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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Xiuli Damarla, Seshu Kumar Huang, Biao University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China University of Alberta Department of Chemical and Materials Engineering AB Canada

ISBN: (纸本)9798350319804

Process data with characteristics such as strong nonlinearity, high dimensionality, cross-correlations and auto correlations pose a great challenge for data-driven soft sensor modeling. Albeit the conventional stacked autoencoder (SAE) is able to learn low dimensional nonlinear features from the process data, it fails to extract spatial and temporal features. To overcome this shortcoming, in the present work, spatiotemporal SAE (STSAE) is proposed based on convolutional and LSTM layers. Lower dimensional nonlinear features (spatial and temporal) extracted from STSAE are used to build the soft sensor. The effectiveness of the STSAE-based soft sensor is demonstrated in an industrial case study, the Dow data challenge problem. For the purpose of comparison, soft sensor models based on popular machine learning algorithms are developed and applied to the industrial case study. The obtained results demonstrate that the proposed STSAE based soft sensor yields more accurate estimations for impurity level at the primary column outlet of Dow process refining system. © 2023 IEEE.

关键词： autoencoder CNN Dow data challenge problem LSTM stacked autoencoder

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stacked autoencoder based Intrusion Detection System using One-Class Classification 11

Stacked Autoencoder based Intrusion Detection System using O...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Gupta, Prabhav Ghatole, Yash Reddy, Nihal Mu Sigma Inc Bangalore Karnataka India BITS Pilani Dept Comp Sci Hyderabad India

ISBN: (纸本)9781665414517

The paper presents a study of deep learning based approach for Intrusion Detection System. Already existing models for classification were based on supervised learning methods which fails to classify instances of unknown attacks. Effective prediction of network packets as normal or attack, known and unknown to the model, is imperative requiring detection with minimal false alarm rate. Even for the attacks not known to (he model, stacked autoencoder turns out to be one such deep learning architecture which identifies complex pattern leading to generation of the best latent representation of inputs. The proposed model was (rained on single labeled instances from KDD Cup 99 dataset along with standardizing the inputs using batch normalization to minimize the problem of internal covariance shift and vanishing gradient to some extent. Experimental results obtained show that the proposed method outperforms all the other algorithms giving accuracy of 98.17% and false alarm rate of 0.38%.

关键词： IDS Anomaly detection One-Class classification stacked autoencoder Batch Normalization

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Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第10期35卷 106132-106132页

作者： Chen, Zhe Li, Yonghua Gong, Qi Wang, Denglong Yin, Xuejiao Dalian Jiaotong Univ Coll Locomot & Rolling Stock Engn Dalian 116028 Peoples R China Dalian Jiaotong Univ Key Lab Railway Ind Safety Serv Key Technol Highsp Dalian 116028 Peoples R China

Remaining useful life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on stacked autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal component analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

关键词： remaining useful life prediction stacked autoencoder generalized wiener process degradation modeling feature extraction

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Sense-Through-Foliage Target Detection Based on stacked autoencoder and UWB Radar Sensor Networks 10th

Sense-Through-Foliage Target Detection Based on Stacked Auto...

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10th International Conference on Communications, Signal Processing, and Systems

作者： Mao, Chengchen Liang, Qilian Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9789811903908;9789811903892

In this paper, we proposed a stacked autoencoder (SAE)-based approach to ultra wide band (UWB) radar for sense-through-foliage target detection. As one of the widely used deep learning structures, SAE could learn representations of data with multiple levels of abstraction automatically. Processing the poor signal collections, in some positions, the SAE-based target detection approach performed well. While in other positions, a single radar target detection performed under satisfaction, RAKE structure was applied in radar sensor networks with maximum ratio combing and equal combine to integrate radar echoes from different radar cluster-members. The experimental results showed that the RAKE-SAE-based method could qualify the mission of target detection.

关键词： Ultra-WideBand radar Sense-through-foliage Target detection Radar sense network stacked autoencoder

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Deep stacked autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2022年第1期15卷 1-26页

作者： Pitchaiyan, Shanthi Savarimuthu, Nickolas Natl Inst Technol Dept Comp Applicat Tiruchirappalli India

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local binary pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a hybrid local texture descriptor (HLTD) that is derived from the logical fusion of local neighborhood XNOR patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the deep stacked autoencoder (DSA) is established on the CK+, MMI, and KDEF-dyn dataset, and the results show that the proposed HLTD-based approach outperforms many of the state-of-the-art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI, and 88.5% for KDEF.

关键词： Deep Neural Network Emotion Facial Expression Feature Fusion Hybrid Local Texture Descriptor stacked autoencoder

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Classification of Silent Speech in English and Bengali Languages Using stacked autoencoder

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SN Computer Science 2022年第5期3卷 389页

作者： Ghosh, Rajdeep Sinha, Nidul Phadikar, Souvik School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kotri Kalan 466114 India Department of Electrical Engineering NIT Silchar Assam Silchar 788010 India

The purpose of a brain–computer interface (BCI) is to enhance or support the normal functions of disabled people, and as such, BCIs have been utilized for a variety of applications, such as prostheses and identification of mental state. One such application concerned with providing a means of communication for disabled individuals is focused on the recognition of silent speech (also known as imagined speech) in an individual. Silent speech can be defined as the speech originating inside the brain of an individual that has not been vocalized by the individual. The proposed work is concerned with the classification of silent speech from the brain activity of an individual recorded using an electroencephalogram (EEG). EEG data from 45 subjects were collected while they imagined the English vowels /a/, /e/, /i/, /o/, and /u/ without vocalization. EEG data were also recorded from 22 subjects who imagined five Bengali vowels /আ/, /ই/, /উ/, /এ/ and /ও/ without vocalization. The selected Bengali vowels have a similar pronunciation to the English vowels. Various temporal and spectral features were evaluated from the EEG recordings, which were then classified using a stacked autoencoder (SAE). The SAE achieved an accuracy of 75.56% and 73.6% in classifying the silent speech from the English and Bengali languages, respectively. Moreover, it has been observed that the proposed SAE outperforms conventional methods such as common spatial pattern (CSP) and support vector machine (SVM) during classification. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Brain–computer interface Deep learning Electroencephalogram Imagined speech Silent speech stacked autoencoder

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Enhancing Top-N Recommendation Using stacked autoencoder in Context-Aware Recommender System

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NEURAL PROCESSING LETTERS 2021年第3期53卷 1865-1888页

作者： Abinaya, S. Devi, M. K. Kavitha Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user's preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.

关键词： Context-aware recommender systems Item splitting Sentiment analysis stacked autoencoder

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A Data-Driven Health Monitoring Method Using Multiobjective Optimization and stacked autoencoder Based Health Indicator

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6379-6389页

作者： Chen, Zhiwen Guo, Rongjie Lin, Zhi Peng, Tao Peng, Xia Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

关键词： Optimization Feature extraction Indexes Monitoring Sorting Sociology Statistics Data driven health monitoring multiobjective optimization stacked autoencoder

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A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第6期18卷 8024-8044页

作者： Seraphim, B. Ida Poovammal, E. Ramana, Kadiyala Kryvinska, Natalia Penchalaiah, N. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Annamacharya Inst Technol & Sci Dept Artificial Intelligence & Data Sci Rajampet India Comenius Univ Fac Management Informat Syst Dept Odbojarov 10 Bratislava 82005 25 Slovakia Annamacharya Inst Technol & Sci Dept CSE Rajampet India

Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed stacked autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.

关键词： intrusion detection system (IDS) stream data mining stacked autoencoder DPSO Hoeffding tree feature selection

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Distributed-ensemble stacked autoencoder model for nonlinear process monitoring

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INFORMATION SCIENCES 2021年 542卷 302-316页

作者： Li, Zhichao Tian, Li Jiang, Qingchao Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex nonlinearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： stacked autoencoder Process monitoring Distributed ensemble Joint feature

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