scholarly journals A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Metals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 1198 ◽  
Author(s):  
Saldaña ◽  
González ◽  
Jeldres ◽  
Villegas ◽  
Castillo ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Analytical Models ◽  
Error Statistics ◽  
Probabilistic Networks ◽  
Independent Variables ◽  
Conditional Dependence ◽  
Normality Test ◽  
Operational Variables ◽  
Input Variables ◽  
Copper Heap Leaching

Multivariate analytical models are quite successful in explaining one or more response variables, based on one or more independent variables. However, they do not reflect the connections of conditional dependence between the variables that explain the model. Otherwise, due to their qualitative and quantitative nature, Bayesian networks allow us to easily visualize the probabilistic relationships between variables of interest, as well as make inferences as a prediction of specific evidence (partial or impartial), diagnosis and decision-making. The current work develops stochastic modeling of the leaching phase in piles by generating a Bayesian network that describes the ore recovery with independent variables, after analyzing the uncertainty of the response to the sensitization of the input variables. These models allow us to recognize the relations of dependence and causality between the sampled variables and can estimate the output against the lack of evidence. The network setting shows that the variables that have the most significant impact on recovery are the time, the heap height and the superficial velocity of the leaching flow, while the validation is given by the low measurements of the error statistics and the normality test of residuals. Finally, probabilistic networks are unique tools to determine and internalize the risk or uncertainty present in the input variables, due to their ability to generate estimates of recovery based upon partial knowledge of the operational variables.

scholarly journals An Invariance Property of Predictors in Kernel-Induced Hypothesis Spaces

2006 ◽  
Vol 18 (4) ◽  
pp. 749-759 ◽  
Author(s):  
Nicola Ancona ◽  
Sebastiano Stramaglia
Keyword(s):  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sufficient Conditions ◽  
Kernel Functions ◽  
Invariance Property ◽  
Risk Minimization ◽  
Independent Variables ◽  
Input Variables ◽  
New Variables

We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel-induced hypothesis spaces enjoy this property. We present sufficient conditions ensuring that the risk minimizer does not change and show that they hold for inhomogeneous polynomial and gaussian radial basis function kernels. We also provide examples of kernel-induced hypothesis spaces whose risk minimizer changes if independent variables are added as input.

Bayesian networks: Theory, applications and sensitivity issues

2017 ◽  
Vol 01 (01) ◽  
pp. 1630014 ◽  
Author(s):  
Ron S. Kenett
Keyword(s):  
Bayesian Networks ◽  
Expert Opinion ◽  
Data Science ◽  
Information Quality ◽  
Theoretical Background ◽  
Hospital Environment ◽  
Statistical Tool ◽  
Data Set ◽  
Operational Variables ◽  
High Level

This chapter is about an important tool in the data science workbench, Bayesian networks (BNs). Data science is about generating information from a given data set using applications of statistical methods. The quality of the information derived from data analysis is dependent on various dimensions, including the communication of results, the ability to translate results into actionable tasks and the capability to integrate various data sources [R. S. Kenett and G. Shmueli, On information quality, J. R. Stat. Soc. A 177(1), 3 (2014).] This paper demonstrates, with three examples, how the application of BNs provides a high level of information quality. It expands the treatment of BNs as a statistical tool and provides a wider scope of statistical analysis that matches current trends in data science. For more examples on deriving high information quality with BNs see [R. S. Kenett and G. Shmueli, Information Quality: The Potential of Data and Analytics to Generate Knowledge (John Wiley and Sons, 2016), www.wiley.com/go/information_quality.] The three examples used in the chapter are complementary in scope. The first example is based on expert opinion assessments of risks in the operation of health care monitoring systems in a hospital environment. The second example is from the monitoring of an open source community and is a data rich application that combines expert opinion, social network analysis and continuous operational variables. The third example is totally data driven and is based on an extensive customer satisfaction survey of airline customers. The first section is an introduction to BNs, Sec. 2 provides a theoretical background on BN. Examples are provided in Sec. 3. Section 4 discusses sensitivity analysis of BNs, Sec. 5 lists a range of software applications implementing BNs. Section 6 concludes the chapter.

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2021 ◽  
Author(s):  
Miguel Abambres ◽  
He J
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.</p>

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Jun He
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.

scholarly journals Potential of neural networks for maximum displacement predictions in railway beams on frictionally damped foundations

2019 ◽  
Author(s):  
Miguel Abambres ◽  
Rita Corrêa ◽  
António Pinto da Costa ◽  
Fernando Simões
Keyword(s):  
Neural Networks ◽  
Computing Time ◽  
Maximum Error ◽  
Moving Loads ◽  
Maximum Displacement ◽  
Know How ◽  
Independent Variables ◽  
Data Points ◽  
Input Variables ◽  
Two Independent Variables

Since the use of finite element (FE) simulations for the dynamic analysis of railway beams on frictionally damped foundations are (i) very time consuming, and (ii) require advanced know-how and software that go beyond the available resources of typical civil engineering firms, this paper aims to demonstrate the potential of Artificial Neural Networks (ANN) to effectively predict the maximum displacements and the critical velocity in railway beams under moving loads. Four ANN-based models are proposed, one per load velocity range ([50, 175] ∪ [250, 300] m/s; ]175, 250[ m/s) and per displacement type (upward or downward). Each model is function of two independent variables, a frictional parameter and the load velocity. Among all models and the 663 data points used, a maximum error of 5.4 % was obtained when comparing the ANN- and FE-based solutions. Whereas the latter involves an average computing time per data point of thousands of seconds, the former does not even need a millisecond. This study was an important step towards the development of more versatile (i.e., including other types of input variables) ANN-based models for the same type of problem.

scholarly journals Potential of neural networks for maximum displacement predictions in railway beams on frictionally damped foundations

2020 ◽  
Author(s):  
Abambres M ◽  
Rita Corrêa ◽  
AP Costa ◽  
F Simões
Keyword(s):  
Neural Networks ◽  
Computing Time ◽  
Maximum Error ◽  
Moving Loads ◽  
Maximum Displacement ◽  
Know How ◽  
Independent Variables ◽  
Data Points ◽  
Input Variables ◽  
Two Independent Variables

<p>Since the use of finite element (FE) simulations for the dynamic analysis of railway beams on frictionally damped foundations are (i) very time consuming, and (ii) require advanced know-how and software that go beyond the available resources of typical civil engineering firms, this paper aims to demonstrate the potential of Artificial Neural Networks (ANN) to effectively predict the maximum displacements and the critical velocity in railway beams under moving loads. Four ANN-based models are proposed, one per load velocity range ([50, 175] ∪ [250, 300] m/s; ]175, 250[ m/s) and per displacement type (upward or downward). Each model is function of two independent variables, a frictional parameter and the load velocity. Among all models and the 663 data points used, a maximum error of 5.4 % was obtained when comparing the ANN- and FE-based solutions. Whereas the latter involves an average computing time per data point of thousands of seconds, the former does not even need a millisecond. This study was an important step towards the development of more versatile (i.e., including other types of input variables) ANN-based models for the same type of problem. </p>

Comparison of Two Data Entry Methods

1965 ◽  
Vol 20 (2) ◽  
pp. 369-384 ◽  
Author(s):  
William K. Earl ◽  
James D. Goff
Keyword(s):  
Experimental Design ◽  
Data Entry ◽  
Relative Location ◽  
Relative Speed ◽  
Automatic Data ◽  
Material Density ◽  
Independent Variables ◽  
Different Types ◽  
Input Variables ◽  
Speed And Accuracy

The purpose of this experiment was to measure the effects of a number of display and input variables on the relative speed and accuracy of input performance when using point-in and type-in data entry methods for entering alphabetical material into automatic data processing machines. The factors tested in the experimental design were: types of arrangement of display material, density of material, different types of input tasks, typing ability, sex, and relative location of the keypunch device to the operator. The major finding of this study was that the point-in data entry method was a more accurate input technique than either the type-in or mixed point-in type-in data entry methods when measured under the effects of the independent variables.

scholarly journals Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite

2014 ◽  
Vol XL-4 ◽  
pp. 325-328
Author(s):  
B. Yang ◽  
X. Yu
Keyword(s):  
High Resolution ◽  
Bayesian Networks ◽  
Satellite Images ◽  
Texture Classification ◽  
Complex Model ◽  
Data Mining Technique ◽  
Probabilistic Networks ◽  
High Resolution Satellite Images ◽  
High Level

Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of High-resolution satellite images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. In the experiment, we choose GeoEye-1 satellite images. Experimental results demonstrate BAN outperform than NBC in the overall classification accuracy. Although it is time consuming, it will be an attractive and effective method in the future.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP PEMILIHAN METODE DEPRESIASI UNTUK AKTIVA TETAP PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK JAKARTA

2019 ◽  
Vol 2 (2) ◽  
pp. 117
Author(s):  
Muhammad Nuryatno ◽  
Nazmel Nazir ◽  
Ramaditya Adinugraha
Keyword(s):  
Stock Exchange ◽  
Manufacturing Companies ◽  
Leverage Ratio ◽  
Nominal Scale ◽  
Independent Variables ◽  
Simultaneous Test ◽  
Normality Test ◽  
Method Selection ◽  
Selection For ◽  
Metric Scale

<em>The objective of this thesis is to identify the influence of firm's size, leverage ratio, and accounting ROA to the depreciation method selection for plant assets in manufacturing companies listed in Jakarta Stock Exchange. The dependent variabel in this research is depreciation method selection, which is measured by nominal scale, while, the independent variables consists firm's size, leverage ratio, and accounting ROA is measured by metric scale. The data in this research includes 55 manufacturing companies which were selected by using a purposive judgement sampling in the period of 2002 until 2005. The method used in this research are normality test, classic assumption, and hypotheses test by using logistic regression analysis. The result of this research is that at the alpha rate of 5%, each of the independent variables -including firm's size, leverage ratio, and ROA- do not have significant influences to the depreciation method selection for plant assets in the manufacturing companies listed in Jakarta Stock Exchange. Meanwhile, a simultaneous test performed to theese three independent variables doesn't show a result of significant influence to the depreciation method selection for plant assets in those companies neither.</em>

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