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

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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Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

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INFRARED PHYSICS & TECHNOLOGY 2024年 142卷

作者： Xu, Min Sun, Jun Cheng, Jiehong Yao, Kunshan Shi, Lei Zhou, Xin Changzhou Coll Informat Technol Sch Elect Engn Changzhou 213164 Jiangsu Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Changzhou Inst Technol Sch Elect & Informat Engn Changzhou 213032 Jiangsu Peoples R China

Grape shelf-life estimation is a substantial challenge for the grape industry. The objective of this study is to investigate the potential of grape shelf-life estimation using HSI technique and a deep learning algorithm. The visible and near-infrared (400.68-1001.61 nm) hyperspectral reflectance images data of grape samples was acquired and preprocessed with different spectral preprocessing methods. Additionally, a stacked denoising autoencoder (SDAE)-based deep learning algorithm was developed to extract deep features from pixel-level hyperspectral data of grapes, and then these features were used as inputs to establish support vector machine (SVM) models for estimating grape shelf-life. Furthermore, SVM, one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) models were used as traditional machine learning and end to end models for comparison. The results demonstrated that the SDAE-SVM model achieved reasonable recognition accuracy of 100 % and 98.125 % for the shelf-life of grapes in the training and test sets, respectively. The overall results suggested that SDAE-based deep learning method can be used as a powerful tool to deal with large-scale hyperspectral data as well as this research confirms the feasibility of non-destructive estimation for grapes shelflife by the combination of HSI technique and deep learning method, which would provide a valuable guidance for shelf-life estimation of other postharvest fruit.

关键词： Hyperspectral imaging Deep learning Fruit shelf-life stacked denoising autoencoder Kyoho grape Non-destructive estimation

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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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Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

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SENSORS 2018年第4期18卷 1129-1129页

作者： Duong, Bach Phi Kim, Jong-Myon Univ Ulsan Sch Elect Elect & Comp Engn Ulsan 44610 South Korea

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

关键词： bearing fault diagnosis combined mode classification deep neural network stacked denoising autoencoder

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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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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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Day-ahead Wind Power Forecasting Based on Single Point Clustering

Day-ahead Wind Power Forecasting Based on Single Point Clust...

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第37届中国控制会议

作者： Song Jiakang Peng Yonggang Xia Yanghong College of Electrical Engineering Zhejiang University

Wind power forecasting is significant for the security and economy of the power system. For the better robustness and accuracy, various clustering-based forecasting methods have been proposed. However, most of them utilize clustering on a daily sample basis usually called day clustering, which mainly exploits daily features of numerical weather prediction(NWP) and meets some limitations. In this paper, a single point clustering method is proposed to enhance the performance of day-ahead forecasting, which is conducted on a point sample basis through K-means algorithm. First, the extraction of clustering features is optimized. Being different from the commonly used clustering features, the high-order features extracted from NWP is used for the proposed method based on stacked denoising autoencoder(SDAE). Then, both the compactness within the single group and the separation among the multiple groups are enhanced, which is realized through choosing the number of clusters to maximize silhouette. In the case study, the proposed method is compared to the conventional day clustering method with two types of features and the single point clustering with the commonly used clustering features of NWP. Through the results, it is shown that the proposed method performs better than the other comparative methods.

关键词： Day-ahead forecasting single point clustering stacked denoising autoencoder silhouette K-means

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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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Research and application of intrusion detection method based on hierarchical features

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第16期34卷 e5799-e5799页

作者： Xie, Xin Jiang, Xunyi Wang, Weiru Wang, Bin Wan, Tiancheng Tang, Wenliang Wang, Xianmin East China Jiaotong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Guangzhou Univ Sch Comp Sci Guangzhou Peoples R China

Intrusion detection is essential to prevent damage to computer systems. However, in recent years, with the development of the network, many complex attack types have appeared, and it has become increasingly difficult to obtain high detection rates and low false alarm rates. In addition, traditional heavily hand-crafted evaluation datasets for network intrusion detection have not been practical. This article proposes an intrusion detection method based on hierarchical feature learning, which can automatically learn traffic features. The method first learns the byte-level features of network traffic through one-dimensional convolutional neural networks and then learns session-level features using stacked denoising autoencoder. The experiment analyzed the model structure and compared it with other methods. Experiments prove that the method in this article has high accuracy and low false alarm rate.

关键词： deep learning hierarchical learning of features intrusion detection 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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