scholarly journals Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1186
Author(s):  
Yunhong Jia ◽  
Xiaodong Zhang ◽  
Zhenchong Wang ◽  
Wei Wang
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Absolute Error ◽  
Calibration Method ◽  
Heavy Duty ◽  
Maximum Absolute Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Roof Bolter ◽  
Learning Machine

Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

Identification Method of Coal Mine Water Inrush Spectrum Based on Multilayer Regularization Extreme Learning Machine

2018 ◽  
Vol 38 (7) ◽  
pp. 0730002
Author(s):  
王亚 Wang Ya ◽  
周孟然 Zhou Mengran ◽  
陈瑞云 Chen Ruiyun ◽  
闫鹏程 Yan Pengcheng ◽  
胡锋 Hu Feng ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Mine Water ◽  
Water Inrush ◽  
Identification Method ◽  
Coal Mine Water ◽  
Learning Machine

scholarly journals Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3415 ◽  
Author(s):  
Muzhou Hou ◽  
Tianle Zhang ◽  
Futian Weng ◽  
Mumtaz Ali ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Information Criterion ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Machine Model ◽  
Learning Machine

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

scholarly journals Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiao Shi-fan ◽  
Tan Jun-kun ◽  
Zhang Yong-gang ◽  
Wan Li-jun ◽  
Zhang Ming-fei ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Absolute Error ◽  
Ground Subsidence ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Foundation Pit ◽  
Machine Model ◽  
Model Based ◽  
Learning Machine

This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.

scholarly journals Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

2019 ◽  
Vol 9 (16) ◽  
pp. 3221 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Deepak Kumar ◽  
Pijush Samui ◽  
Alaa R. Gabr ◽  
Jong Wan Hu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Resilient Modulus ◽  
Particle Swarm ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Swarm Optimization ◽  
Structural Support ◽  
Rigid Pavements ◽  
Learning Machine

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data.

scholarly journals Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

2020 ◽  
Vol 12 (6) ◽  
pp. 2339 ◽  
Author(s):  
Binh Thai Pham ◽  
Trung Nguyen-Thoi ◽  
Hai-Bang Ly ◽  
Manh Duc Nguyen ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Shear Strength ◽  
Monte Carlo Simulations ◽  
Extreme Learning Machine ◽  
Mean Squared Error ◽  
Majority Vote ◽  
Absolute Error ◽  
Liquid Limit ◽  
Backward Elimination ◽  
Learning Machine

Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.

Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

2016 ◽  
Vol 48 (4) ◽  
pp. 1156-1168 ◽  
Author(s):  
Yu Feng ◽  
Daozhi Gong ◽  
Xurong Mei ◽  
Ningbo Cui
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Crop Coefficient ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Area Index ◽  
Generalized Regression ◽  
Learning Machine

Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (Kc) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

scholarly journals Improved RSSI Positioning Algorithm for Coal Mine Underground Locomotive

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Ge ◽  
Kai Wang ◽  
Jianghong Han ◽  
Bao Zhao
Keyword(s):  
Coal Mine ◽  
Gaussian Model ◽  
Correction Method ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Weighted Model ◽  
The Difference ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication

Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed. The RSSI value fluctuates heavily due to the poor environment of coal mine underground. The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error and was given a corresponding weight. The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10 m of communication range, positioning error can be maintained at 0.5 m.

scholarly journals Research of Pose Control Algorithm of Coal Mine Rescue Snake Robot

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yun Bai ◽  
YuanBin Hou
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Control Algorithm ◽  
Weight Coefficient ◽  
Swarm Optimization ◽  
Snake Robot ◽  
Pose Control ◽  
Learning Machine ◽  
Hidden Layer ◽  
Mine Rescue

Aiming at how to achieve optimal control of joint pitch angles in the process of the robot surmounting obstacle, taking the developed coal mine rescue snake robot as an experimental platform, a pose control algorithm based on particle swarm optimization weight coefficient of extreme learning machine (PSOELM) is proposed. In order to obtain the optimized hidden layer matrix of the extreme learning machine (ELM), particle swarm optimization (PSO) is applied to optimize the weight coefficient of hidden layer matrix. The simulation and experiment results prove that, compared with the ELM algorithm, the smaller mean square error (MSE) between the joint pitch angles of robot and the expected values is acquired by the PSOELM, which overcomes the shortcoming that traditional extreme learning machine cannot reach the best performance because of the random selection of the parameters of the hidden layer nodes. PSOELM is superior to ELM algorithm in control accuracy, fast searching for the optimal and stability. Optimal control of robot’s joint pitch angles is achieved. The algorithm is applied to the surmounting obstacle control of the developed snake robot, and it lays the foundation for further implement of the coal mine rescue.

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