, Xiaogang Dai, Jiabing Cao, Yuping China Univ Petr Coll Control Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781728140940

Fisher discriminant analysis (FDA) is an effective fault classification method for complicated industrial processes. However, it builds the classifier only based on the labeled training data and ignores vast amounts of unlabeled data which can provide important data distribution information. To well utilize the unlabeled data for better fault classification performance, this paper presents a semisupervised FDA (SeFDA) method which integrates the stacked autoencoder (SAE) for intrinsic feature extraction and applies the majority voting based ensemble modeling strategy for a more precise solution. Different to the traditional FDA method, the proposed method builds the classifier by considering the labeled and unlabeled samples simultaneously. SeFDA first applies the SAE on the whole data samples to extract the data features. Then the data features corresponding to the labeled samples are used to train a fault discriminant model by FDA. Considering that the single SAE network training is easily affected by the initial weight values, a majority voting based ensemble modeling strategy is applied to combine the results from multiple classifiers. The case studies on the simulated Tennessee Eastman process and the real sucker rod pumping system show that the modified SeFDA method is superior to the basic FDA in terms of fault classification performance.

关键词： Fisher discriminant analysis stacked autoencoder semisupervised learning

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stacked autoencoder based diagnosis applied on a Solar Photovoltaic System

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IFAC-PapersOnLine 2024年第4期58卷 384-389页

作者： Mouaad Bougoffa Samir Benmoussa Mohand Djeziri Thierry Contaret Badji Mokhtar-Annaba University LASA Laboratory Annaba Algeria Aix-Marseille Univ Univ de Toulon CNRS IM2NP Marseille France

This paper deals with the modeling of a photovoltaic system connected to a grid for the simulation of normal and faulty operations and the generation of a data-set for learning a fault detection algorithm based on a stacked autoencoder. To evaluate the effectiveness of the proposed approach, a Mean Squared Error is used. This method enables early fault detection, enhancing system relability and efficiency while addressing the need for proactive fault management in the system under normal conditions. Obtained results under different radiation and temperature conditions highlight the relevance of the proposed model and the effectiveness of the fault detection algorithm.

关键词： Photovoltaic fault detection Mean Squared Error stacked autoencoder

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Multifractal analysis and stacked autoencoder-based feature learning method for multivariate processes monitoring 41

Multifractal analysis and stacked autoencoder-based feature ...

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第41届中国控制会议

作者： Feng Yu Jianchang Liu Liangliang Shang Dongming Liu Department of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering Nantong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Deep learning has become a popular tool for fault detection in industrial processes to learn complex nonlinear ***,the features extracted from most traditional deep networks usually ignore the geometric characters and singularities of the process *** representative features cannot be extracted effectively,which may lead to the inaccurate modeling and is not beneficial for the process ***,this paper proposes a feature learning method based on multifractal analysis and stacked autoencoder(MF-SAE) for fault detection in complex multivariate ***-SAE can learn high-level multifractal features from the raw data in an unsupervised *** analysis is frrst introduced to extract the multi-scale self-similar characteristics from industrial process data,in which sigmoid function is added for preprocessing the process *** to the redundant information existing in the multi-scale feature,SAE is then utilized to learn key feature from the extracted multifractal *** learned hidden feature and residual feature are provided to construct the monitoring *** fault detection performance of MF-SAE is tested on the Tennessee Eastman(TE) process.

关键词： Deep learning Industrial process Multifractal analysis stacked autoencoder

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Bearing Fault Diagnosis Method Based on stacked autoencoder and Softmax Regression 34

Bearing Fault Diagnosis Method Based on Stacked Autoencoder ...

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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

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Cell Classification Based on stacked autoencoder for Single-Cell RNA Sequencing 18th

Cell Classification Based on Stacked Autoencoder for Single-...

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18th International Conference on Intelligent Computing (ICIC)

作者： Qi, Rong Zheng, Chun-Hou Ji, Cun-Mei Yu, Ning Ni, Jian-Cheng Wang, Yu-Tian Qufu Normal Univ Sch Cyber Sci & Engn Qufu Shandong Peoples R China Anhui Univ Sch Artifial Intelligence Hefei Peoples R China Qufu Normal Univ Network Informat Ctr Qufu Shandong Peoples R China

ISBN: (纸本)9783031138294;9783031138287

Single-cell transcriptome sequencing (scRNA-seq) allows researchers to investigate cellular heterogeneity in gene expression profiles, identify cell types and predict cell fate at the single-cell level. Analysis of scRNA-seq data can effectively extract unknown heterogeneity and functional diversity of cell populations. Cell classification is one of the most important tasks in scRNA-seq data analysis, which contains cell clustering and classification of cell subtypes. Before assigning a cell type to each cluster, the unsupervised clustering methods look for marker genes for each cluster. These approaches are susceptible to a number of drawbacks in terms of sources of variation, technology, etc. Meanwhile, as more cell subtypes are gradually discovered, cluster-based cell type identification methods have been gradually leaning towards classification-based cell type identification. In this paper, we proposed a new cell classification method based on stacked autoencoder for representation learning (scSAERLs), which enhances the accuracy of classification by learning the feature representation of the data through deep network models. The stacked autoencoder-based classification model employed an unsupervised greedy pre-training learning procedure, which was followed by supervised label-based fine-tuning of the entire classification model. We tested the model in the intra and inter datasets, evaluated its performance with a standard classification metric. Experimental results showed that scSAERLs outperformed other commonly used classification methods in terms of classification accuracy and F1-score.

关键词： scRNA-seq Representation learning stacked autoencoder Classification

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