scholarly journals Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 614 ◽  
Author(s):  
Baoxian Wang ◽  
Yiqiang Li ◽  
Weigang Zhao ◽  
Zhaoxi Zhang ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Damage Detection ◽  
Extreme Learning Machine ◽  
Crack Detection ◽  
Feature Learning ◽  
Feature Representation ◽  
Fine Tuning ◽  
Detection Model ◽  
Concrete Crack ◽  
Learning Machine ◽  
Sparse Feature Representation

Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

2019 ◽  
Vol 14 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Ji-Yong An ◽  
Yong Zhou ◽  
Lei Zhang ◽  
Qiang Niu ◽  
Da-Fu Wang
Keyword(s):  
Extreme Learning Machine ◽  
Functional Organization ◽  
Web Server ◽  
Feature Representation ◽  
Experimental Results ◽  
Evolutionary Information ◽  
Svm Classifier ◽  
Interacting Proteins ◽  
Learning Machine ◽  
Human Dataset

Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell. Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation. Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. </P><P> Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods. Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

scholarly journals Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1262 ◽  
Author(s):  
Xiaoping Fang ◽  
Yaoming Cai ◽  
Zhihua Cai ◽  
Xinwei Jiang ◽  
Zhikun Chen
Keyword(s):  
Multiobjective Optimization ◽  
Extreme Learning Machine ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Band ◽  
Feature Learning ◽  
Pareto Solution ◽  
Conflicting Objectives ◽  
Learning Machine ◽  
Hidden Layer

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

scholarly journals Crack Detection in Folded Plates With Back-Propagated Artificial Neural Network

2021 ◽  
Author(s):  
Can Gonenli ◽  
Oguzhan Das ◽  
Duygu Bagci Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Crack Detection ◽  
Structural Integrity ◽  
Engineering Structures ◽  
Detection Techniques ◽  
Detection Model ◽  
Artificial Neural ◽  
Destructive Methods

Abstract Engineering structures may face various damages such as crack, delamination, and fatigue in several circumstances. Localizing such damages becomes essential to understand the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Back-propagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions have been modeled in flat and four different folded structures by utilizing the Finite Element Method. The dataset required to perform the crack localization procedure includes the first ten natural frequencies of all structures as input variables. As output variables, the dataset contains a total of 500 crack locations for five structures. It is concluded that the BPANN can localize all cracks with an average accuracy of 95.12%.

Study on novel signal processing and simultaneous-fault diagnostic method for wind turbine

2019 ◽  
Vol 41 (14) ◽  
pp. 4100-4113
Author(s):  
Xian-Bo Wang ◽  
Pu Miao ◽  
Kun Zhang ◽  
Xiaoyuan Zhang ◽  
Jun Wang
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Extreme Learning Machine ◽  
Feature Learning ◽  
Mode Decomposition ◽  
Vibrational Signals ◽  
Fault Diagnostic ◽  
Diagnostic Framework ◽  
Learning Machine ◽  
Signal Processing Methods

High-precision fault diagnosis is important for the widely installed complex industrial product, the wind turbine. However, intelligent monitoring is difficult due to the fuzzy boundaries and individual different variations of the unseen single or simultaneous-fault of such intricate equipment. To solve this problem, this study proposes an ensemble fault diagnostic framework for simultaneous and coupling failure. First, this paper develops novel signal processing methods for effective feature learning and mapping from the non-stationary and nonlinear raw vibrational signals. The adapted variational mode decomposition is introduced based on the particle swarm optimization that applies the minimum mean envelope entropy to optimize the parameters settings. Second, the novel ensemble extreme learning machine-based network is proposed to isolate the faults that applies one extreme learning machine network to count the number of fault scenarios, and the other one to identify the specific single or simultaneous-fault labels. With this scheme, the self-adaptive ensemble extreme learning machine-based fault diagnostic framework is more accurate and faster than the prevailing probabilistic classifier-based methods, as the proposed method does not rely on empirically specified decision-making threshold and generates all the candidate fault labels at the same time. Finally, this study builds the test platform and compares the overall results with the existing feature analysis methods and classifiers. The experimental results verify that the proposed framework detects both single and simultaneous-fault accurately and quickly.

Application of Extreme Learning Machine to Damage Detection of Plate-Like Structures

2017 ◽  
Vol 17 (07) ◽  
pp. 1750068 ◽  
Author(s):  
S. S. Kourehli
Keyword(s):  
Damage Detection ◽  
Extreme Learning Machine ◽  
Damage Identification ◽  
Mode Shapes ◽  
Effective Technique ◽  
Plate Structures ◽  
Modal Data ◽  
Plate Elements ◽  
Damage States ◽  
Learning Machine

An effective method for damage detection of plate structures using the extreme learning machine (ELM) is proposed in this study. With the ELM, the mode shapes and natural frequencies of a damaged plate are treated as the input and the damage states in the plate elements as the output. The proposed method was applied to two numerical examples, namely, a cantilever and a plate with four-fixed supports containing one or several damages with and without noise in the modal data. The results obtained reveal that the methodology can be used as an effective technique for the damage identification of plate structures using the modal data and ELM.

scholarly journals Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 135 ◽  
Author(s):  
Siti Nurmaini ◽  
Annisa Darmawahyuni ◽  
Akhmad Noviar Sakti Mukti ◽  
Muhammad Naufal Rachmatullah ◽  
Firdaus Firdaus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Stress Test ◽  
Feature Learning ◽  
Feature Representation ◽  
Training Data ◽  
Fine Tuning ◽  
Noninvasive Test ◽  
Heartbeat Classification ◽  
Unseen Data

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

scholarly journals An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

2020 ◽  
Vol 10 (5) ◽  
pp. 1827 ◽  
Author(s):  
Rodrigo Olivares ◽  
Roberto Munoz ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Diego Cárdenas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Neurodegenerative Disorder ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Classification Problems ◽  
Learning Machine

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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