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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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A Comparative Study of Object Tracking using CNN and SDAE

A Comparative Study of Object Tracking using CNN and SDAE

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang, Wei Wang, Wei Gao, Yang Jin, Zhanpeng SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA Univ Buffalo SUNY Dept Comp Sci & Engn New York NY 14260 USA

ISBN: (纸本)9781509060146

Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.

关键词： object tracking convolutional neural network stacked denoising autoencoder particle filter

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Classification of EEG Signals for Cognitive Load Estimation Using Deep Learning Architectures 10th

Classification of EEG Signals for Cognitive Load Estimation ...

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10th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Saha, Anushri Minz, Vikash Bonela, Sanjith Sreeja, S. R. Chowdhury, Ritwika Samanta, Debasis Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9783030040215;9783030040208

Measuring cognitive load is crucial for many applications such as information personalization, adaptive intelligent tutoring systems, etc. Cognitive load estimation using Electroencephalogram (EEG) signals is widespread as it produces clear indications of cognitive activities by measuring changes of neural activation in the brain. However, the existing cognitive load estimation techniques are based on machine learning algorithms, which follow signal denoising and hand-crafted feature extraction to classify different loads. There is a need to find a better alternative to the machine learning approach. Of late, deep learning approach has been successfully applied to many applications namely, computer vision, pattern recognition, speech processing, etc. However, deep learning has not been extensively studied for the classification of cognitive load data captured by an EEG. In this work, two deep learning models are studied, namely stacked denoising autoencoder (SDAE) followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data. SDAE and LSTM are used for feature extraction and MLP for classification. It is observed that deep learning models perform significantly better than the conventional machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA).

关键词： Cognitive load stacked denoising autoencoder Long short term memory Multilayer perceptron

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State Assessment and Fault Prediction Method of Distribution Terminal Based on SDAE and Hierarchical Bayesian

State Assessment and Fault Prediction Method of Distribution...

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Sustainable Power and Energy Conference (iSPEC)

作者： Runmiao Liu Shiyong Feng Yueming Cai Mingxiang Liu NARI Group Co. Ltd State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781728149318

State Assessment and Fault Prediction mechanism of distribution terminal is the premise of ensuring safe and reliable operation of power grid. However, the sample size of fault rate data of distribution terminal is usually small and the data is missing seriously, so it is difficult to accurately evaluate and analyze the fault rate data. A reliability assessment and prediction model based on stacked denoising autoencoder and hierarchical bayesian is proposed in this paper. Firstly, the quantile method is used to detect abnormal values of distribution terminals. Then, features of fault data is extracted by stacked denoising autoencoder(SDAE), and the Hierarchical Bayesian(HB) model is used to evaluate the failure rate data of distribution terminals. Finally, the evaluation method is compared the traditional prior distribution and the second-order linear regression. The results show that the proposed method improves the accuracy of fault assessment by 5.37% and 10.09%, the accuracy of prediction by 10.23% and 19.94%. The proposed method has higher reliability, prediction efficiency and evaluation accuracy.

关键词： Distribution terminal Hierarchical Bayesian (HB) stacked denoising autoencoder fault assessment fault prediction stacked denoising autoencoder (SDAE) failure rates reliability evaluation Bayesian inference faults Hierarchical Prediction methods Power grids Noise reduction evaluation method

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Machine Learning for Data Reduction in Quantum State Tomography 37

Machine Learning for Data Reduction in Quantum State Tomogra...

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37th Chinese Control Conference (CCC)

作者： Liu, Ximin Lu, Sicong Wu, Rebing Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563958

The purpose of quantum state tomography (QST) is to obtain a complete quantum state by reconstructing a density matrix from experimental data and therefore gives researchers a powerful tool to analyze complex synthetic quantum systems. In the past decades, many methods have been developed to improve the efficiency of quantum state tomography (QST), such as old methods: compress sensing on almost pure state and low-rank systems, new method: using restricted Boltzmann machine on systems of quantum many-body. However, they are both limited to some certain systems. In this paper, we introduce an universal method using deep neural networks based on stacked denoising autoencoder, which probably can help finish QST on all types of systems with a much smaller number of measurements and therefore is very efficient.

关键词： quantum state tomography deep neural networks stacked denoising autoencoder

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Dual denoising autoencoder Features for Imbalance Classification Problems 10

Dual Denoising Autoencoder Features for Imbalance Classifica...

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EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

作者： Wang, Ting Zeng, Guangjun Ng, Wing W. Y. Li, Jinde South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538630662

In pattern classification problems, it is difficult to force all classes to have the same number of training samples. Undersampling-based methods loss information while oversampling-based methods easily overfit. Therefore, the Dual autoencoders Features (DAF) relieves the imbalanced pattern classification problem via learning a set of better features to project samples in different classes onto a more distinguishable space. However, the feature set learned by the DAF may not be robust to partial corruption of input patterns. Therefore, this work proposes a Dual denoising autoencoders Features (DDAF) to learn a more robust set of features to contaminated or destroyed training datasets. Experimental results show that the DDAF outperforms existing resampling-based methods and the DAF for imbalanced pattern classification problems.

关键词： imbalanced classification feature learning stacked denoising autoencoder

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A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2017年第1期30卷 23-31页

作者： Lee, Hoyeop Kim, Youngju Kim, Chang Ouk Yonsei Univ Dept Informat & Ind Engn Seoul 03722 South Korea

Standard fault detection and classification (FDC) models detect wafer faults by extracting features useful for fault detection from time-indexed measurements of the equipment recorded by in situ sensors (sensor signals) and feeding the extracted information into a classifier. However, the preprocessing-and-classification approach often results in the loss of information in the sensor signals that is important for detecting wafer faults. Furthermore, the sensor signals usually contain noise induced by mechanical and electrical disturbances. In this paper, we propose the use of a stacked denoising auto-encoder (SdA), which is a deep learning algorithm, to establish an FDC model for simultaneous feature extraction and classification. The SdA model can identify global and invariant features in the sensor signals for fault monitoring and is robust against measurement noise. Through experiments using wafer samples collected from a work-site photolithography tool, we confirmed that as the sensor measurement noise severity increased, the SdA's classification accuracy could be as much as 14% higher than those of the twelve models considered for comparison, each of which employed one of three feature extractors and one of four classifiers.

关键词： Semiconductor manufacturing fault detection and classification sensor measurement noise deep learning stacked denoising autoencoder

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Deep learning based total transfer capability calculation model

Deep learning based total transfer capability calculation mo...

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International Conference on Power System Technology (POWERCON)

作者： Yan, Jiongcheng Li, Changgang Liu, Yutian Shandong Univ Sch Elect Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781538664612

A total transfer capability (TTC) calculation model based on stacked denoising autoencoder (SDAE) is proposed in this paper, considering static security, static voltage stability and transient stability constraints. The TTC calculation model consists of feature pre-screening, SDAE and the regression layer. Fast correlation-based filter (FCBF) is used to eliminate irrelevant and redundant features to improve the training efficiency of SDAE. SDAE takes advantage of the deep structure to extract high-order features relevant to TTC from original features. The regression layer is utilized to create the mapping between high-order features and the TTC value. Experiment results of a real power system demonstrate that the proposed TTC calculation model has higher computational accuracy than shallow machine learning models and feature pre-screening decreases the training time of the TTC calculation model obviously.

关键词： total transfer capability deep learning stacked denoising autoencoder fast correlation-based filter

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Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

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JOURNAL OF MEDICAL IMAGING 2017年第4期4卷 041311页

作者： Alex, Varghese Vaidhya, Kiran Thirunavukkarasu, Subramaniam Kesavadas, Chandrasekharan Krishnamurthi, Ganapathy Indian Inst Technol Madras Dept Engn Design Madras Tamil Nadu India Sree Chitra Tirunal Inst Med Sci & Technol Dept Radiol Trivandrum Kerala India

The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients (n = 20, 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database. (c) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： brain lesion gliomas magnetic resonance imaging deep learning stacked denoising autoencoder denoising autoencoder

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A deep learning ensemble approach for crude oil price forecasting

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ENERGY ECONOMICS 2017年 66卷 9-16页

作者： Zhao, Yang Li, Jianping Yu, Lean Chinese Acad Sci Inst Policy & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing Peoples R China

As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite challenging, and thus leads to the difficulty of forecasting. In this study, a deep learning ensemble approach is proposed to deal with this problem. In our approach, two techniques are utilized. One is an advanced deep neural network model named stacked denoising autoencoders (SDAE) which is used to model the nonlinear and complex relationships of oil price with its factors. The other is a powerful ensemble method named bootstrap aggregation (bagging) which generates multiple data sets for training a set of base models (SDAEs). Our approach combines the merits of these two techniques and is especially suitable for oil price forecasting. In the empirical study, the WTI crude oil price series are investigated and 198 economic series are used as exogenous variables. Our approach is tested against some competing approaches and shows superior forecasting ability that is statistically proved by three tests. (C) 2017 ElsevierB.V. All rights reserved.

关键词： Crude oil price forecasting Deep leaming Ensemble learning stacked denoising autoencoder Bagging Multivariate forecasting

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