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Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 109卷 159-170页

作者： Yang, Shuo Yin, Zhong Wang, Yagang Zhang, Wei Wang, Yongxiong Zhang, Jianhua Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai Key Lab Modern Opt Syst Minist Educ Engn Res Ctr Opt Instrument & Syst Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subject specific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

关键词： Mental workload Human-machine system Electroencephalogram stacked denoising autoencoder Deep learning

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A deep autoencoder feature learning method for process pattern recognition

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JOURNAL OF PROCESS CONTROL 2019年 79卷 1-15页

作者： Yu, Jianbo Zheng, Xiaoyun Wang, Shijin Tongji Univ Sch Mech Engn Shanghai 200082 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200082 Peoples R China

Recognition of various defect patterns exhibited in discrete manufacturing processes can significantly reduce the diagnostic processes, and increase manufacturing process stability and quality. Thus the effective recognizers are in great demand to improve the performance of process pattern recognition (PPR). Deep learning has been widely applied in image and visual analysis with great successes. However, the application of deep learning in feature learning for process control is still few. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for PPR in manufacturing processes. This paper will concentrate on developing an SDAE model to learn effective features from the process signals and then implementing an effective PPR through a deep network architecture. Feature visualization is also performed to explicitly present the feature representation of the proposed SDAE model. The effectiveness of the proposed PPR method is verified through a big simulation dataset and Tennessee Eastman process. The result shows that the proposed method obtains good feature learning and PPR performance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Manufacturing process Process pattern recognition Deep learning stacked denoising autoencoder Feature learning

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AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning

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JOURNAL OF NEUROSCIENCE METHODS 2020年第0期343卷 108840-108840页

作者： Wang, Yufei Wang, Jianxin Wu, Fang-Xiang Hayrat, Rahmatjan Liu, Jin Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Peoples R China Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 5A9 Canada

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. New method: To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. Results: Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. Comparison with existing methods: Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. Conclusion: The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.

关键词： Autism spectrum disorder identification Functional magnetic resonance imaging Functional connectivity stacked denoising autoencoder Ensemble learning

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Tensor-Train Based Deep Learning Approach for Compressive Sensing in Mobile Computing 15

Tensor-Train Based Deep Learning Approach for Compressive Se...

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15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

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

ISBN: (纸本)9781538677476

Several recent studies have been conducted on solving the compressive sensing problem with deep learning framework, which enhance the signal recovery performance and greatly shorten the running time compared with traditional algorithms. However, as the size of signals increases, so does the neural network, which will impose large memory space and high computational complexity, making it hard to use this method on mobile devices. To address this issue, in this paper, the neural network is decomposed by Tensor-Train (TT) format, which reduces the number of parameters to a great extent. In particular, the neural network decomposed by TT format is a stacked denoising autoencoder (SDA) network, which called TT-SDA. The experiments demonstrate that, especially with low measurement rates, the proposed TT-SDA network can improve the reconstruction results, reduce the computational complexity and save the memory space.

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

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An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive ...

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第三十九届中国控制会议

作者： Zhanpeng Zheng Zhong Yin Jianhua Zhang Engineering Research Center of Optical Instrument and System Ministry of EducationShanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and Technology

Evaluating operator cognitive workload（CW） levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human *** study proposes an inter-subject CW classifier,extreme learning machine（ELM）-based deep stacked denoising autoencoder ensemble（ED-SDAE）,to adapt the variations of the electroencephalogram（EEG） feature distributions across different *** ED-SDAE consists of two cascade-connected modules,which are termed as high level personalized feature abstractions and abstraction *** combination of SDAE and locality preserving projection（LPP） technique is regarded as base learner to obtain ensemble members for training meta-classifier by stacking-based *** ELM model with Q-statistics diversity measurement is acted as meta-classifier to fuse above inputs to improve classification *** feasibility of the SD-SDAE is tested by two EEG *** multi-class classification rate achieves 0.6353 and 0.6747 for T1 and T2 respectively,and significantly outperforms several shallow and deep CW *** computing the main time complexity,the computational workload of the ED-SDAE is also acceptable for high-dimensional EEG features.

关键词： Cognitive workload EEG ensemble learning extreme learning machine stacked denoising autoencoder

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Hybrid Deep Neural Network based on SDAE and GRUNN

Hybrid Deep Neural Network based on SDAE and GRUNN

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第三十九届中国控制会议

作者： Yingyong Zou Jun Yu Jiangen Tang Yongde Zhang School of Mechanical and Power Engineering Harbin University of Science and Technology College of Mechanical and Vehicular Engineering Changchun University Key Laboratory of Advanced Manufacturing and Intelligent Technology Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

stacked autoencoder(SAE) is hard to achieve satisfactory performance,when input data are complex and ***,the identification performance of recurrent neural network(RNN) may decrease rapidly under noisy *** order to deal with these problems,a novel hybrid deep neural network(DNN) based on stacked denoising autoencoder(SDAE) and gated recurrent unit neural network(GRUNN) is ***,the structure of the presented hybrid DNN is *** hybrid DNN contains a SDAE,a GRUNN,and a softmax ***,the training algorithm based on action discovery(AD) is proposed to train the presented hybrid *** experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.

关键词： deep neural network stacked denoising autoencoder gated recurrent unit neural network denoising ability action discovery

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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 End-to-End Model for Transient Stability Assessment With PMU Data

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IEEE ACCESS 2018年 6卷 65474-65487页

作者： Zhu, Qiaomu Chen, Jinfu Zhu, Lin Shi, Dongyuan Bai, Xiang Duan, Xianzhong Liu, Yilu Huazhong Univ Sci & Technol Sch Elect & Elect Engn Elect Power Secur & High Efficiency Key Lab State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China

Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.

关键词： Deep learning feature extraction PMU data stacked denoising autoencoder transient stability assessment

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Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach

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IEEE SENSORS JOURNAL 2018年第15期18卷 6399-6411页

作者： Wang, Tao He, Yigang Li, Bing Shi, Tiancheng Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China

This paper introduces a novel fault diagnosis approach for transformer based on self-powered radio-frequency identification (RFID) sensor and deep learning technique. The exploited RFID sensor tag with functionalities of signal collection, data storage, and wireless transmission employs surrounding electromagnetic field as power source. A customized power management circuit, including ac-dc converter, supercapacitor, and its corresponding charging circuit, is presented to guarantee constant de power for the sensor tag. The measured vibration signal contains miscellaneous noises and is characterized as nonlinearity and nonstationarity, so it is difficult to extract robust and useful features by using traditional feature extraction approaches. As one of the deep learning techniques, stacked denoising autoencoder (SDA) shows satisfactory performance in learning robust features from complex signal. Hence, in this paper, SDA approach is employed to learn robust and discriminative features from measured signals. The experimental results show that the presented power supply can generate 2.5-V de voltage, which is the rated operating voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 173 m in the test environment. Furthermore, the SDA approach shows satisfactory performance in learning robust and discriminative features. Experimental results indicate that the presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core.

关键词： Transformer fault diagnosis self-powered RFID sensor tag stacked denoising autoencoder

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Feature Ensemble Learning Using stacked denoising autoencoders for Induction Motor Fault Diagnosis 8

Feature Ensemble Learning Using Stacked Denoising Autoencode...

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Wang, Junwei Sun, Chuang Zhao, Zhibin Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538603703

Fault diagnosis is significant to induction motor which has been widely used as industrial power driving sources. By fault diagnosis, proper maintenance can be arranged to avoid accidents, ensure safety and reduce maintenance costs. However, variable operating conditions and background noise always reduce effectiveness of traditional fault diagnosis methods. Currently the most advanced machine learning technology, that is deep learning, not only has strong ability for hierarchical representations, but also can adaptively extract feature information from considerable measured data without necessary of much prior knowledge. Nevertheless, only the last layer is utilized for feature extraction in current deep learning models, due to its better performance of feature abstraction and compactness than that of the lower layers. Different feature layers hold different identification ability. In order to consider all features in different layers and achieve effective fault diagnosis, an alternative method of feature ensemble learning based on stacked denoising autoencoders (SDAE) is proposed in this paper. Our proposed method can be constructed in three steps: first, obtaining the samples of raw data under different health conditions. A deep neural network with multiple hidden layers is then established layer-by-layer by considering each pair of layers as a denoising autoencoder (DAE), with the strategies of greedy layer-wise pre-training and fine-tuning. Finally, softmax regression is created by the last hidden layer and integrate feature to classify different fault types. Feature ensemble is considered by mutually independent rules with different weights according to corresponding accuracy. Effectiveness of the proposed method is demonstrated though identifying health conditions of an induction motor.

关键词： fault diagnosis stacked denoising autoencoder feature ensemble learning

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