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Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第7期15卷 1749-1758页

作者： Danjie Sheng Jin Yu Fei Tan Defu Tong Tianjun Yan Jiahe Lv Faculty of Engineering China University of GeosciencesWuhan430074China CCCC Second Harbor Engineering Compasny Ltd. Wuhan430040China

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

关键词： Rock mass quality classification Deep learning Stacked autoencoder(SAE) back propagation algorithm

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 215-230页

作者： S.Muthukumaran P.Geetha E.Ramaraj Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Dr.Umayal Ramanathan College for WomenKaraikudi630003India

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)algorithm with Genetic algorithm(GA)and Multi Objective Particle Swarm Optimization algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network back propagation algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

关键词： ANN back propagation algorithm genetic algorithm multi objective particle swarm optimization algorithm

Artificial neural network approach to assess selective flocculation on hematite and kaolinite

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International Journal of Minerals,Metallurgy and Materials 2014年第7期21卷 637-646页

作者： Lopamudra Panda P.K.Banerjee Surendra Kumar Biswal R.Venugopal N.R.Mandre R&D Tata Steel Limited Institute of Minerals and Materials Technology(Council of Scientific and Industrial Research) Department of Fuel & Minerals Engineering Indian School of Mines University

Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.

关键词： hematite kaolinite flocculation artificial neural networks back propagation algorithm Fourier transform infrared spectroscopy separation efficiency

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

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Wuhan University Journal of Natural Sciences 2005年第6期10卷 1009-1012页

作者： ZHOU Hui-lin TIAN Mao CHEN Xiao-li School of Electronic Information Wuhan University Wuhan 430072 Hubei China

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

关键词： ground penetrating radar nonstationary signal dyadic wavelet transform feed-forward multi-layer perceptron back propagation algorithm

A Method for the Environmental Quality Assessment of Surface Water Based on Artificial Neural Networks

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Systems Science and Systems Engineering 2000年第1期10卷 72-76页

作者： YANG Guo\|dong\+1,\ PAN Da\|feng\+2,\ FAN Ying\|fang\+3 1.Dep. of Environmental Science, Shanxi Uni., Taiyuan 030006, China 2.Shanxi Academy of Agriculture Science, Taiyuan 030031, China 3.Institute of Molecular Science, Shanxi Uni., Taiyuan 030006, C

An assessment method for the environmental quality of surface water was established based on artificial neural networks (ANN), in which different classification values were trained as learning samples. The assessment results from the major river section in Datong city indicated that the ANN model have characteristics of simple operation and distinct and quantitative expression of assessment results in comparison with the standard index method for the environmental quality assessment of surface water.

关键词： artificial neural newtorks back propagation algorithm surface water quality comprehensive assessment

Neural Network Approach Based on Agent to Predict Stock Performance

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International Journal of Systems and Control

International Journal of Systems and Control 2008年第3期 173-179页

作者： SHAO Yan-hua1,2,LI Jian-shi1,CHEN Tian-jian 1,TIAN Feng1,WANG Jin-rong1 1.Department of Computer Science and Technology,University of Guizhou,Guiyang 550025,China 2.Department of Economics and Management,Guizhou University for Nationalities,Guiyang 550025,China

Predicting stock performance is a very large and profitable area of study.A large number of studies have been reported in literature with reference to the use of artificial neural network in modeling stock performance in western countries.However,not much work along the approach to neural network based on agent has been reported.This thesis focuses on the development and the simulation of a stock market performance model of utilizing a neural network approach base on agent.This model is easy to understand,and can be easily implemented as a software simulation.First we will discuss the basic concepts behind this type of neural network based on agent,then,we'll get into some of the more application ideas.

关键词： Artificial Neural Network back propagation algorithm Agent Swarm Simulation Toolkit

BP Net for Forecasting Reservoir Parameters

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BP Net for Forecasting Reservoir Parameters