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Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders

EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Zheng, Zhanpeng Yin, Zhong Wang, Yongxiong Zhang, Jianhua Univ Shanghai Sci & Technol Engn Res Ctr Opt Instrument & Syst Shanghai Key Lab Modern Opt Syst Minist Educ Jungong Rd 516 Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Jungong Rd 516 Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

Machine learning approaches can build a computational model to predict cognitive workload levels by using electroencephalogram (EEG) feature inputs at the same time instant. However, when the EEG signals are recorded on different people, the accuracy of such a model could be impaired due to its incapability of fitting varied statistical distributions across different individuals. To this end, we propose an individual-independent workload estimator, a cascade ensemble of multilayer autoencoders to tackle the individual difference within the EEG features. It could assess the workload levels of an unseen subject by adapting the EEG data recorded from non-overlapped existing subjects. We first construct a deep stacked denoising autoencoder to abstract EEG features from a specific individual. Its shallow weights are optimized with individual-specific geometrical information of the features. Then, to find generalizable feature properties, we introduce Q-statistics to measure the independence between base learners. Finally, a regularized extreme learning machine is used as a cascade metaclassifier to fuse and filter high-level EEG abstractions and determine workload levels. We employ databases from two different experiments to validate our approach. The proposed framework can lead to acceptable accuracy and computational complexity compared to several existing workload classifiers.

关键词： Cognitive workload stacked denoising autoencoder Extreme learning machine Electroencephalogram Abstraction fusion

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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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Attention neural collaboration filtering based on GRU for recommender systems

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COMPLEX & INTELLIGENT SYSTEMS 2021年第3期7卷 1367-1379页

作者： Xia, Hongbin Luo, Yang Liu, Yuan Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Jiangsu Key Lab Media Design & Software Technol Wuxi Jiangsu Peoples R China

The collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user's preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, stacked denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items' latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

关键词： stacked denoising autoencoder Gated recurrent unit Attention mechanism Collaborative filtering Auxiliary information

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Deep Hawkes Process for High-Frequency Market Making 24

Deep Hawkes Process for High-Frequency Market Making

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Pankaj Kumar Jheronimus Academy of Data Science Netherlands

ISBN: (纸本)9798400704864

High-frequency market making is a liquidity-providing trading strategy that simultaneously generates many bids and asks for a security at ultra-low latency while maintaining a relatively neutral position. The strategy makes a profit from the bid-ask spread for every buy and sell transaction, against the risk of adverse selection, uncertain execution and inventory risk. We design realistic simulations of limit order markets and develop a high-frequency market making strategy in which agents process order book information to post the optimal price, order type and execution time. By introducing the Deep Hawkes process to the high-frequency market making strategy, we allow a feedback loop to be created between order arrival and the state of the limit order book, together with self- and cross-excitation effects. Our high-frequency market making strategy accounts for the cancellation of orders that influence order queue position, profitability, bid-ask spread and the value of the order. The experimental results show that our trading agent outperforms the baseline strategy, which uses a probability density estimate of the fundamental price. We investigate the effect of cancellations on market quality and the agent's profitability. We validate how closely the simulation framework approximates reality by reproducing stylised facts from the empirical analysis of the simulated order book data.

关键词： deep hawkes process high-frequency market making multi-agent-based model stacked denoising autoencoder

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Intrusion Detection Algorithm Based on SDA-ELM 1

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34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Wei, Xiaotao Ren, Shuyu Li, Yinglong Wang, Xi-Xi Jin, Mengxia Beijing Jiaotong Univ Sch Software Beijing Peoples R China

ISBN: (数字)9783030794637

ISBN: (纸本)9783030794620;9783030794637

A deep learning intrusion detection algorithm based on stacked denoising autoencoder and extreme learning machine (SDA-ELM) is proposed to solve the problem that the traditional machine learning algorithm can't cope with the classification of multisource heterogeneous network intrusion data. The algorithm use Dropout regularization to improve the SDA deep learning model, and the integration features of low dimensionality and high robustness are extracted. Then the model use ELM to carry out a supervised learning for the low dimension data to recognize the network attack. The algorithm combines the abstract feature extraction capability of SDA and the fast learning ability of ELM. The experimental results show that the SDA-ELM algorithm improves the accuracy of classification and the detection rate of small sample attacks, reduces the false alarm rate.

关键词： Intrusion detection stacked denoising autoencoder Extreme learning machine

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An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

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FUEL 2022年 308卷 121975-121975页

作者： Han, Zhezhe Li, Jian Hossain, Md Moinul Qi, Qi Zhang, Biao Xu, Chuanlong Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Univ Kent Sch Engn Canterbury CT2 7NT Kent England

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control and environmental protection. This study proposes a novel ensemble deep learning model for exhaust emissions (NOx and CO2) prediction. In this ensemble learning model, the stacked denoising autoencoder is established to extract the deep features of flame images. Four forecasting engines include artificial neural network, extreme learning machine, support vector machine and least squares support vector machine are employed for preliminary prediction of NOx and CO2 emissions based on the extracted image deep features. After that, these preliminary predictions are combined by Gaussian process regression in a nonlinear manner to achieve a final prediction of the emissions. The effectiveness of the proposed ensemble deep learning model is evaluated through 4.2 MW heavy oil-fired boiler flame images. Experimental results suggest that the predictions are achieved from the four forecasting engines are inconsistent, however, an accurate prediction accuracy has been achieved through the proposed model. The proposed ensemble deep learning model not only provides accurate point prediction but also generates satisfactory confidence interval.

关键词： Emission prediction Flame image Ensemble method stacked denoising autoencoder Gaussian process regression

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Fast probability estimation of HVDC successive commutation failure caused by AC grid cascading failures

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107618-107618页

作者： Zhu, Yuanzhen Liu, Tianhao Li, Changgang Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan 250061 Peoples R China

For line-commutated converter (LCC) based HVDC system, successive commutation failure (SCF) can result in continual cessations of power transmission or even forced blocking of the DC system. AC grid cascading failure near inverters is an important reason for the SCF. The SCF probability is utilized to represent the impact of AC grid cascading failures on DC systems. This paper proposes a fast probability estimation method of SCF based on sequential importance sampling (SIS) and stacked denoising autoencoder (SDAE). Firstly, the probability estimation of SCF caused by cascading failures is transformed into a statistical analysis problem. Secondly, considering AC line failures have different impacts on DC system, a cascading failure sampling method based on SIS is presented to efficiently generate cascading failure samples and evaluate the SCF probability. Thirdly, SDAE based assessment network is established to quickly assess the impact of AC failures on SCF. Topology features related to AC grid structure and short-circuit fault locations are selected as inputs of the assessment network. Finally, real-life hybrid AC/DC grids are adopted to demonstrate the validity of the proposed method.

关键词： Cascading failure HVDC Sequential importance sampling stacked denoising autoencoder Successive commutation failure

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Bone Age Assessment Based on Improved Deep Residual Networks 22

Bone Age Assessment Based on Improved Deep Residual Networks

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Yanna Liao Tao Zhang School of Electronic Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Bone age plays an important role in the scene of pediatrics and judicial identification, because the traditional bone age assessment is time-consuming, laborious and depends on the experience of physicians, the results of artificial bone age assessment will vary from person to person. This paper has collected the X-ray image from a class A tertiary children's hospital, and presents the convolutional neural network suitable for China 05 bone age assessment. For the collected Chinese 3-16-year-old youth hand bone image, using the stacked denoising autoencoder (SDAE) combined with ResNet50, while reducing the noise of soft tissue and effectively improving the feature extraction ability of the model; Secondly, the 3×3 convolution in the ResNet50 residual block is replaced with a pyramid split attention (PSA) module to get the new model, fusion multi-level features of space and channel attention, adapt to re-define features; Presents the adaptive dual-channel pooling layer by combining the max pooling and average pooling; Use pre-excitement to speed up convergence and label smooth loss function to prevent the model from overfitting, and finally establish a deep learning classification model for China 05 bone age assessment. The experimental results show that the accuracy of ±1 year in this method reaches 93.22% of men, and 91.71% of women. The Mean Absolute Error (MAE) also decreases.

关键词： Bone age assessment Image processing stacked denoising autoencoder

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A denoising and multiscale residual deep network for soft sensor modeling of industrial processes

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第10期33卷 105117-105117页

作者： Gao, Renzhi Zhu, Hegong Wang, Gang Wu, Zhangjun Hefei Univ Technol Sch Management Hefei Anhui Peoples R China Hefei Univ Technol Minist Educ Key Lab Proc Optimizat & Intelligent Decision Mak Hefei Anhui Peoples R China Res Ctr Intelligent Decision Making & Informat Sy Minist Educ Engn Hefei 230009 Peoples R China

Deep learning plays an important role in soft sensors of industrial processes for the timely measurement of key quality variables. However, since sensors are often operated under noisy and nonstationary industrial conditions, the collected industrial process data exhibit extreme complexity, which severely restricts the learning capacity and measurement accuracy of deep learning methods. In this paper, a novel denoising and multiscale residual deep network (DMRDN) is proposed for soft sensor modeling. Firstly, a stacked denoising autoencoder with level-aware attention is developed to denoise the process data, in which denoised features on different levels are learned and fused. Secondly, the denoised features are fed into multiscale residual convolutional neural network with scale-aware attention, which is designed to capture and fuse deep dynamic features from different scales. Finally, experiments were conducted on an industrial debutanizer column. The experimental results demonstrate that the proposed DMRDN greatly strengthens the learning ability and achieves better prediction performance compared with other methods.

关键词： soft sensor deep learning stacked denoising autoencoder convolutional neural network denoising and multiscale residual deep network

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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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