Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

Advanced Feature Fusion Algorithm Based on Multiple Convolutional Neural Network for Scene Recognition

Scene recognition is a popular open problem in the computer vision field.Among lots of methods proposed in recent years,Convolutional Neural Network(CNN)based approaches achieve the best performance in scene recognition.We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network(Multi-CNN)for scene recognition.Unlike existing works that usually use individual convolutional neural network,a fusion of multiple different convolutional neural networks is applied for scene recognition.Firstly,we split training images in two directions and apply to three deep CNN model,and then extract features from the last full-connected(FC)layer and probabilistic layer on each model.Finally,feature vectors are fused with different fusion strategies in groups forwarded into SoftMax classifier.Our proposed algorithm is evaluated on three scene datasets for scene recognition.The experimental results demonstrate the effectiveness of proposed algorithm compared with other state-of-art approaches.

Noisy Chaotic Neural Network for Resource Allocation in High-Speed Train OFDMA System

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multidomain resource allocation strategy was proposed in the orthogonal frequency-division multiple access(OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy.

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing properties.In contrast to the generally used transformational learning approach,the proposed model was trained before and after clustering.The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct category.The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the instances.The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings.Models forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include.The testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured accuracy.The proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes possible.The result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.

Additive-Multiplicative Fuzzy Neural Network and Its Performance

In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation.

Application of neural networks and ultraviolet spectroscopy to simultaneous determination of multiple vitamins

Multivariate analysis and filtering techniques are widely applied to simultaneous and/or selective determination of multicomponent systems. Many methods among them are based on the principle of linear addition, while this principle does not always hold due to various physical and chemical factors. Using quite a different way, neural network (NN) based on a given learning rule, such as back propagation (BP) model, needs neither knowing nor using any form of input/output relationship. Particularly, NN can resolve various problems such as those with casual relation, those with fuzzy backgrounds, and those with uncertain inferential processes. NN was used by us to investigate quantitative struc-

A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF(Electric Field)sensors.Currently,an EF sensor or EPS(Electric Potential Sensor)system is attracting attention as a next-generationmotion sensing technology due to low computation and price,high sensitivity and recognition speed compared to other sensor systems.However,it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion,due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions.This hinders the further utilization of the EF sensing;thus,it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices.In this study,we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject.This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy.After setting motion frames,we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types.Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1%accuracy,99.25%detection rate,98.4%motion frame matching rate and 97.7%detection&extraction success rate.

Pedotransfer Functions for Estimating Soil Bulk Density:A Case Study in the Three-River Headwater Region of Qinghai Province,China

Bulk density(BD) is an important soil physical property and has significant effect on soil water conservation function. Indirect methods, which are called pedotransfer functions(PTFs), have replaced direct measurement and can acquire the missing data of BD during routine soil surveys. In this study, multiple linear regression(MLR) and artificial neuron network(ANN) methods were used to develop PTFs for predicting BD from soil organic carbon(OC), texture and depth in the Three-River Headwater region of Qinghai Province, China. The performances of the developed PTFs were compared with 14 published PTFs using four indexes, the mean error(ME), standard deviation error(SDE), root mean squared error(RMSE) and coefficient of determination(R^2). Results showed that the performances of published PTFs developed using exponential regression were better than those developed using linear regression from OC. Alexander(1980)-B, Alexander(1980)-A and Manrique and Jones(1991)-B PTFs, which had good predictions, could be applied for the soils in the study area. The PTFs developed using MLR(MLR-PTFs) and ANN(ANN-PTFs) had better soil BD predictions than most of published PTFs. The ANN-PTFs had better performances than the MLR-PTFs and their performances could be improved when soil texture and depth were added as predictor variables. The idea of developing PTFs for predicting soil BD in the study area could provide reference for other areas and the results could lay foundation for the estimation of soil water retention and carbon pool.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.