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Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第7期14卷 1242-1250页

作者： Wang, Ying Lu, Hong Xiao, Xianyong Yang, Xiaomei Zhang, Wenhai Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China

Cable incipient fault is an intermittent arc fault, and may evolve into a permanent fault. Due to the short duration of the fault, the conventional overcurrent protection device cannot detect it. A cable incipient fault identification method is proposed in this study, using restricted Boltzmann machine (RBM) and stacked autoencoder (SAE). Firstly, disturbance current waveforms data is effectively compressed by RBM, which can improve analysis efficiency and obtain the shallow features of the data. Then, the compressed data is used as the input of SAE, and the optimal network parameters are obtained through layer-by-layer pre-training and fine-tuning. Finally, a well-trained SAE network is used to learn deep features from the input data to identify cable incipient fault, and softmax outputs identification result. In addition, the performance of the proposed method is compared with other methods. The accuracy of the proposed method is 98.33/95.62% for simulated data/measured data, and is 1.66/1.09%, 3.33/1.76%, 17.31/28.48% and 40.17/46.1% higher than the accuracies of convolutional neural network, deep belief network, random forest and back propagation neural network, respectively. The results show that the proposed method has high identification accuracy and feasibility.

关键词： Boltzmann machines backpropagation neural nets overcurrent protection fault diagnosis feature extraction belief networks arcs (electric) learning (artificial intelligence) power distribution faults restricted Boltzmann machine stacked autoencoder intermittent arc fault permanent fault conventional overcurrent protection device cable incipient fault identification method RBM disturbance current waveforms data compressed data layer-by-layer pre-training SAE network identification result high identification accuracy

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Prediction of potential miRNA-disease associations based on stacked autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbac021-bbac021页

作者： Wang, Chun-Chun Li, Tian-Hao Huang, Li Chen, Xing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China China Univ Min & Technol Xuzhou Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Inst Bioinformat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Big Data Res Ctr Xuzhou 221116 Jiangsu Peoples R China

In recent years, increasing biological experiments and scientific studies have demonstrated that microRNA (miRNA) plays an important role in the development of human complex diseases. Therefore, discovering miRNA-disease associations can contribute to accurate diagnosis and effective treatment of diseases. Identifying miRNA-disease associations through computational methods based on biological data has been proven to be low-cost and high-efficiency. In this study, we proposed a computational model named stacked autoencoder for potential MiRNA-Disease Association prediction (SAEMDA). In SAEMDA, all the miRNA-disease samples were used to pretrain a stacked autoencoder (SAE) in an unsupervised manner. Then, the positive samples and the same number of selected negative samples were utilized to fine-tune SAE in a supervised manner after adding an output layer with softmax classifier to the SAE. SAEMDA can make full use of the feature information of all unlabeled miRNA-disease pairs. Therefore, SAEMDA is suitable for our dataset containing small labeled samples and large unlabeled samples. As a result, SAEMDA achieved AUCs of 0.9210 and 0.8343 in global and local leave-one-out cross validation. Besides, SAEMDA obtained an average AUC and standard deviation of 0.9102 +/- /-0.0029 in 100 times of 5-fold cross validation. These results were better than those of previous models. Moreover, we carried out three case studies to further demonstrate the predictive accuracy of SAEMDA. As a result, 82% (breast neoplasms), 100% (lung neoplasms) and 90% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by databases. Thus, SAEMDA could be a useful and reliable model to predict potential miRNA-disease associations.

关键词： microRNA disease association prediction stacked autoencoder pretraining fine-tuning

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stacked autoencoder Based Weak Supervision for Social Image Understanding

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IEEE ACCESS 2019年 7卷 21777-21786页

作者： Xu, Chaoyang Dai, Yuanfei Lin, Renjie Wang, Sniping Putian Univ Sch Informat Engn Putian 351100 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China

Many studies in recent years have focused on social image understanding due to the increasing number of shared images from social networks and online communities. However, previous work in social image understanding fails to learn an effective feature representation because of a large amount of missing and irrelevant tags, though matrix completion techniques are frequently utilized for this purpose. autoencoder models have been validated to be effective in learning latent low-dimensional representations in unsupervised learning. In this paper, we propose a new social image understanding model based on deep autoencoders, which can learn the shared latent codes of social images and tags as supervision information in the deep autoencoders. First, social images are extracted with multi-modal features, which provide a comprehensive characterization to image semantic understanding. And, the social image understanding problems are transformed into the problem of minimizing an optimization objective. Second, multi-layered autoencoders with weak supervision integration are employed to learn an efficient low-dimensional representation from the multi-view feature sources that can make up the semantic gap between image features and tags through minimizing the problem formulation. Finally, we design a new balanced loss function based on binary cross entropy, in which we address highly sparse inputs for a better optimization performance. The extensive experiments on several real-world social image datasets confirm the effectiveness and robustness of the proposed model compared with the state-of-the-art methods.

关键词： Computer vision social image understanding stacked autoencoder image tag refinement tag-based retrieval

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stacked autoencoder for operation prediction of coke dry quenching process

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CONTROL ENGINEERING PRACTICE 2019年 88卷 110-118页

作者： Wang, Jian-Guo Wang, Yu Yao, Yuan Yang, Bang-Hua Ma, Shi-Wei Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

Coke dry quenching (CDQ) is widely adopted for waste heat recovery in iron and steel plants. In this work, an economic benefit index was introduced to evaluate the performance of the CDQ system and stacked autoencoder (SAE) based deep neural networks are adopted for CDQ operation prediction. Based on the prediction results, a guidance is provided for online adjustment of the supplementary air flow rate, hence the efficiency and safety of the CDQ system can be improved. The case study on a real plant shows that the proposed method increases the economic efficiency of the CDQ process by 4.39%.

关键词： Coke dry quenching stacked autoencoder Deep learning Economic benefit index Process modeling

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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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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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A Semi-supervised stacked autoencoder Approach for Network Traffic Classification 28

A Semi-supervised Stacked Autoencoder Approach for Network T...

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28th IEEE International Conference on Network Protocols (IEEE ICNP)

作者： Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti Univ Nantes LS2N 2 Chemin Houssiniere Nantes France IIT Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781728169927

Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by denoising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.

关键词： Traffic classification Feature extraction Deep learning Machine learning stacked autoencoder stacked Denoising autoencoder Dropout Semi-supervised learning

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Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation

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GEOCARTO INTERNATIONAL 2022年第7期37卷 2031-2054页

作者： Ahmadi, Seyyed Ali Mehrshad, Nasser Arghavan, Seyyed Mohammadali Univ Birjand Fac Elect & Comp Engn Birjand Iran Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods.

关键词： Classification hyperspectral image limited labelled samples deep learning stacked autoencoder Gabor feature extended multi-attribute profile

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Probabilistic Prediction of Solar Generation Based on stacked autoencoder and Lower Upper Bound Estimation Method

Probabilistic Prediction of Solar Generation Based on Stacke...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pan, Cheng Tan, Jie Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781728169262

The lower upper bound estimation method is an important probabilistic prediction method and has been applied to the solar generation forecasting. However, when the input dimension of the lower upper bound estimation method is large, its performance will be seriously affected. To overcome this challenge, a novel probabilistic prediction of solar generation based on stacked autoencoder and lower upper bound estimation method is proposed. In this method, stacked autoencoder is first used to obtain highly compressed features, which are utilized as the input of the lower upper bound estimation method. Besides, to make the target value in the center of the prediction interval as much as possible, inspired by the idea of support vector machine, the mean squared error of prediction interval is introduced to the loss function, which keeps the target value as far as possible from the lower and upper bounds of the prediction interval. To verify the performance of the proposed method, a large number of experiments have been carried out on the freely available dataset. The results show that the proposed method has better forecasting performance.

关键词： stacked autoencoder lower upper bound estimation solar generation forecasting probabilistic prediction

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Anomaly detection of organic pollution in drinking water based on fluorescence spectroscopy and stacked autoencoder

Anomaly detection of organic pollution in drinking water bas...

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Chinese Automation Congress (CAC)

作者： Shi, Jiegen Cao, Yitong Yu, Jie Hou, Dibo Ren, Yongqin Zhang, Guangxin Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Natl Ocean Technol Ctr Tianjin Peoples R China

ISBN: (纸本)9781728176871

In this paper, a three-dimensional fluorescence anomaly detection method for organic pollutants of drinking water is studied, and a normal water sample reconstruction model based on stacked autoencoder is proposed. The organic contamination events are detected by the residual of the reconstructed output and the original input of samples with the 3 sigma criterion, and the algorithm is validated on the common organic pollutants such as phenol, salicylic acid and rhodamine B in drinking water. The results show that the algorithm can effectively detect both high-concentration and low-concentration organic pollutants under the premise of ensuring low false alarm rate when affected by fluctuations in water quality background. The method proposed is proved to have the potential to improve the sensitivity and robustness of anomaly detection of organic pollution in drinking water.

关键词： 3D fluorescence spectroscopy organic pollutant anomaly detection stacked autoencoder

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