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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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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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Life prediction of lithium-ion batteries based on stacked denoising autoencoders

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 208卷 107396-107396页

作者： Xu, Fan Yang, Fangfang Fei, Zicheng Huang, Zhelin Tsui, Kwok-Leung China Univ Geosci Sch Automat Wuhan 430072 Hubei Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Tong 83 Tat Chee Ave Hong Kong Peoples R China Shenzhen Univ Sch Econ Dept Stat Shenzhen 518061 Peoples R China Virginia Polytech Inst & State Univ Grad Dept Ind & Syst Engn 225 Durham Hall1145 Perry St Blacksburg VA 24061 USA

Accurate life prediction of lithium-ion batteries is important to help assess battery quality in advance, improve long-term battery planning, and subsequently guarantee the safety and reliability of battery operations. In this study, a deep learning-based stacked denoising autoencoder (SDAE) method is proposed to directly predict battery life by extracting various battery features. In general, the SDAE contains autoencoders and uses a deep network architecture to learn the complex nonlinear input-output relationship in a layer-by-layer fashion. Many features enabling the life prediction of lithium-ion batteries are extracted from discharge temperature and voltage curves. As redundancies in these features may result in poor prediction accuracy, a clustering by fast search (CFS) method is adopted to filter and select essential features. The CFS selects effective features by aggregating the types of battery features into clusters. All selected features are then fed into the SDAE to predict battery life cycle. Key hyperparameters are investigated, such as the number of iterations, the learning rate, and the denoising rate of the SDAE network. Experimental results show that the proposed selected-features-based deep learning method can provide more accurate and efficient battery life predictions with less fluctuation than the method without feature selection.

关键词： Lithium-ion battery Life prediction stacked denoising autoencoder Clustering by fast search Deep learning

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Intelligent sensor validation for sustainable influent quality monitoring in wastewater treatment plants using stacked denoising autoencoders

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JOURNAL OF WATER PROCESS ENGINEERING 2021年 43卷

作者： Ba-Alawi, Abdulrahman H. Vilela, Paulina Loy-Benitez, Jorge Heo, SungKu Yoo, ChangKyoo Kyung Hee Univ Dept Environm Sci & Engn Coll Engn Integrated Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea

Wastewater treatment plants (WWTPs) influent conditions can dramatically affect a treatment unit's state and effluent quality. WWTP sensors may record faulty measurements due to abnormal events or the malfunction of the system, leading to serious problems in the system's operation and the violation of effluent discharge standards. Therefore, automatic fault detection and faulty data reconciliation are crucial for an efficient and stable WWTP monitoring. In this study, a holistic framework for sensor validation of WWTP influent conditions is presented considering the non-linearity, measurement noise, and complexity of the WWTP's data. A stacked denoising autoencoder (SDAE) model is proposed to detect, identify, and reconcile faulty data based on data from a real WWTP in South Korea. The proposed SDAE architecture presented a detection rate (DR) between 74% and 98%. The faulty sensor was identified using an SDAE-based sensor validity index (SVI). Data reconciliation showed that the SDAE was the most suitable reconciliation method based on the root mean square error (RMSE) for total nitrogen (TN) influent conditions of 4.04 mg N/L. Finally, faulty, noisy, and reconciled measurements were evaluated in a WWTP model to determine the proposed method's resilience potential.

关键词： Fault detection stacked denoising autoencoder Sensor validation Smart monitoring Wastewater treatment

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Deep Learning Approach Based on Tensor-Train for Sparse Signal Recovery

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IEEE ACCESS 2019年 7卷 34753-34761页

作者： Zou, Cong Yang, Fang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Shenzhen Res Inst Key Lab Digital TV Syst Guangdong Prov & Shenzhen Shenzhen 518057 Peoples R China

Compressive sensing is a desirable technique to acquire and reconstruct signals at sub-Nyquist rates. Recently, several deep learning-based studies on solving the compressive sensing problem have been carried out, which dramatically reduce the intensive computational complexity of the traditional greedy or convex recovery algorithms and even improve the signal recovery performance. However, as the signal size increases, most of these methods recover signals block by block due to the large computational complexity and memory consumption, which usually imposes block effect on the recovered signals. To deal with this issue, in this paper, we apply a tensor decomposition method named Tensor-Train (TT) on the neural network, by which the number of parameters is reduced by a tremendous factor and the computational complexity is further decreased so that the large signals can be recovered as a whole. In particular, the TT-decomposition is jointly applied on a stacked denoising autoencoder (SDA) network called TT-SDA in this paper. The experiments indicate that the proposed TT-SDA network can preserve the reconstruction performance of the conventional SDA network and outperform the traditional methods, especially with low measurement rates. Meanwhile, it can also significantly reduce the computational complexity and occupied memory space, which becomes a time and memory efficient method in compressive sensing problem.

关键词： Compressive sensing deep learning Tensor-Train stacked denoising autoencoder

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Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2019年第2期13卷

作者： Xu, Junfeng Zhang, Baoming Guo, Haitao Lu, Jun Lin, Yuzhun Informat Engn Univ Inst Geospatial Informat Zhengzhou Henan Peoples R China

In order to make full use of local neighborhood information for high-resolution remote sensing images, this study combined iterative slow feature analysis (ISFA) and stacked denoising autoencoder (SDAE) to improve the change detection precision. First, this approach introduced ISFA for initial change detection in an unsupervised way, which enlarged the separability of changed and unchanged areas. Then, by setting different membership degrees, the changed and unchanged samples were obtained through fuzzy-means clustering. Finally, the change model was built by SDAE to represent the local neighborhood features deeply, and the change detection result can be obtained after all the samples were fed into the model. Experiments were performed on three real datasets, and the results validated the effectiveness and superiority of the proposed approach. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： change detection high-resolution remote sensing image deep learning slow feature analysis stacked denoising autoencoder

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Transfer learning for short-term wind speed prediction with deep neural networks

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RENEWABLE ENERGY 2016年第Jan.期85卷 83-95页

作者： Hu, Qinghua Zhang, Rujia Zhou, Yucan Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wind speed prediction Transfer learning Deep neural networks stacked denoising autoencoder

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An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram

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IEEE ACCESS 2018年 6卷 16529-16538页

作者： Xia, Yufa Zhang, Huailing Xu, Lin Gao, Zhifan Zhang, Heye Liu, Huafeng Li, Shou Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Guangdong Med Univ Sch Informat Engn Dongguan 523808 Peoples R China Gen Hosp Guangzhou Mil Command PLA Dept Cardiol Guangzhou 510000 Guangdong Peoples R China Zhejiang Univ Dept Opt Engn State Key Lab Modern Opt Instrumentat Hangzhou 310027 Zhejiang Peoples R China Western Univ London ON N6A 3K7 Canada

This paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer with Bluetooth 4.2. Then, these ECG data are classified by the automatic cardiac arrhythmia classification system. First, the ECG feature representation is learned by the SDAE with sparsity constraint. Then, the softmax regression is used to classify the ECG beats. In the fine-tuning phase, an active learning is added to improve the performance. In the active learning phase, we use the method that relies on the deep neural networks posterior probabilities to associate confidence measures to select the most informative samples. Breaking-ties and modified breaking-ties methods are used to select the most informative samples. We validate the proposed method on the well-known MIT-BIH arrhythmia database and ECG data obtained from the wearable device. We follow the recommendations of the Association for the Advancement of Medical Instrumentation for class labeling and results presentation. The results show that the classification performance of our proposed approach outperforms the most of the state-of-the-art methods.

关键词： stacked denoising autoencoder wearable device active learning breaking-ties modified breaking-ties

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A deep learning-based conditional system health index method to reduce the uncertainty of remaining useful life prediction

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SOFT COMPUTING 2023年第7期27卷 3641-3654页

作者： Jang, Jaeyeon Catholic Univ Korea Dept Data Sci 43 Jibong Ro Bucheon 14662 South Korea

Many recent data-driven studies have used sensor profile data for prognostics and health management (PHM). However, existing data-driven PHM techniques are vulnerable to three types of uncertainty: sensor noise inherent to the sensor profile data, uncertainty regarding the current health status diagnosis caused by monitoring a single health index (HI), and uncertainty in predicting the remaining useful life (RUL), which is affected by unpredictable changes in system operating conditions and the future external environment. This study proposes a deep conditional health index extraction network (DCHIEN) for PHM to effectively manage these three types of uncertainty. DCHIEN is a model that combines a stacked denoising autoencoder that extracts high-level features robust to sensor noise with a feed-forward neural network that produces an HI based on user-defined monitoring conditions. This approach supports system health monitoring using the conditional HI, as well as prognostics using RUL interval predictions. Extensive experiments were conducted using NASA's turbofan engine degradation dataset. The results show that the proposed method achieves a superior RUL prediction performance compared to state-of-the-art methods and that uncertainties can be effectively managed.

关键词： Health index Prognostics and health management Remaining useful life stacked denoising autoencoder Uncertainty management

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Sparsity-based autoencoders for denoising cluttered radar signatures

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IET RADAR SONAR AND NAVIGATION 2021年第8期15卷 915-931页

作者： Ram, Shobha Sundar Vishwakarma, Shelly Sneh, Akanksha Yasmeen, Kainat Indraprastha Inst Informat Technol New Delhi 110020 India UCL Dept Secur & Crime Sci London England

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (stackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the stackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of -10 dB and label mismatch error of 50%.

关键词： wall characteristics range enhanced frontal images indoor radar images cluttered radar signatures image resolution wall clutter radar clutter high range resolution sparsity based autoencoders free space images denoised ACR signatures noisy images image coding radar imaging stacked denoising autoencoder stackedSDAE dynamic clutter target independent static clutter diverse radar frequencies sparse denoising autoencoder Doppler time spectrograms clean images image denoising image reconstruction

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