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Anomaly Detection for HTS Cable Using stacked autoencoder and Reflectometry

IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2023年第5期33卷 1页

作者： Bang, Su Sik Kwon, Gu-Young Tech Univ Korea Dept Elect Engn Shihung Gyeonggi Do South Korea Dongguk Univ Dept Smart Safety Engn Gyeongju South Korea

Since a high-temperature superconducting (HTS) cable has a cooling process and the possibility of a quench, a real-time monitoring technique is essential than other conventional power cable. Conventional reflectometry-based monitoring parameters have inherent disadvantages in that the boundary between normal and abnormal cannot be determined and multiple baselines cannot be considered during the monitoring process. In this paper, a stacked autoencoder-based anomaly detection technique for HTS cable is newly proposed. The autoencoder monitors the condition of HTS cable in real-time by calculating an anomaly score from the input data. The proposed method can automatically set the boundary between normal and abnormal and has the advantage of using multiple baselines. The performance of the proposed method is verified by emulating a local quench in a testbed containing a commercial 22.9 kV HTS cable and compared with that of the conventional monitoring parameter. Based on the advantage of being able to set multiple baselines, it is expected that the proposed technique can be used for real-time monitoring of HTS cable to enhance the reliability of the systems.

关键词： Superconducting cables High-temperature superconductors Monitoring Power cables Training Reflectometry Anomaly detection high-temperature superconducting (HTS) cable quench reflectometry stacked autoencoder

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A hybrid model of stacked autoencoder and modified particle swarm optimization for multivariate chaotic time series forecasting

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APPLIED SOFT COMPUTING 2022年第0期116卷 108321-108321页

作者： Xu, Xinghan Ren, Weijie Kyoto Univ Dept Environm Engn Kyoto 6158540 Japan Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Heilongjiang Peoples R China

Phase space reconstruction (PSR) is an effective method for chaotic system modeling, which can reveal the implicit evolution information in a complex system. However, the reconstructed time series tend to have a high dimension and contain some redundant information. It is difficult for a traditional simple model to directly forecast the reconstructed time series. In this paper, we propose a hybrid model of stacked autoencoder (SAE) and modified particle swarm optimization (MPSO) for multivariate chaotic time series forecasting. We utilize SAE to extract the reconstructed time series and adopt feedforward neural network (FNN) to forecast time series. In the proposed hybrid model, the SAE is followed by FNN, and we make the MPSO to train the output weights of the model, which is a large-scale optimization problem. To enhance the generalization ability and prevent over-fitting, we add a regularization item to the objective function when MPSO trains the weights of the model. Experimental results show that MPSO algorithm has advantages in the exploration and exploitation in large-scale optimization problems. Then, experiments on Lorenz time series and two real-world time series datasets verify the effectiveness of the hybrid model in multivariate chaotic time series forecasting. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multivariate chaotic time series Particle swarm optimization stacked autoencoder

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Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with stacked autoencoder

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REMOTE SENSING 2021年第9期13卷 1732-1732页

作者： Madani, Hadis McIsaac, Kenneth Western Univ Elect & Comp Engn Dept London ON N6A 3K7 Canada

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

关键词： hyperspectral image classification spectral-spatial feature vector distance transform stacked autoencoder

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Process monitoring of nonlinear uncertain systems based on Part Interval stacked autoencoder and Support Vector Data Description

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Wu, Qiqi Lu, Weipeng Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China POB 293 MeiLong Rd 130 Shanghai 200237 Peoples R China

Chemical process measurement may cause inaccuracy and uncertainty in some parts of the measure-ment data due to equipment aging and environmental conditions, such as temperature, humidity, gas pressure, gas flow, and other related factors. Uncertain data of this kind commonly fluctuate in an interval centered on the true value. Given the uncertain characteristics of some parts of the measurement data, the certain of some parts, and the nonlinearity of the industrial process, the present paper proposes a novel algorithm of process monitoring and fault diagnosis called Part Interval stacked autoencoder and Support Vector Data Description (PISAE-SVDD). The loss function of stacked autoencoder (SAE) is improved in this algorithm. In certain measurement data, the reconstruction error value is the mean square error of the original input data and output data. By contrast, uncertain measurement data provide a certain allowable range for reconstruction error value. The error value of measurement data is considered as zero within this range. The reconstruction error value of measurement data beyond the allowable range is calculated in the same way as for certain measurement data. At the same time, the characteristic information of the industrial process is extracted through the strong nonlinear characterization ability of SAE. Support Vector Data Description is then used to obtain the control limit of the fluctuation range of the normal working condition based on the SAE feature information data extracted from normal samples. This approach is adopted to realize the process monitoring of the industrial process with partial uncertain measurement. To detect fault, this algorithm was applied to the numerical simulation, the Tennessee Eastman process, and the process in industrial wastewater treatment plants, and then compared with other advanced algorithms. The results indicate the excellence and high efficiency of the PISAE-SVDD algorithm in the field of process monitoring. (C)

关键词： stacked autoencoder Process monitoring Tennessee Eastman process Industrial wastewater treatment plant Support Vector Data Description

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Application of stacked autoencoder for identification of bone fracture

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JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS 2023年第1期146卷 106077页

作者： Kim, Dong-Yoon Park, Eunbin Ku, Kyobeom Hwang, Se Jin Hwang, Kyu Tae Lee, Chang-Hun Yoon, Gil Ho Hanyang Univ Sch Mech Engn Seoul South Korea Hanyang Univ Coll Med Dept Anat & Cell Biol Seoul South Korea Hanyang Univ Coll Med Dept Orthopaed Surg Seoul South Korea

This study presents a stacked autoencoder (SAE)-based assessment method which is one of the unsupervised learning schemes for the investigation of bone fracture. Relatively accurate health monitoring of bone fracture requires considering physical interactions among tissue, muscle, wave propagation and boundary conditions inside the human body. Furthermore, the investigation of fracture, crack and healing process without state-ofthe-art medical devices such as CT, X-ray and MRI systems is challenging. To address these issues, this study presents the SAE method that incorporates bilateral symmetry of the human legs and low-frequency transverse vibration. To verify the presented method, several examples are employed with plastic pipes, cadaver legs and human legs. Virtual spectrograms, created by applying a short-time Fourier transform to the differences in vibration responses, are employed for image-based training in SAE. The virtual spectrograms are then classified and the fine-tuning is also carried out to increase the accuracy. Moreover, a confusion matrix is employed to evaluate classification accuracy and training validity.

关键词： Bone fracture Transverse vibration Frequency response function Bilateral symmetry 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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Outlier Detection for Monitoring Data Using stacked autoencoder

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IEEE ACCESS 2019年 7卷 173827-173837页

作者： Wan, Fangyi Guo, Gaodeng Zhang, Chunlin Guo, Qing Liu, Jie Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada

Monitoring data contain the important status information of the monitored object, and are the basis for following data mining and analysis. However, the monitoring data usually suffer the pollution of the outliers, leading to negative effect on the subsequent data processing. To address the problem, this paper proposed an outlier detection method based on stacked autoencoder (SAE). SAE has a powerful capability of feature extraction and greatly preserves the original information of the data. The trained SAE by normal data can learn the characteristics of normal data. When a set of data with outliers are inputted to the trained network, there are larger reconstruction errors at the outliers between the original input data and the reconstructed data obtained by using the encoding parameters and the decoding parameter mapping, which provides a basis for locating outliers. Meanwhile, this paper introduced the Grubbs criterion and the PauTa criterion to identify the reconstruction errors corresponding to the outliers based on the traditional threshold method. The method can quickly isolate the abnormal data from the normal data according to the reconstruction error and the identification criterion. The effectiveness and superiority of the proposed method have been validated by experiment on real data and comparisons with traditional outlier detection algorithms.

关键词： Condition monitoring outlier detection stacked autoencoder monitoring data

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Nonlocal, local and global preserving stacked autoencoder based fault detection method for nonlinear process monitoring

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期235卷

作者： Yang, Jingchao Wang, Li Shanghai Inst Technol Sch Elect & Elect Engn Shanghai 200230 Peoples R China

In recent years, the application of deep learning methods has improved the ability of nonlinear feature extraction in industrial processes. However, most of the deep learning methods cannot consider the manifold structure-related information (i.e., nonlocal, local and global manifold structure) of the data. To extract the vital structure-related features, a novel local, nonlocal and global preserving stacked autoencoder (NLGPSAE) for nonlinear process monitoring is proposed. NLGPSAE has an ability to extract crucial structure-related features by introducing a new regularized objective function with local, nonlocal and global structural information. For the preservation of local structure, NLGPSAE can preserve the original neighbor data points to be neighbor data points in the reconstructed space;for the preservation of nonlocal structures, NLGPSAE projects the nonlocal data points to be far apart in the reconstructed space;for the preservation of the global structure, the distance relationship between the original data points and the center of the data points is preserved in the reconstructed space. Two statistics Hotelling's T-squared (T2) and squared prediction error (SPE) based on features extracted by NLGPSAE are established for fault detection. The effectiveness of the proposed algorithm is verified in a complex numerical process and Tennessee Eastman process.

关键词： Nonlinear feature extraction Fault detection stacked autoencoder Structure preserving projection

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