scholarly journals Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhenhua Yang ◽  
Hongwei Zhang ◽  
Sheng Li ◽  
Chaojun Fan
Keyword(s):  
Coal Seam ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Prediction Method ◽  
Gas Content ◽  
Percentage Error ◽  
Support Vector ◽  
Prevention Measures ◽  
Residual Gas ◽  
Learning Machine

In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.

scholarly journals Two-Dimensional Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Jia ◽  
Dong Li ◽  
Zhisong Pan ◽  
Guyu Hu
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Studies ◽  
Back Propagation ◽  
Image Data ◽  
Network Models ◽  
Support Vector ◽  
Two Dimensional ◽  
Neural Network Models ◽  
Efficiency And Effectiveness ◽  
Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

scholarly journals Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sunday O. Olatunji ◽  
Taoreed O. Owolabi
Keyword(s):  
Barium Titanate ◽  
Band Gap ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Energy Gap ◽  
Gravitational Search Algorithm ◽  
High Rate ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Machine

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

scholarly journals Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1850
Author(s):  
Long Chen ◽  
Xiaojie Zhi ◽  
Hai Wang ◽  
Guanjin Wang ◽  
Zhenghua Zhou ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Traffic Accidents ◽  
Back Propagation ◽  
Learning Technologies ◽  
Support Vector ◽  
Driver Fatigue ◽  
Learning Machine ◽  
Fatigue Driving

Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.

scholarly journals Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Sandstone Reservoir

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cao ◽  
Jucheng Yang ◽  
Yan Wang ◽  
Dan Wang ◽  
Yancui Shi
Keyword(s):  
Parameter Estimation ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Support Vector ◽  
Core Data ◽  
Porosity And Permeability ◽  
Sandstone Reservoir ◽  
Learning Machine ◽  
Reservoir Parameter ◽  
Propagation Network

This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples from 8 wells with core data have been used to train and validate the neural network, and robust ELM algorithm provides fast and accurate prediction results, which is also testified by comparison with BP (back propagation) network and SVM (support vector machine) approaches. The network model is then applied to estimate porosity and permeability for the remaining wells. The predicted attributes match well with the oil test conclusions. Based on the estimations, reservoir porosity and permeability have been mapped and analyzed. Two favorable zones have been suggested for further research in the survey.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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