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Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction

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APPLIED ENERGY 2019年 254卷 113686-000页

作者： Liu, Hui Chen, Chao Cent South Univ Sch Traff & Transportat Engn IAIR Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

Accurate wind speed prediction is essential for proper use of wind energy resource. In this paper, a novel hybrid multi-step wind speed forecasting model is developed, which consists of sparse feature extraction, bidirectional deep learning, multi-objective optimization, and adaptive decomposition-based error correction. Apart from the traditional average-based resolution transformation method, a two-layer stacked sparse autoencoder (SSAE) is proposed to extract the hidden representation of original 3 s high-resolution wind speed data. Trained by the data generated from different resolution transformation methods, two bidirectional long short-term memory (BiLSTM) networks serve as base predictors and provide 10-step forecasting results. The results of base predictors are reasonably ensembled by multi-objective multi-universe optimization (MOMVO). Moreover, to reduce the predictable components in error series further, a correction model based on empirical wavelet transform (EWT) and outlier robust extreme learning machine model (ORELM) is constructed to reduce the forecasting error further. The effectiveness of the proposed hybrid model is comprehensively evaluated by a series of experiments. The experimental results demonstrate that: (a) the proposed model is well trained, with great convergence, and an average RMSE of 0.2618 m/s in 10-step forecasting;(b) the proposed model outperforms other existing models in all experimental sites and forecasting steps;(c) the multi-objective optimization algorithm can rationally integrate base predictors to obtain better performance in each step;(d) the proposed residual error correction model can generate more than 78% improvement of RMSE, significantly better than compared correction methods.

关键词： Wind speed forecasting stacked sparse autoencoder Bidirectional long short-term memory Multi-objective multi-universe optimization Residual error correction

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Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

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JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2018年第2期12卷 73-84页

作者： Wang, Peng-Fei Luo, Xiao-Qing Li, Xin-Yi Zhang, Zhan-Cheng Jiangnan Univ Sch IoT Engn Wuxi Peoples R China Suzhou Univ Sci & Technol Coll EIE Suzhou Peoples R China

stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform;second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands;third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients;then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients;finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.

关键词： Image fusion stacked sparse autoencoder shift invariant shearlet transform feature extraction

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Classification of PolSAR Images Based on Adaptive Nonlocal stacked sparse autoencoder

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第7期15卷 1050-1054页

作者： Hu, Yuanyuan Fan, Jianchao Wang, Jun Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Natl Marine Environm Monitoring Ctr Dalian 116023 Peoples R China

Land cover classification using polarimetric synthetic aperture radar (PolSAR) images is an important tool for remote sensing analysis. In view that PolSAR image effective interpretation is commonly affected by the absence of discriminative features and the presence of speckle noises, this letter proposes an adaptive nonlocal stacked sparse autoencoder (ANSSAE) to achieve PolSAR image classification. It extracts the adaptive nonlocal spatial information by adaptively calculating weighted average values of each pixel from nonlocal regions, which can reduce the influence of speckle noises and retain edge details. In the first layer of the ANSSAE, the adaptive nonlocal spatial information is introduced into the objective function to obtain the robust feature representation, whose effects would transfer to the rest of layers. Therefore, the ANSSAE can automatically capture spatial-related, robust, and distinguishable features, which can suppress speckle noises and gain accurate classification results. Experimental results on two real PolSAR images demonstrate that the proposed approach can significantly improve the classification accuracy.

关键词： Adaptive nonlocal spatial information polarimetric synthetic aperture radar (PolSAR) image classification stacked sparse autoencoder

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Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using stacked sparse autoencoder

Missing Structural and Clinical Features Imputation for Semi...

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IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health

作者： Jabason, Emimal Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781538636039

In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been given paramount importance in the medical field. However, in reality, the input datasets contain lots of missing values due to several factors such as increasing mortality rate, avoiding invasive procedures, and dropping out from the study. In this work, after analyzing the pattern of structural and clinical data from tadpole study in Alzheimer's disease neuroimaging initiative (ADNI) database, it has been found that the unobserved data are not missing completely at random. In view of this fact, with the assumption that the missing data patterns are in blocks, we propose a novel stacked sparse autoencoder based method to assign a value in the missing places and to select the significant structural and clinical features in order to discriminate the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results, it is shown that the proposed imputation algorithm achieves better performance for semi-supervised AD classification in terms of accuracy, sensitivity, and specificity in 5-fold cross validation when compared to the state-of-the-art methods.

关键词： Alzheimer's disease (AD) Structural magnetic resonance imaging (MRI) Neurophysiological test Imputation Feature selection stacked sparse autoencoder Supervised classification

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Application of stacked sparse autoencoder in Automated Detection of Glaucoma in Fundus Images

Application of Stacked Sparse Autoencoder in Automated Detec...

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Conference on Unconventional Optical Imaging

作者： Pratiher, Sawon Chattoraj, Subhankar Vishwakarma, Karan Indian Inst Technol Kharagpur Kharagpur WB India Techno India Univ Salt Lake W Bengal India

ISBN: (数字)9781510618817

ISBN: (纸本)9781510618817

In this contribution, intelligent identification of glaucoma from digital fundus images using stacked sparse autoencoder (SSAE) is proposed. The fundus images are initially converted to gray-scale and normalized w.r.t., background illuminance while maintaining contrast constancy across the dataset. Unfolded feature vectors from the pre-processed with proper rescaling and grays-scale converted fundus images are fed to SSAE for learning efficient feature representation and classification thereof using a softmax layer. A comparative evaluation highlighting the superiority of SSAE method with existing state-of the art techniques is presented to validate its efficacy in glaucoma detection. The proposed framework can be used as a clinical decision support system assisting ophthalmologists in confirming their diagnosis with high reliability & accuracy.

关键词： Medical imaging Fundus image Glaucoma stacked sparse autoencoder

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Vertebrae Segmentation via stacked sparse autoencoder from Computed Tomography Images

Vertebrae Segmentation via Stacked Sparse Autoencoder from C...

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2019第十一届数字图像处理国际会议

作者： Syed Furqan Qadri Zhiqi Zhao Danni Ai Mubashir Ahmad Yongtian Wang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and PhotonicsBeijing Institute of Technology AICFVE of Beijing Film Academy

An accurate vertebrae segmentation in the spine is an essential pre-requisite in many applications of image-based spine assessment,surgical planning,clinical diagnostic treatment,and biomechanical *** this paper,we present the stacked sparse autoencoder（SSAE） model for the segmentation of vertebrae from CT *** the preprocessing step,we extracted overlapped patches from the vertebrae CT images as the inputs of our proposed *** SSAE model was trained in an unsupervised way to learn high-level features from the input pixels of the unlabeled images *** improve the discriminability of the learned features,we further refined the feature representation in a supervised fashion and fine-tuned the whole model by using the feedforward neural network parameters for classifying the overlapped *** then validated our model on a publicly available MICCAI CSI2014 dataset and found that our model outperforms the other state-of-the-art methods.

关键词： Vertebrae segmentation deep learning stacked sparse autoencoder CT images

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Predicting Lymph Node Metastasis of Lung Cancer Using stacked sparse autoencoder 14

Predicting Lymph Node Metastasis of Lung Cancer Using Stacke...

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14th IEEE International Conference on Signal Processing (ICSP)

作者： Wang Wei Wang Yisong Wang Lihui Sun Caixia Cheng Xinyu Li Zhi Zhang Jian Yang Feng Zhu Yuemin Guizhou Univ Key Lab Intelligent Med Image Anal & Precise Diag Sch Comp Sci & Technol Guiyang Guizhou Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Univ Lyon INSA Lyon INSERM CNRSCREATIS UMR 5220U1206 F-69621 Lyon France

ISBN: (纸本)9781538646731

Predicting accurately the lymph node metastasis is very essential for right staging and making the appropriate treatment strategies for lung cancer. Existing prediction methods are mostly based on the image features, which depend greatly on the postprocessing algorithms. To deal with this problem, we proposed to use the stacked sparse autoencoder (SSAE) network to predict the lymph node metastasis in lung cancer. To assess the performance of SSAE, the radiomics features based prediction and image based SSAE prediction were quantitatively compared in terms of ROC and AUC. In the radiomics-based prediction, more than 400 features were fed into a SSAE network, and in the image-based SSAE prediction, 100 images were inputted. The experimental results show that the image-based SSAE experienced a better performance than that radiomics-based one, even with a fewer samples to train the network. The AUC obtained by image-based SSAE was enhanced by about 50%.

关键词： Lung cancer stacked sparse autoencoder lymph node metastasis

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Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder

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JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷 042604-1-042604-15页

作者： Abdi, Ghasem Samadzadegan, Farhad Reinartz, Peter Univ Tehran Fac Surveying & Geospatial Engn Coll Engn Tehran Iran German Aerosp Ctr DLR Remote Sensing Technol Inst Dept Photogrammetry & Image Anal Wessling Germany

Classification of hyperspectral remote sensing imagery is one of the most popular topics because of its intrinsic potential to gather spectral signatures of materials and provides distinct abilities to object detection and recognition. In the last decade, an enormous number of methods were suggested to classify hyperspectral remote sensing data using spectral features, though some are not using all information and lead to poor classification accuracy;on the other hand, the exploration of deep features is recently considered a lot and has turned into a research hot spot in the geoscience and remote sensing research community to enhance classification accuracy. A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral-spatial information. A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectralspatial information;then, a soft classifier is employed to train high-level features and to fine-tune the deep learning architecture. Comparative experiments are performed on two widely used hyperspectral remote sensing data (Salinas and PaviaU) and a coarse resolution hyperspectral data in the long-wave infrared range. The obtained results indicate the superiority of the proposed spectral-spatial deep learning architecture against the conventional classification methods. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： deep features deep learning hyperspectral imagery classification softmax regression spectral-spatial unsupervised feature learning stacked sparse autoencoder

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Spectral-spatial classification of hyperspectral images using trilateral filter and stacked sparse autoencoder

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Zhao, Chunhui Wan, Xiaoqing Zhao, Genping Yan, Yiming Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

A spectral-spatial classification method using a trilateral filter (TF) and stacked sparse autoencoder (SSA) for improving the classification accuracy of hyperspectral image (HSI) is proposed. The operation is carried out in two main stages: edge-preserved smoothing and high-level feature learning. First, a reference image obtained from dual tree complex wavelet transform is adopted in a TF for smoothing the HSI. As expected, the filter not only can effectively attenuate the mixed noise (e. g., Gaussian noise and impulse noise) where the bilateral filter shows poor performance but also can produce useful spectral-spatial features from HSI by considering geometric closeness and photometric similarity between pixels simultaneously. Second, an artificial fish swarm algorithm (AFSA) is first introduced into a SSA, and the proposed deep learning architecture is used to adaptively exploit more abstract and differentiable high-level feature representations from the smoothed HSI, based on the factor that AFSA provides better trade-off among concurrency, search efficiency, and convergence rate compared with gradient descent and back-propagation algorithms in a traditional SSA. Finally, a random forest classifier is utilized to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed method generates competitive performance compared with those of conventional methods. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： classification hyperspectral imagery spatial filtering artificial fish swarm algorithm stacked sparse autoencoder

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Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2018年第12期29卷 125002-125002页

作者： Saufi, Syahril Ramadhan bin Ahmad, Zair Asrar Leong, Mohd Salman Lim, Meng Hee Univ Teknol Malaysia Fac Engn Sch Mech Engn Skudai 81310 Malaysia Univ Teknol Malaysia Inst Noise & Vibrat Kuala Lumpur 54100 Malaysia

The rolling clement bearing is an important component in most rotating machinery. The unexpected failure of a bearing may cause the whole mechanism to break down. Hence, research has focused on developing effective intelligent fault diagnosis to generate more accurate and robust diagnostic results. Bearing fault diagnosis based on stacked sparse autoencoder (SSAE) architecture is proposed in this study. SSAE is capable of providing a featureless methodology for bearing fault diagnosis. However, the architecture of SSAE is greatly influenced by its hyperparameter settings and there is no standard method of determining the optimal hyperparameter values. In addition, the standard learning algorithm used in SSAE architecture is time-intensive. In this paper, a method that combines differential evolution and a resilient back-propagation approach is proposed to improve the performance of SSAE networks in bearing fault classification. The differential evolution approach optimised SSAEs hyperparameters such as the hidden nodes number, weight decay parameter, sparsity parameter, and weight of the sparsity penalty term, that are associated with each hidden layer of SSAE networks. An increase in the hidden layers of SSAE will further complicate the hyperparameter selection process. The resilient back-propagation training algorithm is used to train the SSAE network due to its low computation cost. Results from analysis of three databases demonstrate that the proposed model achieved 99% performance accuracy in bearing fault diagnosis. The proposed model is found to be more user-friendly and effective in handling multi-condition of bearing faults compared to the original autoencoder.

关键词： bearing fault diagnosis stacked sparse autoencoder resilient back-propagation differential evolution scalogram

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