scholarly journals A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

2021 ◽  
Author(s):  
Amoussou Coffi Adoko ◽  
Festus Saadaari ◽  
Daniel Mireku-Gyimah ◽  
Askar Imashev
Keyword(s):  
Neural Network ◽  
Stability Conditions ◽  
Feed Forward Neural Network ◽  
Graph Method ◽  
Average Accuracy ◽  
Artificial Neural Network Ann ◽  
Neural Network Classifiers ◽  
The Stability ◽  
Stability Graph ◽  
Stope Design

AbstractAssessing the stability of stopes is essential in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the profitability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes.

New rock stress factor for the stability graph method

2011 ◽  
Vol 48 (1) ◽  
pp. 141-145 ◽  
Author(s):  
Hani S. Mitri ◽  
Rory Hughes ◽  
Yaohua Zhang
Keyword(s):  
Stress Factor ◽  
Rock Stress ◽  
Graph Method ◽  
The Stability ◽  
Stability Graph

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

ANN Modeling of Motional Resistance for Micro Disk Resonator

2020 ◽  
pp. 199-216
Author(s):  
Manjula A. Sutagundar ◽  
Basavaprabhu G. Sheeparamatti ◽  
Dakshayani S. Jangamshetti
Keyword(s):  
Neural Network ◽  
Resonance Frequency ◽  
Simulation Software ◽  
Equivalent Model ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Disk Resonator ◽  
Motional Resistance ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

This article describes how modeling is an integral part of design and development of any system that provides the theoretical characterization of the system and helps in understanding the relations between various parameters of the system, before the system is developed. The capability of an Artificial Neural Network (ANN) to model the complex relations between a set of inputs and outputs is exploited to model the motional resistance and resonance frequency for a contour mode disk resonator. The solution was to develop a multilayer feed forward neural network. The data set required to train the ANN is obtained by developing an electrical equivalent model and through the MEMS simulation software Coventorware. The network is trained using a Levenberg Marquardt algorithm. The number of hidden layers and the number of neurons in each hidden layer is optimized using a genetic algorithm. The ANN model developed an efficient model of the motional resistance and resonance frequency of the disk resonator. The ANN output is compared with the output of an electrical equivalent model and a reported fabricated structure.

scholarly journals An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets

2019 ◽  
Vol 10 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Meryeme Boumahdi ◽  
Chaker El Amrani ◽  
Siegfried Denys
Keyword(s):  
Neural Network ◽  
Uv Light ◽  
Feed Forward Neural Network ◽  
Street Canyons ◽  
Purification Method ◽  
Anthropogenic Pollutants ◽  
Urban Streets ◽  
High Purification ◽  
Neural Network Algorithm ◽  
Artificial Neural Network Ann

In the present work, multiphysics modeling was used to investigate the feasibility of a photocatalysis-based outdoor air purifying solution that could be used in high polluted streets, especially street canyons. The article focuses on the use of a semi-active photocatalysis in the surfaces of the street as a solution to remove anthropogenic pollutants from the air. The solution is based on lamellae arranged horizontally on the wall of the street, coated with a photocatalyst (TiO2), lightened with UV light, with a dimension of 8 cm × 48 cm × 1 m. Fans were used in the system to create airflow. A high purification percentage was obtained. An artificial neural network (ANN) was used to predict the optimal purification method based on previous simulations, to design purification strategies considering the energy cost. The ANN was used to forecast the amount of purified with a feed-forward neural network and a backpropagation algorithm to train the model.

PREDICTION OF POTENTIAL HIT SONG AND MUSICAL GENRE USING ARTIFICIAL NEURAL NETWORKS

2009 ◽  
Vol 20 (11) ◽  
pp. 1697-1718 ◽  
Author(s):  
CHRISTOPHER MONTEROLA ◽  
CHERYL ABUNDO ◽  
JERIC TUGAFF ◽  
LORCEL ERICKA VENTURINA
Keyword(s):  
Neural Network ◽  
Feed Forward Neural Network ◽  
Linear Discriminant ◽  
The Public ◽  
Musical Genre ◽  
Alternative Rock ◽  
Discriminating Power ◽  
Average Accuracy ◽  
Classification And Regression ◽  
Size Criterion

Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of Φ NN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, Φ LDA = 61%) and classification and regression trees (CART, Φ CART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, Φ PCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*Φ PCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

A Review of Neural Network Application for Fault Diagnosis of NPP

2009 ◽  
Author(s):  
He Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Layer Structure ◽  
Engineering Application ◽  
Model Performance ◽  
Training Sample ◽  
Feed Forward Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Artificial Neural Network (ANN) with its self-learning capabilities, nonlinear mapping ability and generalization ability, has been widely applied for fault diagnosis of complex system like Nuclear Power Plant (NPP). In this paper, an overview of the application of supervised multi-layer feed-forward neural network for fault diagnosis of NPP is presented, including the following aspects: the acquisition of the training sample data, the determination of appropriate input and output data, the choice of hidden layer structure and the evaluation of network model performance. Finally, a number of key issues about the engineering application of neural network fault diagnosis in practice were discussed.

scholarly journals IN SILICO MODEL QSPR FOR PREDICTION OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES

2018 ◽  
Vol 127 (1A) ◽  
pp. 67
Author(s):  
Nguyen Minh Quang ◽  
Tran Xuan Mau ◽  
Pham Van Tat ◽  
Tran Nguyen Minh An ◽  
Vo Thanh Cong
Keyword(s):  
Neural Network ◽  
Stability Constants ◽  
In Silico ◽  
Metal Ion ◽  
Test Group ◽  
Structure And Property ◽  
The Neural Network ◽  
Complex Solutions ◽  
Artificial Neural Network Ann ◽  
The Stability

In the present work, the stability constants logb<sub>11</sub> and the concentration of metal ion and thiosemicarbazone in complex solutions were determined by using <em>in silico</em> models. The 2D, 3D, physicochemical and quantum descriptors of complexes were generated from the molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The quantitative structure and property relationships (QSPRs) were constructed by using the ordinary linear regression (OLR) and artificial neural network (ANN). The best linear model QSPR<sub>OLR</sub> (with <em>k</em> of 6) involved descriptors k0, core-core repulsion, xp5, xch5, valence, and SHHBd. The quality of model QSPR<sub>OLR</sub> had the statistical values: <em>R</em><sup>2</sup><sub>train</sub> = 0.898, <em>R</em><sup>2</sup><sub>adj</sub> = 0.889, <em>Q</em><sup>2</sup><em><sub>LOO</sub></em> = 0.846, MSE = 1.136, and <em>F<sub>stat</sub></em> = 91.348. The neural network model QSPR<sub>ANN</sub> with architecture I(6)-HL(6)-O(1) had the statistical values: <em>R</em><sup>2</sup><em><sub>train</sub></em> = 0.9768, and <em>Q</em><sup>2</sup><em><sub>LOO</sub></em> = 0.8687. The predictability of QSPR models for complexes of the test group turned out to be in good agreement with those from the experimental data in the literature.

scholarly journals The Consolidated Mathews Stability Graph for Open Stope Design

2021 ◽  
Author(s):  
Ali Mortazavi ◽  
Bakytzhan Osserbay
Keyword(s):  
Probabilistic Approach ◽  
Primary Objective ◽  
Stability Number ◽  
Stability Chart ◽  
The Stability ◽  
Extended Graph ◽  
Stability Graph ◽  
Stope Design

Abstract The stability graph method of stope design is one of the most widely used methods of stability assessments of stopes in underground polymetallic mines. The primary objective of this work is to introduce a new stability chart, which includes all relevant case histories, and to exclude parameters with uncertainties in the determination of stability number. The modified stability number was used to achieve this goal, and the Extended Mathews database was recalculated and compared with the new stability graph. In this study, a new refined Consolidated stability graph was developed by excluding the entry mining methods data from the Extended graph data, and only the non-entry methods data was used. The applicability of the proposed Consolidated stability chart was demonstrated by an open stope example. The stability for each stope surface was evaluated by a probabilistic approach employing a logistic regression model and the developed Consolidated stability chart. Comparing the stability analysis results with that of other published works of the same example shows that the determined Consolidated chart, in which the entry-method data is excluded, produces a more conservative and safer design. In conclusion, the size and quality of the dataset dictate the reliability of this approach.

scholarly journals Soft Computing Research For Weather Prediction Using Multilayer Architecture

2019 ◽  
Vol 8 (6) ◽  
pp. 3779-3783
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Weather Prediction ◽  
Series Data ◽  
Feed Forward Neural Network ◽  
Rainfall Prediction ◽  
Agriculture Sector ◽  
Precise Prediction ◽  
Multilayer Architecture ◽  
Artificial Neural Network Ann

Rainfall prediction is helpful for the agriculture sector. Early prediction of drought and torrent situations is achieved through time series data. For the precise prediction, Artificial Neural Network(ANN) technique is used. The rainy dataset is tested using Feed Forward Neural Network(FFNN). The performance of this model is evaluated using Mean Square Error(MSE) and Magnitude of Relative Error(MRE). Better performance achieved when compared with other data mining techniques.

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