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δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting

NEUROCOMPUTING 2017年 247卷 31-38页

作者： Zhou, Teng Han, Guoqiang Xu, Xuemiao Lin, Zhizhe Han, Chu Huang, Yuchang Qin, Jing South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Affiliated Shantou Hosp Shantou 515000 Guangdong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong 999077 Hong Kong Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Hong Kong Polytech Univ Sch Nursing Ctr Smart Hlth Hong Kong 999077 Hong Kong Peoples R China

Accurate and timely traffic flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Recently, the stacked autoencoder has been proven promising for traffic flow forecasting but still exists some drawbacks in certain conditions. In this paper, a training samples replication strategy is introduced to train a series of stacked autoencoders and an adaptive boosting scheme is proposed to ensemble the trained stacked autoencoders to improve the accuracy of traffic flow forecasting. Furthermore, sufficient experiments have been conducted to demonstrate the superior performance of the proposal. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Short-term traffic flow forecasting AdaBoost stacked autoencoder Time-series model Dynamic system

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Detection of Double Compressed AMR Audio Using stacked autoencoder

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2017年第2期12卷 432-444页

作者： Luo, Da Yang, Rui Li, Bin Huang, Jiwu Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

The adaptive multi-rate (AMR) audio codec adopted by many portable recording devices is widely used in speech compression. The use of AMR speech recordings as evidence in court is growing. Nowadays, it is easy to tamper with digital speech recordings, which makes audio forensics increasingly important. The detection of double compressed audio is one of the key issues in audio forensics. In this paper, we propose a framework for detecting double compressed AMR audio based on the stacked autoencoder (SAE) network and the universal background model-Gaussian mixture model (UBM-GMM). Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms. Audio frames are used as network input and the last hidden layer's output constitutes the features of a single frame. For an audio clip with many frames, the features of all the frames are aggregated and classified by UBM-GMM. Experimental results show that our method is effective in distinguishing single/double compressed AMR audio and outperforms the existing methods by achieving a detection accuracy of 98% on the TIMIT database. Exhaustive experiments demonstrate the effectiveness and robustness of the proposed method.

关键词： Adaptive multi-mate double compressed AMR audio forensics stacked autoencoder

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Mutual stacked autoencoder for unsupervised fault detection under complex multi-residual correlations

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Yu, Jianbo Lv, Zhaomin Ruan, Hang Hu, Shijie Jiang, Qingchao Yan, Xuefeng Liu, Yuping Yang, Xiaofeng Fudan Univ Sch Microelect Shanghai 200433 Peoples R China Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai 201620 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Due to the increasing complexity of variable relationships, fault detection has garnered significant attention, as it is crucial for ensuring industrial safety and engineering reliability. Traditional detection methods can be classified as twofold: global-based and local-based strategies, which respectively focus on mining macro- and micro-level information. However, our theoretical derivation and experiment results reveal that some spurious assumptions, such as local groups and their provided information are mutually independent are implicitly adhered to but are hardly satisfied in unsupervised fault detection under real industrial scenarios. Hence, this study introduces a novel mutual stacked autoencoder (M-SAE) which can be divided into three subnetworks: L-Net, R-Net, and M-Net. L-Net enriches local information learning through multiple local backbones by incorporating the unsupervised clustering algorithm. R-Net, employing a multi-scale attention mechanism, leverages complete local information for residual strength calculation and utilizes local features to capture residual information within the latent feature space. M-Net fuses the multi-scale local feature information to perform a reconstruction for each local. A multitask entropy-aided loss function is introduced to enrich local details, the global structure, and the residual associations. Finally, results on eleven datasets validate the high-performance of the proposed M-SAE and the ablation experiments demonstrate the efficacy of each component in M-SAE, confirming that this research effectively and accurately addresses multivariable industrial fault detection tasks, thereby enabling timely interventions that are crucial for maintaining operational safety in real-world scenarios.

关键词： Fault detection Global-based strategy Local-based strategy stacked autoencoder

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Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 61卷 576-591页

作者： Han, Tian Pang, Jiachen Tan, Andy C. C. Univ Sci & Technol Beijing Sch Mech Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Tunku Abdul Rahman LKC Fac Engn Sungai Long Campus Kajang 43000 Selangor Malaysia

Remaining Useful Life (RUL) prediction play a crucial part in bearing maintenance, which directly affects the production efficiency and safety of equipment. Moreover, the accuracy of the prediction model is constrained by the feature extraction process and full life data of bearings. In this paper, the life prediction method of faulty rolling bearing with limited data is presented including degradation state model and RUL prediction model. In order to obtain health indication (HI) without human interference in the degradation state modeling stage, the bottleneck structure of stacked autoencoder (SAE) is utilized to fuse the four selected features into one HI using Intelligent Maintenance Systems (IMS) bearing dataset as training sample. In RUL prediction model, the Long Short-Term Memory (LSTM) neural network is carried out to establish the model with Standard deviation (Std) input and HI training label. In order to solve the problem of large training error caused by insufficient data in the failure stage of bearing acceleration test, the third-order spline curve interpolation is utilized to enhance the data points. Through parameter analysis, the RMSE and MAE of the test set on the prediction model are 0.032582 and 0.024038, respectively. Furthermore, the effectiveness of the proposed method is further validated by dataset from Case Western Reserve University (CWRU) with different bearing fault degrees. The analysis indicates that the RUL prediction of bearing fault data is consistent with the size of artificial added faults, that is,the more severe the fault the shorter the time of remaining life. The results validate that the proposed method can effectively extract the bearing health state by incorporating feature fusion and establish accurately prediction model for bearing remaining life.

关键词： Remaining useful life prediction stacked autoencoder Recurrent neural network Bearing

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Link Quality Estimation Method for Wireless Sensor Networks Based on stacked autoencoder

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IEEE ACCESS 2019年 7卷 21572-21583页

作者： Luo, Xionghui Liu, Linlan Shu, Jian Al-Kali, Manar Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China Nanchang Hangkong Univ Sch Software Nanchang 330063 Jiangxi Peoples R China

In wireless sensor networks, effective link quality estimation is the basis of topology management and routing control. Effective link quality estimation can guarantee the transmission of data, as well as improve the throughput rate, and hence, extend the life of the entire network. For this reason, a stacked autoencoder-based link quality estimator (LQE-SAE) is proposed. Specifically, the zero-filling method is developed to process the original missing link information. Then, the SAE model is used to extract the asymmetric characteristics of the uplink and downlink from the received signal strength indicator, link quality indicator, and signal-to-noise ratio, respectively. These characteristics are fused by SAE to construct the link features' vectors, which are given as inputs to the support vector classifier (SVC), for which the link quality grade is taken as its label. The experimental results in different scenarios show that the LQE-SAE has better accuracy than link quality estimators based on the SVC, ELM, and WNN.

关键词： Wireless sensor networks link quality estimation stacked autoencoder deep learning asymmetry of link

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Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder

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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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A Noise Estimation Method for Hyperspectral Image Based on stacked autoencoder

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IEEE ACCESS 2023年 11卷 89835-89843页

作者： Deng, Lei Zhou, Bing Ying, Jiaju Zhao, Runze Army Engn Univ PLA Elect & Opt Engn Dept Shijiazhuang 050003 Peoples R China Army Engn Univ PLA Equipment Command & Management Dept Shijiazhuang 050003 Peoples R China

The imaging spectrometer is limited by a short response time and narrow channel, resulting in a low signal-to-noise ratio of hyperspectral images. The accurate estimation of noise has a significant impact on some preprocessing and downstream tasks. The existing noise estimation methods for hyperspectral images are all focused on satellite and aviation data, and there is little research on hyperspectral images with high spatial resolution. For this type of image, This article proposes a noise estimation method based on a stacked autoencoder. Firstly, the image is divided into multiple uniform regions using the K-means algorithm, and then a stacked automatic encoder is set for each uniform region. Reconstruct the spectral signal on each pixel through the corresponding stacked automatic encoder. Calculate the residual between the reconstructed image and the original image to achieve signal-to-noise separation. Finally, the image is divided into a large number of subblocks, and the subblocks containing edges are removed. The remaining subblocks are used for noise estimation in this band. The applicability of some classic noise estimation methods was experimentally tested, and the effectiveness and stability of the proposed method were verified through simulation and real data experiments.

关键词： Hyperspectral imaging Estimation Image reconstruction Image edge detection Training Spatial resolution Noise measurement Encoding Hyperspectral images noise estimation stacked autoencoder K-means algorithm

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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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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

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KNOWLEDGE-BASED SYSTEMS 2022年第0期256卷

作者： Luo, Shuyang Huang, Xufeng Wang, Yanzhi Luo, Rongmin Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Deep transfer learning algorithm is regarded as a promising method to address the issue of rolling bearing fault diagnosis with limited labeled data. stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm. However, there are still some limitations for the transfer learning based on SAE, including the vanishing gradient problem caused by the sigmoid activation function in SAE, and low accuracy under the condition of cross-domain or limited labeled training data. In this work, an improved SAE based on convolutional shortcuts and domain fusion strategy (ISAE-CSDF) is proposed for fault diagnosis of rolling bearing. The sparse term Kullback-Leibler (KL) divergence in the original SAE is replaced with the convolutional shortcuts to prevent vanishing gradient problem and improve the feature extraction ability. The domain fusion strategy can transfer commonly shared feature information from various domains. The feasibility of ISAE-CSDF is validated on two publicly available bearing datasets and a custom-built experiment device. Results show that ISAE-CSDF outperforms the state-of-art methods in the context of different working conditions, cross-domain, and limited labeled data. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Transfer learning Fault diagnosis stacked autoencoder Convolutional shortcuts Domain fusion

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SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

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BMC BIOINFORMATICS 2021年第1期22卷 1-18页

作者： Liu, Dayun Huang, Yibiao Nie, Wenjuan Zhang, Jiaxuan Deng, Lei Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA

Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

关键词： miRNA-disease associations stacked autoencoder Latent feature XGBoost

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