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Fault classification based on variable-weighted dynamic sparse stacked autoencoder for industrial processes

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第1期101卷 420-430页

作者： Dong, Jie Tu, Yanan Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing Peoples R China Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling Technol Beijing Peoples R China

Effective process monitoring and fault diagnosis are of great significance to the safe operation of industrial processes as production scale increases and production systems become more and more complex. Identifying fault types in complex industries by fault classification can help workers to determine the source of the fault as soon as possible, which is crucial to timely recover work. In this paper, a fault classification method based on a variable-weighted dynamic sparse stacked autoencoder is proposed. First, considering the dynamic characteristics of process data, the input data are processed dynamically by a sliding window. Then, in the pre-training stage, the weight of each variable is calculated by Fisher discriminant analysis for the reconstruction of the loss function. The sparse term is added to the loss function so that it can learn the effective representation of data in a harsh environment. Finally, the proposed method is applied to the Tennessee-Eastman benchmark to evaluate the classification performance. The result shows the superiority of the proposed method.

关键词： deep learning fault classification Fisher discriminant analysis stacked autoencoder

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Multi-label classification using a cascade of stacked autoencoder and extreme learning machines

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NEUROCOMPUTING 2019年 358卷 222-234页

作者： Law, Anwesha Ghosh, Ashish Indian Stat Inst Machine Intelligence Unit 203 BT Rd Kolkata 700108 India

This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhance the performance of a stand-alone multi-label extreme learning machine (MLELM) by collaborating it with other networks. There are three basic phases in the proposed method: feature encoding, soft classification and class score approximation. In the first step, an SAE network is employed to generate a discriminating and reduced input representation of the multi-label data. This makes the data compact and more manageable for the successive stages. This data in turn is used by an MLELM in the next phase for the prediction of soft labels. In the final step, to improve the prediction capability of the previous network, a novel approach of approximating the class score is proposed using an additional MLELM. Comprehensive experimental evaluation of the proposed approach has been performed on seven datasets against eleven relevant algorithms, and overall it displays a promising performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-label classification Extreme learning machine stacked autoencoder Multi-label extreme learning machine

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Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

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Petroleum Science 2021年第5期18卷 1465-1482页

作者： Jaejun Kim Changhyup Park Seongin Ahn Byeongcheol Kang Hyungsik Jung Ilsik Jang Department of Energy Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Energy and Resources Engineering Kangwon National UniversityChuncheonKangwon24341Republic of Korea Geo-ICT Convergence Research Team Korea Institute of Geoscience and Mineral ResourcesDaejeon34132Republic of Korea Department of Energy and Resources Engineering Chosun UniversityGwangju61452Republic of Korea

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history *** proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering *** functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs *** iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration *** proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population *** learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata *** confirms the proposed workflow constructs more plausible *** workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

关键词： Deep neural network stacked autoencoder History matching Iterative learning Clustering Many-objective

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Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-15页

作者： Abadlia, Houda Smairi, Nadia Univ Manouba ENSI LARIA UR22ES01 Campus Univ Manouba Manouba 2010 Tunisia

In recent years, several fraud attempts have been made in various sectors including finance, banking and insurance. In fact, credit card fraud refers to the unauthorized use of a credit card account to obtain money, products or services. It involves the manipulation of card details for fraudulent purchases or withdrawals. These fraudulent incidents result in substantial financial losses of different divisions of businesses. The present manuscript presents an innovative system used to detect credit card fraud employing unsupervised deep learning. However, the effectiveness of deep learning models relies on the configuration of hyperparameter values and the avoiding of overffiting issue, tasks that provide time-consuming and require significant trial and error. The proposed model utilizes an improved particle swarm optimization (PSO) to optimize the training hyperparameters such as global initial connection weights and thresholds, while leveraging stacked autoencoder for classification purposes. Unlike the existing model, that introduced in this work takes into account transactional data and enables the classifier to accurately identify the most crucial transactions within the input sequence, which allows predicting fraudulent transactions more accurately. This approach combines the strengths of three methods: SMOTE-Tomek for handling imbalanced data, local search PSO for hyperparameter optimization, and an enhanced stacked autoencoder used to detect anomaly in credit card fraud transactions. The comparative study reveals that the developed model is the most efficient, in comparison with the other models.

关键词： Fraud detection stacked autoencoder PSO Hyperparameter optimization Imbalanced Data

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A system evaluation of NBA rookie contract execution efficiency with stacked autoencoder and hybrid DEA

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OPERATIONAL RESEARCH 2021年第4期21卷 2771-2807页

作者： Zhu, Qing Zuo, Renxian Li, Yuze Liu, Shan Shaanxi Normal Univ Int Business Sch Xian 710119 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100864 Peoples R China

Most labor contract evaluations rely on performance evaluations by human resource management, which is time-consuming and costly. However, there has been little research into quantitative contract evaluations. This paper embedded a stacked autoencoder into a weighted two-stage data envelopment analysis model to evaluate NBA rookie seasonal contracts in an attempt to quantitatively assess contract execution efficiency. It was found that the model was able to effectively evaluate the NBA rookie contracts and provide guidance to the coach regarding their on-court performances. The NBA rookie contract execution analyses also found that performance and therefore contract fulfilment was possibly affected by time allocation problems. Finally, a dynamic and comprehensive contract evaluation system that has significant possible commercial value was constructed to assist the player, coach and manager make timely decisions, which may be a breakthrough in objective human resource management performance evaluation systems.

关键词： Contract execution efficiency NBA stacked autoencoder Two-stage DEA

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Hybrid Sea Lion Crow Search Algorithm-Based stacked autoencoder for Drug Sensitivity Prediction From Cancer Cell Lines

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-21页

作者： Mahajan, Rupali A. Shaikh, Nilofer Karim Tikhe, Atharva Balkrishna Vyas, Renu Chavan, Smita M. MIT ADT Univ Pune Maharashtra India Govt Coll Engn Pune Maharashtra India

Providing better therapy to cancer patients remains a major task due to drug resistance of tumor cells. This paper proposes a sea lion crow search algorithm (SLCSA) for drug sensitivity prediction. The drug sensitivity from cultured cell lines is predicted using stacked autoencoder, and the proposed SLCSA is derived from a combination of sea lion optimization (SLnO) and crow search algorithm (CSA). The implemented approach has offered superior results. The maximum value of testing accuracy for normal is 0.920, leukemia is 0.920, NSCLC is 0.912, and urogenital is 0.914.

关键词： Cancer Cell Lines Crow Search Algorithm (CSA) Drug Sensitivity Prediction Sea Lion Optimization (SLnO) stacked autoencoder

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Trust aware routing using sunflower sine cosine-based stacked autoencoder approach for EEG signal classification in WSN

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JOURNAL OF HIGH SPEED NETWORKS 2021年第2期27卷 101-119页

作者： Kumaraguru, Shanthi Jebarani, M. R. Ebenezar Sathyabama Inst Sci & Technol Chennai Tamil Nadu India

Trust-aware routing is the significant direction for designing the secure routing protocol in Wireless Sensor Network (WSN). However, the trust-aware routing mechanism is implemented to evaluate the trustworthiness of the neighboring nodes based on the set of trust factors. Various trust-aware routing protocols are developed to route the data with minimum delay, but detecting the route with good quality poses a challenging issue in the research community. Therefore, an effective method named Sunflower Sine Cosine (SFSC)-based stacked autoencoder is designed to perform Electroencephalogram (EEG) signal classification using trust-aware routing in WSN. Moreover, the proposed SFSC algorithm incorporates Sunflower Optimization (SFO) and Sine Cosine Algorithm (SCA) that reveals an optimal solution, which is the optimal route used to transmit the EEG signal. Initially, the trust factors are computed from the nodes simulated in the network environment, and thereby, the trust-based routing is performed to achieve EEG signal classification. The proposed SFSC-based stacked autoencoder attained better performance by selecting the optimal path based on the fitness parameters, like energy, trust, and distance. The performance of the proposed approach is analyzed using the metrics, such as sensitivity, accuracy, and specificity. The proposed approach acquires 94.708%, 94.431%, and 95.780% sensitivity, accuracy, and specificity, respectively, with 150 nodes.

关键词： Sine cosine algorithm sunflower optimization stacked autoencoder electroencephalogram signal trust aware routing

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Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第7期101卷 3998-4015页

作者： Yu, Feng Liu, Jianchang Liu, Dongming Wang, Honghai Shang, Liangliang Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang Peoples R China Nantong Univ Sch Elect Engn Nantong Peoples R China

Recently, stacked autoencoder (SAE)-based deep learning has been widely employed for industrial process fault detection, which can extract representative low-dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self-reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC-SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC-SAE can describe the distribution characteristics of process data, and more informative high-level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T-squared (T-2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC-SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed-batch fermentation penicillin (FBFP) process.

关键词： deep learning density peak clustering fault detection stacked autoencoder

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Speech emotion recognition in Persian based on stacked autoencoder by comparing local and global features

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第23期82卷 36413-36430页

作者： Bastanfard, Azam Abbasian, Alireza Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran Iran Broadcasting Univ Fac Media Engn Tehran Iran

Among the barriers to establishing effective human-machine interactions is the machines' inability to properly distinguish emotions from the human voice. The Speech Emotion Recognition (SER) systems have emerged to tackle this limitation. The accuracy of these systems depends on different factors such as the quantity and the types of emotions included in the database, feature extraction process including local and global features, feature selection method, and the type of classifier. This study presents a methodology for speech emotion recognition using an autoencoder neural network. It is shown that using a digit-level stacked autoencoder can be suitable for digit classification. The speech emotion recognition is done using the Persian emotional speech database (Persian ESD), which includes six emotional states: Happiness, Sadness, Fear, Disgust, Anger, and Neutral. Moreover, the popular, widely-used Berlin Emotional database (EMO-DB) is used to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed method has significantly improved recognition accuracy.

关键词： Speech emotion recognition stacked autoencoder Persian language Deep learning

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Traffic-flow prediction via granular computing and stacked autoencoder

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GRANULAR COMPUTING 2020年第4期5卷 449-459页

作者： Chen, Jianhua Yuan, Wenjing Cao, Jingjing Lv, Haili Wuhan Univ Technol Sch Logist Engn Wuhan Peoples R China Minist Educ Engn Res Ctr Port Logist Technol & Equipment Wuhan Peoples R China

Accurate traffic-flow prediction is essential to traffic management. Traffic data collected in very short intervals normally contain high variability, while common preprocessing approaches applied within a window such as simple average or median operator are unable to obtain sufficient latent information from original data. Moreover, the prediction performance of shallow neural network is not satisfying, since its capacity to represent the temporal-spatial correlation of mass traffic data is insufficient, and its adaptation capacity to noisy data is relatively poor. In this paper, fuzzy information granulation (FIG) and deep neural network are combined to solve these two issues. To be specific, FIG is utilized to process original data series and extract granules, which denote the trend and fluctuation range of each time window. Then, stacked autoencoder is combined to obtain the predictive results based on processed granules, especially, a multi-output mechanism is designed to predict all granulation parameters simultaneously, which makes better use of the correlation of diverse inputs. A real-world traffic volume data set is applied to conduct an empirical study, and the experimental results illustrate that based on the proposed method, the interval prediction of the traffic-flow fluctuation range is realized, and superior traffic trend prediction performance is achieved.

关键词： Traffic-flow prediction Deep learning stacked autoencoder Fuzzy information granulation Granular computing

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