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2020 Chinese Intelligent Systems Conference

作者： Yin, Hongpeng Guo, Jiaxin Liao, Guobo Chai, Yi Chongqing Univ Chongqing 400044 Peoples R China

ISBN: (纸本)9789811584527;9789811584503;9789811584497

Both linear and nonlinear relationships are two typical characteristics among industrial process variables, and diagnosing a process with such complicated correlations among variables is indispensable. However, individual dictionary learning or autoencoder based method is hard to extract these complicated correlations well. The parallel label consistent KSVD-stacked autoencoder (P-LCKSVD-SAE) model is proposed to integrate the linear and nonlinear feature extraction for effective industrial process fault diagnosis. First, LCKSVD and SAE methods are applied to extract the linear and nonlinear features from industrial process. Second, a matrix fusion algorithm is proposed for combining these intrinsic fault features. Then, the SVM classifier is utilized to verify the effectiveness of the proposed model for fault classification. Finally, several case studies on Tennessee Eastman process demonstrate that the proposed P-LCKSVD-SAE fault diagnosis scheme is better than the conventional LC-KSVD as well as the SAE methods at performing industrial process fault diagnosis.

关键词： Nonlinear industrial process Fault diagnosis Label consistent KSVD stacked autoencoder

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Head pose healthiness prediction using a novel image quality based stacked autoencoder

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DIGITAL SIGNAL PROCESSING 2022年 130卷

作者： Nejkovic, Valentina Ozturk, Muhammed Maruf Petrovic, Nenad Univ Nis Fac Elect Engn Dept Comp Sci Nish Serbia Suleyman Demirel Univ Fac Engn Dept Comp Engn Isparta Turkey

This paper introduces an approach aiming to determine head pose healthiness of computer users. The main contributions of this paper are: 1) Image Quality Assessment (IQA) based stacked autoencoder (referred to as IQASAE) which adjusts the value of learning rate based on the quality of images;2) Head Pose Healthiness Prediction (HPHP) framework which leverages the proposed IQASAE algorithm in combination with image processing operations;3) A set of features suitable for face analysis applications;4) Ontology-driven semantic framework which enables further exploiting pose estimation results within applications in synergy with healthcare expert domain knowledge about pose healthiness. Our framework was evaluated on both offline (BIWI and AFLW) and online (our own, collected using Arduino) datasets. Furthermore, it was compared to several state-of-art methods, including Multi-Layer Perceptron (MLP), CART, Random Forest, Convolutional Neural Networks (CNN), Temporal Deep Learning Model (TDLM), hybrid CNN with Support Vector Machine (SVM), Quatnet and Trinet. According to the achieved experimental results, it reaches accuracy up to 79.63% outperforming all of them, except Quatnet and Trinet. However, the main advantages of IQASAE compared to state-of-art methods are: 1) it does not require selection of features, so the processing time is reduced, 2) utilizing angle between chin and mouth reduces training time for SAE, 3) leveraging vector-based feature set to create training data resulted in a significant improvement, especially in offline facial images. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Head pose healthiness prediction stacked autoencoder Image quality Pose estimation Ontology

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A stacked autoencoder based gene selection and cancer classification framework

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期78卷 000-000页

作者： Gokhale, Madhuri Mohanty, Sraban Kumar Ojha, Aparajita Jabalpur Engn Coll Dept Comp Sci & Engn Jabalpur 482001 India PDPM Indian Inst Informat Technol Design & Mfg Comp Sci & Engn Jabalpur 482005 India

Cancer is one of the most common causes of death worldwide and is, therefore, a prominent area of biomedical research. Cancer is a genetic disease in which improperly functioning genes tend to change expressions. Thus, gene expression analysis is utilized for early diagnosis of cancer prognosis, and therapy prediction in a clinical environment. Usually, some dominant genes among thousands of them play an important role in the diagnosis of cancer. But designing a suitable framework to find out the key set of genes is a challenging task. Numerous gene selection approaches have been introduced by researchers for cancer classification, using statistical, or traditional feature selection methods. In recent years, deep learning methods have also been applied for gene selection using autoencoder networks. However, improving the accuracy of cancer classification still remains a challenging task. In the present paper, a stacked autoencoder-based framework is proposed for gene selection and cancer classification. Nine different classifiers are employed to evaluate the performance of the gene selection model. Then the best performing combination of gene selection and cancer classification models are chosen to finally select the genes. Random Forest and Support Vector Machine show better performance on ten different benchmark datasets, when the gene selection is done using the stacked autoencoder. The classifier with the highest accuracy is selected to build the cancer classification model. The proposed model outperforms seven existing methods on all the ten datasets.

关键词： Gene selection Cancer classification stacked autoencoder

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A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism

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APPLIED ENERGY 2022年 327卷

作者： Fazlipour, Zahra Mashhour, Elaheh Joorabian, Mahmood Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Golestan AvePOB *** Ahvaz Iran

This paper presents an innovative univariate Deep LSTM-based stacked autoencoder (DLSTM-SAE) model for short-term load forecasting, equipped with a Multi-Stage Attention Mechanism (MSAM), including an input AM and several temporal AM in the pre-training phase. The input AM is used to capture the high-impact load sequence time steps of univariate input data. It should be noted that the model's performance is improved by increasing the network depth;however, finding the optimal network parameters is a challenging task due to the random assignment of the initial weights of the network. An unsupervised greedy layer-wise pre-training structure equipped with the MSAM is expanded to solve setting the random initial weight problem of the DLSTM-SAE model. The multi-stage temporal AM in the pre-training structure leads the DLSTM-SAE to properly learn the time dependencies related to remarkably long sequence input data and capture the temporal merit features lied in the LSTM memory. The performance of the proposed model is evaluated through various comparative tests with current prevalent models using actual energy market data New England ISO using three criteria indexes. The results show the superiority of the proposed model and its robustness in offline and online load forecasting.

关键词： Deep learning Attention mechanism Short-term load forecasting LSTM stacked autoencoder

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Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression

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JOURNAL OF ENERGY STORAGE 2022年 47卷

作者： Wei, Meng Ye, Min Wang, Qiao Xinxin-Xu Twajamahoro, Jean Pierre Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710064 Shaanxi Peoples R China Univ Rwanda Kigali 3900 Rwanda Coll Sci & Technol Kigali 3900 Rwanda

Lithium-ion batteries have been widely applied in energy storage systems and electric vehicles (EVs), the remaining useful life (RUL) prediction is one of the critical technologies for prognostics and health management. However, high accuracy RUL prediction with reliability is the biggest bottleneck. To improve RUL prediction and adaptively extract indirect health indicators (HIs), the RUL prediction framework based on the stacked autoencoder and Gaussian mixture regression (SAE-GMR) is proposed. Firstly, the indirect HIs are extracted from charging and discharging data, and the gray relation analysis (GRA) is adopted to analyze the relation with capacity. In this paper, the SAE neural network is proposed to reduce the dimensions and noise of battery and obtain a syncretic HI. Then, the GMR model is estiblished not only to improve accuracy of RUL prediction, but also describe the reliability. Finally, the proposed method is compared with esixting methods,which shows that the proposed model has superiority for other methods.

关键词： Energy storage systems Lithium-ion batteries Remaining useful life stacked autoencoder Gaussian mixture regression

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stacked autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 470-476页

作者： Kamalakannan, K. Anandharaj, G. Adhiparasakthi Coll Arts & Sci PG & Res Dept Comp Sci Kalavai TN India

Parkinson's disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson's are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson's disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the stacked autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System - Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithm

关键词： Immune algorithms Parkinson's disease stacked autoencoder airs-parallel machine learning

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Deep Co-Saliency Detection via stacked autoencoder-Enabled Fusion and Self-Trained CNNs

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第4期22卷 1016-1031页

作者： Tsai, Chung-Chi Hsu, Kuang-Jui Lin, Yen-Yu Qian, Xiaoning Chuang, Yung-Yu Texas A&M Univ Dept Elect & Comp Engn Uvalde TX 77843 USA Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan Natl Chiaorung Univ Dept Comp Sci Hsinchu 300 Taiwan

Image co-saliency detection via fusion-based or learning-based methods faces cross-cutting issues. Fusion-based methods often combine saliency proposals using a majority voting rule. Their performance hence highly depends on the quality and coherence of individual proposals. Learning-based methods typically require ground-truth annotations for training, which are not available for co-saliency detection. In this work, we present a two-stage approach to address these issues jointly. At the first stage, an unsupervised deep learning model with stacked autoencoder (SAE) is proposed to evaluate the quality of saliency proposals. It employs latent representations for image foregrounds, and auto-encodes foreground consistency and foreground-background distinctiveness in a discriminative way. The resultant model, SAE-enabled fusion (SAEF), can combine multiple saliency proposals to yield a more reliable saliency map. At the second stage, motivated by the fact that fusion often leads to over-smoothed saliency maps, we develop self-trained convolutional neural networks (STCNN) to alleviate this negative effect. STCNN takes the saliency maps produced by SAEF as inputs. It propagates information from regions of high confidence to those of low confidence. During propagation, feature representations are distilled, resulting in sharper and better co-saliency maps. Our approach is comprehensively evaluated on three benchmarks, including MSRC, iCoseg, and Cosal2015, and performs favorably against the state-of-the-arts. In addition, we demonstrate that our method can be applied to object co-segmentation and object co-localization, achieving the state-of-the-art performance in both applications.

关键词： Proposals Saliency detection Image segmentation Image reconstruction Reliability Task analysis Fuses Co-saliency detection stacked autoencoder reconstruction residual adaptive fusion optimization self-paced learning CNNs

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Residential Customer Baseline Load Estimation Using stacked autoencoder With Pseudo-Load Selection

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第1期38卷 61-70页

作者： Wang, Xinan Wang, Yishen Wang, Jianhui Shi, Di GEIRI North Amer San Jose CA 95134 USA Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75205 USA

Accurate estimation of customer baseline load (CBL) is a key factor in the successful implementation of demand response (DR). CBL technologies implemented at utilities currently are primarily designed for large industrial and commercial customers. The U.S. Federal Energy Regulatory Commission (FERC) order 745 states that DR owners, including residential customers, can sell their load reduction in the wholesale market. However, since residential load is random and un-schedulable, this tends to inherently degrade the effectiveness of existing CBL technologies. In this paper, a novel SAE based CBL method for residential customers that uses the data reconstruction capability of a stacked autoencoder (SAE) is described. In the model, two SAEs are synchronously trained-one SAE generates a pseudo-load pool and the second one is used to select a pseudo-load to reconstruct a residential CBL. A support vector machine (SVM) classifier is self-trained to conduct the pseudo-load selection. The proposed strategy is validated using a real data set consisting of 328 residential customers' smart meter readings. Benchmarks from other machine learning techniques and existing CBL methods are compared with the proposed method. Test results show that the accuracy of the residential CBL reconstruction significantly improves when compared with existing methods, such as HighXofY and exponential moving average.

关键词： Load modeling Estimation Training Support vector machines Image reconstruction Noise measurement Decoding Machine learning residential customer baseline load stacked autoencoder demand response unsupervised

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Diagnosis of breast cancer with stacked autoencoder and Subspace kNN

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 551卷 124591-124591页

作者： Adem, Kemal Aksaray Univ Dept Management Informat Syst TR-68100 Aksaray Turkey

Breast cancer is one of the most common and deadliest cancer types in women worldwide. Research on this disease has become very important because early diagnosis stages, clinical applications and the speed of response to treatment are facilitated in diseases such as cancer. In this study, an approach is proposed in which a Subspace kNN algorithm is used together with stacked autoencoder (SAE) for diagnosis of disease on the breast cancer microarray dataset for the first time. Such hybrid approaches can provide better results when classifying data sets with high-dimensional and uncertainty. The data set used in the study was taken from Kent Ridge-2 database. It consists of 97 samples (51 benign, 46 malicious) and 24482 attributes. The performance of the proposed method was evaluated and the results were compared with other well-known methods of dimension reduction and machine learning. As a result of the comparison, the data set was reduced to 100 attributes by using SAE and Subspace kNN and 91.24% accuracy was achieved. The result obtained provides important classification accuracy, especially in high-dimensional data sets. The importance of this study is that the models that were created by using various classifiers to increase the success rate of the stacked autoencoder-softmax classifier model in the breast cancer microarray data set were applied for the first time. In this regard, it is considered that automation-based studies will provide diagnostic decision support system a solution using the proposed method in future works. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Breast cancer stacked autoencoder Subspace kNN

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Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive stacked autoencoder

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IEEE ACCESS 2020年 8卷 190431-190447页

作者： Zhang, Guoling Wang, Xiaodan Li, Rui Song, Yafei He, Jiaxing Lai, Jie Air Force Engn Univ Coll Air & Missile Def Xian 710051 Peoples R China

In the field of intrusion detection, there is often a problem of data imbalance, and more and more unknown types of attacks make detection difficult. To resolve above issues, this article proposes a network intrusion detection model called CWGAN-CSSAE, which combines improved conditional Wasserstein Generative Adversarial Network (CWGAN) and cost-sensitive stacked autoencoders (CSSAE). First of all, the CWGAN network that introduces gradient penalty and L2 regularization is used to generate specified minority attack samples to reduce the class imbalance of the training dataset. Secondly, the stacked autoencoder is used to intelligently extract the deep abstract features of the network data. Finally, a cost-sensitive loss function is constructed to give a large misclassification cost to a minority of attack samples. Thus, effective detection of network intrusion attacks can be realized. The experimental results based on KDDTest(+), KDDTest-21, and UNSW-NB15 datasets show that the CWGAN-CSSAE network intrusion detection model improves the detection accuracy of minority attacks and unknown attacks. In addition, the method in this article is compared with other existing intrusion detection methods, excellent results have been achieved in performance indicators such as accuracy and F1 score. The accuracy on the above datasets reached 90.34%, 80.78% and 93.27% respectively. The accuracy of U2R on the KDDTest(+) and KDDTest21 datasets both reached 42.50%. The accuracy of R2L on the KDDTest(+) and KDDTest-21 datasets reached 54.39% and 52.51%, respectively. And the F1 score on the above datasets reached 91.01%, 87.18% and 93.99% respectively.

关键词： Intrusion detection conditional Wasserstein GAN stacked autoencoder imbalanced classification cost-sensitive regularization deep learning

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