scholarly journals A Model to Predict Crosscut Stress Based on an Improved Extreme Learning Machine Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 896 ◽  
Author(s):  
Xiaobo Liu ◽  
Lei Yang ◽  
Xingfan Zhang ◽  
Liancheng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Underground Mining ◽  
Liaoning Province ◽  
Stress Monitoring ◽  
Network Algorithms ◽  
Iron Mine ◽  
Time In Literature ◽  
Learning Machine ◽  
First Time

The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut’s stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms.

scholarly journals Water inrush characteristics and hazard effects during the transition from open-pit to underground mining: a case study

2019 ◽  
Vol 6 (3) ◽  
pp. 181402
Author(s):  
Huijie Zhang ◽  
Bin Zhang ◽  
Nengxiong Xu ◽  
Lei Shi ◽  
Hanxun Wang ◽  
...  
Keyword(s):  
Mine Water ◽  
Underground Mining ◽  
Water Inrush ◽  
Mine Safety ◽  
Liaoning Province ◽  
Open Pit ◽  
Comprehensive Method ◽  
Iron Mine ◽  
Mine Roadway

During the transition from open-pit to underground mining in iron ore mines, water inrush is a prominent problem for mine safety and production. In this paper, a comprehensive method that incorporates hydrochemical analysis and numerical simulation is proposed to analyse the characteristics of water inrush during the transition from open-pit to underground mining. The proposed method revealed the migration law of groundwater and analysed the source of mine water inrush in the Yanqianshan iron mine located in Liaoning province, China. The results show that the excavated mine roadway is the primary factor affecting groundwater migration and that the source of the mine water inrush is the groundwater in the aquifer around the mine roadway. Moreover, based on the results of the study, appropriate methods for prevention and treatment of mine water inrush were proposed. This approach provides a novel idea for the assessment of water inrush hazards and will serve as a valuable reference for analogous engineering cases.

Extreme Learning Machine Based Channel Estimator and Equalizer for Underground Mining VLC Systems

2021 ◽  
Author(s):  
Pablo Palacios Jativa ◽  
Raimundo Becerra ◽  
Cesar A. Azurdia-Meza ◽  
David Zabala-Blanco ◽  
Ismael Soto ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Underground Mining ◽  
Channel Estimator ◽  
Learning Machine

A novel MADM approach to the ground-handling agent selection problem in B2B markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahmut Bakır ◽  
Emircan Özdemir ◽  
Şahap Akan
Keyword(s):  
Airline Industry ◽  
Multiple Attribute Decision Making ◽  
Content Type ◽  
B2b Marketing ◽  
Multiple Attribute ◽  
Time In Literature ◽  
Agent Selection ◽  
Ground Handling ◽  
First Time

PurposeGround-handling services are important for effective aircraft operations in the air transportation system. Airlines often outsource these services to ground-handling agents through business-to-business (B2B) marketing decisions. Therefore, this paper aims to address the problem of ground-handling agent selection in the airline industry.Design/methodology/approachA real-world case study was carried out to demonstrate the applicability of the integrated best worst method and fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA) approach to solve ground-handling agent selection problems under uncertainty and imprecision. A two-stage sensitivity analysis was also conducted to ensure the credibility and validity of the application.FindingsIn the weighting stage, “Quality” was determined as the most important criterion in terms of supplier performance. With regard to the performance of the ground-handling agents, A2 was found as the optimal supplier in terms of both credibility and validity.Practical implicationsThis study enumerated several criteria that ground-handling agents must meet in order to effectively supply services for the airlines. In addition, this study provides a novel framework from which managers can gain additional benefits from their businesses. Finally, it is concluded that this approach will help airline managers quantitatively in choosing the most appropriate ground-handling agent.Originality/valueThe contributions of this study to the existing literature are twofold. First, we propose a novel multiple attribute decision-making approach to address the problem of supplier selection for airlines under uncertainty and imprecision. Second, the selection of ground-handling agents from the B2B perspective is addressed for the first time in literature.

Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction

2019 ◽  
Vol 12 (2) ◽  
pp. 175-193
Author(s):  
Smita Rath ◽  
Binod Kumar Sahu ◽  
Manoj Ranjan Nayak
Keyword(s):  
Performance Analysis ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Forecasting Model ◽  
Symbiotic Organisms Search ◽  
Content Type ◽  
Stock Indices ◽  
Learning Machine ◽  
Symbiotic Organisms ◽  
First Time

Purpose Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices. Design/methodology/approach A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms. Findings Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study. Originality/value The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

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