Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

Over the last decades,the development of Geographic Information System(GIS) technology has provided a method for the evaluation of landslide hazard.Through the use of direct-reverse DEM technology,the Changshougou valley is divided into 216 slope units,which includes 123 landslide units.According to mechanism analyses of landslides in the study area,six environmental factors are selected to evaluate the landslide occurrence,such as elevation,slope,aspect,curvature,distance to rivers,and human activities.Each factor is extracted in terms of slope unit within the scope of ArcGIS.The spatial analysis shows that most of landslides in the Changshougou valley are located at the elevation ranging from 100 to 150 m,with an aspect of 135°~225° and 40°~60° in slope,and on convex slopes,which are also influenced by hydrological processing and human activities.After the spatial analysis of environmental factors,this paper presents a case study for landslide hazard prediction,using back-propagation artificial neural network modeling optimized by genetic algorithms.Parameters of genetic algorithms and neural networks are set.The population size is 100,crossover probability 0.65,mutation probability 0.01,momentum factor 0.60,learning rate 0.7,max learning number 10000,and target error 0.000001.From a database of 216 landslides,120 landslides are used for training neural network models,and 96 landslides are used for the validation of landslide susceptibility.Comparing landslide presence with a susceptibility map,it is noted that the prediction accuracy of landslide occurrence is 93.02%,while the units without landslide occurrence is predicted with accuracy of 81.13%.The verification shows satisfactory agreement with accuracy of 86.46% between the susceptibility map and the landslide locations.In this case study,it is also found that some disadvantages can be overcome in the application of BP neural networks,for example,the low convergence rate and local minimum,after the optimization is carried out using genetic algorithm.In conclusion,we note that genetic algorithm-BP neural networks are an effective method to predict landslide hazard with high accuracy.

Application of back propagation neural networks with optimization of genetic algorithms to landslide hazard prediction

In response to the relatively huge error, low efficiency, and low accuracy in the prediction and analysis data on the quality of multimedia professional talents at present, an analytical method for the prediction of the quality of multimedia professional talents based on the multiobjective data fuzzy evolutionary algorithm is proposed in this paper. The weights of the talent quality data are analyzed in detail mainly based on the multiobjective data fuzzy evolution algorithm to establish a prediction and analysis model for the quality of multimedia professional talents. This model can be used to implement the prediction and analysis of the comprehensive abilities of multimedia professional talents effectively. Finally, through a practical case study, it is verified that the method put forward in this paper has high efficiency and high accuracy in predicting and analyzing the quality of multimedia professional talents.

/pdf/prediction-and-analysis-of-the-quality-of-multimedia-3m6ugebpdq.pdf

Prediction and Analysis of the Quality of Multimedia Professional Talents Combining Multiobjective Data Fuzzy Evolution

A BP artificial neural network model was developed to predict monthly mean daily global solar radiation of Lanzhou by using the meteorological observation dataThe model used the interpretation diagram method and connection weights method to compute the relative contribution of each input variable for estimating the importance to the output variableThen,the redundant input variables can be eliminatedThe measured meteorology values from 1971 to 1995 for Lanzhou station were used for the training of the networkThen we can predict the solar radiation from 1996 to 2000 with the simplified networkThe measured values of monthly mean daily global solar radiation for Lanzhou station were analyzed to test the network modelThe experimental results indicate that the method can increase the transparency of model and improve the reliability and robustness of the modelThe model has the smaller error and the higher precisionThe coefficient of determination is 0987Results have shown better agreement between the estimated and measured values of global solar radiation than other empirical modelsTherefore,using the BP neural network model with the meteorological observation data is a very effective method to predict the solar radiation of some region which has no radiation observation sites

Simulation of solar radiation in Lanzhou based on BP neural network

Analysis of Mission-Oriented Aeronautic Spare Parts Demand Prediction

The problem of low digging efficiency and mining imbalance due to outdated digging technology and low degree of equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key to the intelligence of roadheading equipment. Accurate lithology identification significantly affects the automatic control of roadheader cutting conditions. Completing the identification of lithology in the process of rock wall cutting by a roadheader involved the following steps: building a tunneling experiment platform, making four rock specimens with different lithologies, completing the tunneling simulation experiments on four lithologies, obtaining current sensor data of four lithologies cutting, and finally proposing an intelligent lithology identification method of PSO-VMD-LSSVM. The research results show that the particle swarm algorithm (PSO) optimized the variational modal decomposition (VMD) with minimum envelope information entropy as the fitness function can realize the adaptive decomposition of the current signal of truncated motors. The signal reconstruction can increase the signal-to-noise ratio of the current signal by selecting the eigenmodal components according to the energy density and correlation coefficient criterion. The multi-scale fuzzy entropy is used as the eigenvector of the reconstructed current signal as the fuzzy entropy of different lithology cut-off motor currents has better differentiation at different scales. The least-squares support vector machine (LSSVM) is used to classify the feature vectors processed by custom decomposition parameter VMD and gives a recognition rate of 87.5%. The recognition rate increases to 97.5% for the feature vectors processed by PSO-VMD. The particle swarm algorithm optimizes the noise reduction via VMD to effectively improve the lithology recognition rate. The research results can provide a methodological reference for rock property recognition during rock cutting by a roadheading machine.

/pdf/identification-of-rock-properties-of-rock-wall-cut-by-dm7nhchi.pdf

Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM

Air material demand forecast is an important content of air material management, How to scientifically determine the general aircraft material demand has always been a key research subject of general aviation enterprise. Consider the general aircraft material demand forecast problem, forecast method is proposed by using Modified Particle Swarm Optimization (MPSO) algorithm to optimize the BP neural network. Firstly analyzed the main influence factors of general aircraft material demand, then introduces the basic principle of BP neural network and PSO algorithm and its improvement and MPSO-BP neural network forecast model is constructed, finally case analysis is carried out by using historical data of general aviation enterprise. The results show that the model prediction accuracy is improved effectively and good results have been achieved.

http://www.sersc.org/journals/IJMUE/vol11_no6_2016/16.pdf

General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network

Drilling rate of penetration (ROP) optimization is crucial due to its vital role in increasing the drilling efficiency and decreasing the non-productive time. The existing methods seldom pay attention to updating model parameter during drilling, which is actually important for ROP optimization under dynamical and uncertain downhole condition. In this paper, an online drilling ROP optimization method is proposed via three stages (drilling data pre-processing, online ROP modelling and online optimal operational parameters optimization). In the first stage, drilling data is pre-processed by resampling and Savitzky-Golay filter. Following this, support vector regression and moving-window strategy are introduced together to establish an online ROP model in the next stage. Finally, particle swarm optimization algorithm is applied in the online ROP optimization framework to obtain the optimal operational parameters in the last stage. Simulation results indicated the effectiveness of the proposed method.

An online drilling rate of penetration (ROP) optimization method

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Papers

Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM

General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network

An online drilling rate of penetration (ROP) optimization method