Yao Yevenyo Ziggah
Other affiliations: China University of Geosciences (Wuhan)
Bio: Yao Yevenyo Ziggah is an academic researcher from University of Mines and Technology. The author has contributed to research in topics: Artificial neural network & Geodetic datum. The author has an hindex of 13, co-authored 66 publications receiving 458 citations. Previous affiliations of Yao Yevenyo Ziggah include China University of Geosciences (Wuhan).
TL;DR: The proposed GPR approach can predict ground vibration more accurately than the standard techniques presented in this study and has the highest correlation coefficient, variance accounted for, and the lowest values of the statistical error indicators applied.
Abstract: An attempt has been made to propose a novel prediction model based on the Gaussian process regression (GPR) approach. The proposed GPR was used to predict blast-induced ground vibration using 210 blasting events from an open pit mine in Ghana. Out of the 210 blasting data, 130 were used in the model development (training), whereas the remaining 80 were used to independently assess the performance of the GPR model. The formulated GPR model was compared with the other standard predictive techniques such as the generalised regression neural network, radial basis function neural network, back-propagation neural network, and four conventional ground vibration predictors (United State Bureau of Mines model, Langefors and Kihlstrom model, Ambraseys–Hendron model, and Indian Standard model). Comparatively, the statistical results revealed that the proposed GPR approach can predict ground vibration more accurately than the standard techniques presented in this study. The GPR had the highest correlation coefficient (R), variance accounted for, and the lowest values of the statistical error indicators (mean absolute error and root-mean-square error) applied. The superiority of GPR to the other methods is explained by the ability of the GPR to quantitatively model the noise patterns in the blasting data events adequately. The study will serve as a foundation for future research works in the mining industry where artificial intelligence technology is yet to be fully explored.
TL;DR: The statistical analysis revealed that the proposed DWT-PSO-RBFNN method performed better based on MAPE, MAD, and RMSE emphasizing its great potential.
TL;DR: This study proposes the Multivariate Adaptive Regression Splines (MARS) as a novel alternative technique to model and predict blast-induced ground vibration and revealed that the MARS produced the best performance and can successfully be used for the prediction of blast- induced ground vibration.
Abstract: Modelling and prediction of blast-induced ground vibration is a significant aspect of mining and civil engineering operations, as ground vibration has dire consequences on both the environment, min...
TL;DR: In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability.
Abstract: In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.
TL;DR: A new class of advanced artificial neural network known as brain inspired emotional neural network (BI-ENN) is proposed to predict AOp, which is known to be one of the most important environmental hazards of mining.
Abstract: Blasting is the live wire of mining and its operations, with air overpressure (AOp) recognised as an end product of blasting. AOp is known to be one of the most important environmental hazards of mining. Further research in this area of mining is required to help improve on safety of the working environment. Review of previous studies has shown that many empirical and artificial intelligence (AI) methods have been proposed as a forecasting model. As an alternative to the previous methods, this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network (BI-ENN) to predict AOp. The proposed BI-ENN approach is compared with two classical AOp predictors (generalised predictor and McKenzie formula) and three established AI methods of backpropagation neural network (BPNN), group method of data handling (GMDH), and support vector machine (SVM). From the analysis of the results, BI-ENN is the best by achieving the least RMSE, MAPE, NRMSE and highest R, VAF and PI values of 1.0941, 0.8339%, 0.1243%, 0.8249, 68.0512% and 1.2367 respectively and thus can be used for monitoring and controlling AOp.
01 Jun 2005
09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; firstname.lastname@example.org. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.
01 Oct 2015
TL;DR: In this article, the Extreme Learning Machine (ELM) was used to train a classifier for learning to solve problems in the real world, and the results showed that the classifier achieved good performance.
Abstract: 본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.
01 Jan 2015
TL;DR: Assurance requirements were included in the disposal regulations to compensate in a qualitative manner for the inherent uncertainties in projecting the behavior of natural and engineered components of the WIPP for many thousands of years.
Abstract: Assurance requirements were included in the disposal regulations to compensate in a qualitative manner for the inherent uncertainties in projecting the behavior of natural and engineered components of the WIPP for many thousands of years (50 FR 38072). Section 194.42 is one of the six assurance requirements in the Compliance Criteria. Section 194.42 specifically addresses requirements for monitoring the disposal system during preand post-closure operations. This requirement distinguishes between preand post-closure monitoring because of the differences in monitoring techniques used to access the repository during operations (pre closure) and after the repository has been backfilled and sealed (post-closure). The purpose of monitoring is to confirm that the repository is behaving as predicted.