scholarly journals Coordinate transformation parameters in Nepal by using neural network and SVD methods

2019 ◽  
Vol 9 (1) ◽  
pp. 22-28
Author(s):  
Kutubuddin Ansari ◽  
Prabin Gyawali ◽  
Prachand Man Pradhan ◽  
Kwan-Dong Park
Keyword(s):  
Neural Network ◽  
Coordinate Transformation ◽  
Data Sets ◽  
Extension Model ◽  
Global Positioning ◽  
Coordinate Measurements ◽  
Transformation Parameters ◽  
Artificial Neural Network Ann ◽  
Value Decomposition ◽  
Svd Method

Abstract The present study computes B-W extension model (extended Bursa-Wolf model) coordinate transformation parameters from World Geodetic System 1984 (WGS-84) to the Everest datum namely Everest (1830) and Everest (1956) using records of coordinate measurements from Global Positioning System (GPS) observable across Nepal region. Synthetic or modeled coordinates were determined by using the Artificial Neural Network (ANN) and Singular Value Decomposition (SVD) methods. We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method. The comparative analysis of the ANN, as well as SVD methods, was done with the observed output following one way ANOVA test. The analysis showed that the null hypothesis for both datums were acceptable and suggesting all models statistically significantly equivalent to each other. The outcomes from this study would complement a relatively better understanding of the techniques for coordinate transformation and precise coordinate assignment while assimilating data sets from different resources.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

FEM-ANN Sequential Modelling of Laser Transmission Welding for Prediction of Weld Pool Dimensions

2019 ◽  
pp. 249-261
Author(s):  
Bappa Acherjee
Keyword(s):  
Neural Network ◽  
Weld Pool ◽  
Welding Process ◽  
Time Frame ◽  
Data Sets ◽  
Fe Model ◽  
Ann Model ◽  
Laser Transmission Welding ◽  
Scripting Language ◽  
Artificial Neural Network Ann

In this chapter, a sequential modeling approach has been applied for modeling of laser transmission welding process using finite element method (FEM) and artificial neural network (ANN) technique to predict the weld pool dimensions in a shorter time frame. The scripting language, APDL (ANSYS® Parametric Design Language), is used to develop the three-dimensional FE model. During preprocessing, all the major physical phenomena of laser transmission welding process are incorporated into the model physics. Based on the temperature field predicted by the model, the weld pool dimensions (i.e., weld width and weld penetration depth) are calculated. The weld dimensions predicted by the developed FE model are further used for training a neural network model. It is found from the results of test data sets that the developed ANN model can predict the outputs with significant accuracy and takes less prediction time, which in turn saves time, cost, and the efforts for performing experiments.

Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 221-225 ◽  
Author(s):  
Cheng Long Xu ◽  
Sheng Li Lv ◽  
Zhen Guo Wang ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Fatigue Life ◽  
Network Architecture ◽  
Fatigue Tests ◽  
Data Sets ◽  
Absolute Relative Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

scholarly journals LANL* V2.0: global modeling and validation

2011 ◽  
Vol 4 (1) ◽  
pp. 575-594
Author(s):  
J. Koller ◽  
S. Zaharia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Space Weather ◽  
Radiation Belt ◽  
Geosynchronous Orbit ◽  
Data Sets ◽  
Magnetic Field Model ◽  
Integration Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. We describe in this paper the new version of LANL*. Just like the previous version, this new version V2.0 of LANL* is an artificial neural network (ANN) for calculating the magnetic drift invariant, L*, that is used for modeling radiation belt dynamics and for other space weather applications. We have implemented the following enhancements in the new version: (1) we have removed the limitation to geosynchronous orbit and the model can now be used for any type of orbit. (2) The new version is based on the improved magnetic field model by Tsyganenko and Sitnov (2005) (TS05) instead of the older model by Tsyganenko et al. (2003). We have validated the model and compared our results to L* calculations with the TS05 model based on ephemerides for CRRES, Polar, GPS, a LANL geosynchronous satellite, and a virtual RBSP type orbit. We find that the neural network performs very well for all these orbits with an error typically Δ L* < 0.2 which corresponds to an error of 3% at geosynchronous orbit. This new LANL-V2.0 artificial neural network is orders of magnitudes faster than traditional numerical field line integration techniques with the TS05 model. It has applications to real-time radiation belt forecasting, analysis of data sets involving decades of satellite of observations, and other problems in space weather.

Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

1998 ◽  
Vol 1633 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Hossam Eldin Ali ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Network ◽  
Numerical Approach ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Ann Model ◽  
Neural Network Approach ◽  
Liquefaction Potential ◽  
Network Training ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

scholarly journals A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019 ◽  
Vol 5 (10) ◽  
pp. 2120-2130 ◽  
Author(s):  
Suraj Kumar ◽  
Thendiyath Roshni ◽  
Dar Himayoun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Discharge Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Emotional Neural Network ◽  
Selection Of

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

ENHANCING SOFTWARE RELIABILITY OF A COMPLEX SOFTWARE SYSTEM ARCHITECTURE USING ARTIFICIAL NEURAL-NETWORKS ENSEMBLE

2011 ◽  
Vol 18 (03) ◽  
pp. 271-284 ◽  
Author(s):  
PARMOD KUMAR KAPUR ◽  
V. S. SARMA YADAVALLI ◽  
SUNIL KUMAR KHATRI ◽  
MASHAALLAH BASIRZADEH
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Estimation ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Neural Networks Ensemble

Modeling of software reliability has gained lot of importance in recent years. Use of software-critical applications has led to tremendous increase in amount of work being carried out in software reliability growth modeling. Number of analytic software reliability growth models (SRGM) exists in literature. They are based on some assumptions; however, none of them works well across different environments. The current software reliability literature is inconclusive as to which models and techniques are best, and some researchers believe that each organization needs to try several approaches to determine what works best for them. Data-driven artificial neural-network (ANN) based models, on other side, provide better software reliability estimation. In this paper we present a new dimension to build an ensemble of different ANN to improve the accuracy of estimation for complex software architectures. Model has been validated on two data sets cited from the literature. Results show fair improvement in forecasting software reliability over individual neural-network based models.

scholarly journals Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

2019 ◽  
Vol 13 (1) ◽  
pp. 118-128 ◽  
Author(s):  
Oluwaseye Onikeku ◽  
Stanley M. Shitote ◽  
John Mwero ◽  
Adeola. A. Adedeji ◽  
Christopher Kanali
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Degrees Of Freedom ◽  
Industrial Wastes ◽  
P Value ◽  
Data Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Bamboo Leaf Ash ◽  
Experimental Values

Background: Agro industrial wastes such as Bamboo Leaf Ash (BLA) and Bagasse Ash (BA) need to be employed so as to replace cement in order to produce cheaper concrete, which, in turn, save the environment. Objective: This research focuses on the compressive strength and slump based on Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models for forecasting of compressive strength and slump value for concrete by blending BLA and BA as partial supplementary cement materials accordingly. Methods: Three-layer perceptron was constructed through R (nnet package). A sum total of eleven artificial neural networks were formulated using 214 data sets attained from 27 laboratory concrete mixtures performed. Results: The neural network model forecasted the compressive strength for training, testing and validation with predicted errors of 0.802 MPa and 1.380 MPa. The model over forecasted the compressive strength averagely by 0.644 MPa and 1.905 MPa. The forecasted compressive strength changed averagely by 2.328% and 3.946%. The average difference between the predicted and experimental values was 0.588 MPa and 1.050 MPa. The coefficients of determination were 0.961 and 0.905. The MLR model predicted the slump with predictive error values of 6.634 mm and 8.374 mm. The predicted slump deviated on average by 3.633% and 8.034%. The residual error was 3.075 on 12 degrees of freedom. The multiple R2 and adjusted R2 were 0.9336 and 0.9115. The P-value was found to be 5.639e-07. Conclusion: The results show that ANN and MLR are potent tools for forecasting the compressive strength and slump of concrete blending bamboo leaf ash and baggage ash. Hence, this contributes towards forecasting of the compressive strength and slump of BLA and BA blended concrete. They extends 28 days compressive strength usually found in the literature to 56 and 90 days compressive strength and there was a remarkable improvement as curing age increases. The slump of combined effect of blending BLA and BA at different percentage replacements was tested. In this study, we used BLA blended with BA to produce concrete which is an innovation.

Utilizing Artificial Neural Network for Real-Time Prediction of Differential Sticking Symptoms

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Dalila Gomes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Model Development ◽  
Data Sets ◽  
Proper Training ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN) approach. The model is designed to provide users, i.e., driller or monitoring specialist, a warning whenever a risk is identified. Since multi-step forecasting is used, the warning is given with enough time for the driller or monitoring specialist to evaluate which preventative action or intervention is necessary. The warnings are provided typically between 30 minutes and 4 hours ahead. The model validation includes the performance metrics and a confusion matrix. Practical cases with real-time wells are also provided. The ML model was proven robust and practical with our data sets, for both historical and live wells. The huge amount of data produced while drilling holds valuable information and when smartly fed into an Artificial Intelligence (AI) model, it can prevent NPT such as stuck pipe events as demonstrated in this paper.

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