scholarly journals Stability Analysis of Fluid-Conveying Beams using Artificial Intelligence

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Theddeus T Akano ◽  
Olumuyiwa S Asaolu
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Inference System ◽  
Fluid Field ◽  
Neuro Fuzzy ◽  
Pipes Conveying Fluid ◽  
The Stability ◽  
Stability Results ◽  
Conveying Fluid

This paper employs artificial intelligence in predicting the stability of pipes conveying fluid. Field data was collected for different pipe structures and usage. Adaptive Neuro-Fuzzy Inference System (ANFIS) model is implemented to predict the stability of the pipe using the fundamental natural frequency at different flow velocities as the index of stability. Results reveal that the neuro-fuzzy model compares relatively well with the conventional finite element method. It was also established that a pipe conveying fluid is most stable when the pipe is clamped at both ends but least stable when it is a cantilever.

Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models 

2021 ◽  
Author(s):  
Sonal Bindal
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Fuzzy Inference ◽  
Spatial Scales ◽  
Fuzzy Model ◽  
Fuzzy Regression ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Predictive Algorithms

<p>In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R<sup>2</sup>), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.</p>

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

2012 ◽  
Vol 1 (2) ◽  
pp. 44-59 ◽  
Author(s):  
M. S. Abdel Aziz ◽  
M. A. Moustafa Hassan ◽  
E. A. El-Zahab
Keyword(s):  
Artificial Intelligence ◽  
Distribution System ◽  
Fault Location ◽  
Fuzzy Inference ◽  
Distribution Networks ◽  
Inference System ◽  
New Approach ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Faults

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

Research of Faulty Sensors Data Reconstructed for Liquid-Propellant Rocket Engines

2012 ◽  
Vol 229-231 ◽  
pp. 1449-1453 ◽  
Author(s):  
Yan Jun Li ◽  
Xiao Hui Peng ◽  
Yu Qiang Cheng ◽  
Jian Jun Wu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rocket Engine ◽  
Fuzzy Model ◽  
Rocket Engines ◽  
Liquid Propellant ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Faulty Sensors ◽  
Propellant Rocket

In this paper, the data of faulty sensors reconstruct algorithm of liquid-propellant rocket engine is developed based on adaptive neuro-fuzzy inference system. First, the input parameters selected for method is according to regularity criterion and the relationships between each parameter; second, adaptive neuro-fuzzy inference system is train by normal test, finally, the fuzzy mode is validated by normal data and the data of faulty sensor is reconstructed. The results indicate that this algorithm can reconstruct the data of faulty sensors accurately and show that the fuzzy model approach has good performance in faulty sensors data reconstruct for LRE.

scholarly journals A NEURO FUZZY MODEL FOR THE INVESTIGATION OF DETERIORATION OF METALLIC PIPE CONVEYING FLUID UNDER DIFFERENT PIPE BURIAL DEPTH, SOIL TYPES AND PROPERTIES

2016 ◽  
Vol 36 (1) ◽  
pp. 72-79
Author(s):  
TT Akano ◽  
OA Fakindele ◽  
HE Mgbemere ◽  
JC Amechi
Keyword(s):  
Soil Properties ◽  
Membership Function ◽  
Fuzzy Model ◽  
Soil Types ◽  
Burial Depth ◽  
Pipe Conveying Fluid ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Conveying Fluid

Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of establishing corrosion rate criterion as a function of pipe burial depth, soil types, and properties for the prediction of deterioration of metallic pipe conveying fluid. The proposed model includes a fuzzy model and the artificial neural network (ANN) to determine soil corrosivity potential (CoP) based on soil properties. The combination contains the data of linguistic variables characterising various soil properties, and learning capability of the system that constructs relationships among those soil properties and CoP. Subsequently, the artificial neuro-fuzzy inference system (ANFIS) maps each element of its input membership function to an output membership function between 0 and 1 to determine the deterioration rate (CoP) of metallic fluid-conveying-pipe. Field data from buried fluid pipes were examined to illustrate the application of the proposed model. The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of buried fluid pipes more than any other considered factor. http://dx.doi.org/10.4314/njt.v36i1.10

scholarly journals Modeling of the Safety Climate and the Cultural Attitudes to Predict Unsafe Behaviors Using the Neuro-Fuzzy Inference System (ANFIS)

2020 ◽  
Author(s):  
Reza Pourbabaki ◽  
Zahra Beigzadeh ◽  
Behnam Haghshenas ◽  
Ali Karimi ◽  
Zahra Alaei ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Safety Climate ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Cross Sectional ◽  
Inference System ◽  
Cultural Attitudes ◽  
Neuro Fuzzy ◽  
Unsafe Behavior ◽  
Unsafe Behaviors

Background: Unsafe behavior in industries can be due to different factors. The aim of this study was to predict and model unsafe behavior using a safety atmosphere and cultural attitudes questionnaires. Methods: This study was a descriptive-analytic and cross-sectional examination that analyzed the data and predicted the unsafe behaviors of 90 construction workers using Neuro-Fuzzy Inference System (ANFIS) in MATLAB R2016a software. Results: In this study, the model of the safety atmosphere - unsafe behavior and the model of the cultural attitudes - unsafe behavior had the regression coefficients of 0.93373 and 0.9234, respectively. It showed that each of the parameters has a close relationship to the rate of the unsafe behavior. In this regard, a combination of the safety atmosphere and safety attitude parameters for the estimation of the unsafe behaviors achieved the better results with a regression coefficient of 0.9453 which indicates the direct effect of both parameters simultaneously on unsafe behavior. Conclusion: Based on the findings, it can be concluded that the neuro-fuzzy model can be used as an appropriate tool for predicting unsafe behavior in the industries.

scholarly journals Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Lu Minh Le ◽  
Hai-Bang Ly ◽  
Binh Thai Pham ◽  
Vuong Minh Le ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Buckling Load ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Steel Columns ◽  
Neuro Fuzzy ◽  
Hybrid Artificial Intelligence

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

scholarly journals Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3110
Author(s):  
Konstantinos V. Blazakis ◽  
Theodoros N. Kapetanakis ◽  
George S. Stavrakakis
Keyword(s):  
Artificial Intelligence ◽  
Electric Power ◽  
Power Distribution ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Electrical Power System ◽  
Inference System ◽  
Neuro Fuzzy

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

Air Pollutants and Meteorological Parameters Influence on PM2.5 Forecasting and Performance Assessment of the Developed Artificial Intelligence-Based Forecasting Model

2017 ◽  
Vol 68 (4) ◽  
pp. 864-868
Author(s):  
Marian Popescu ◽  
Sanda Florentina Mihalache ◽  
Mihaela Oprea
Keyword(s):  
Artificial Intelligence ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Fuzzy Inference ◽  
Computational Effort ◽  
Forecasting Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Performance ◽  
The Comparative Study

Particulate matter with an aerodynamic diameter lower than 2.5 �m (PM2.5) is one of the most important air pollutants. Current regulations impose measuring and limiting its concentrations. Thus, it is necessary to develop forecasting models programs that can inform the population about possible pollution episodes. This paper emphasizes the correlations between PM2.5 and other pollutants, and meteorological parameters. From these, nitrogen dioxide and temperature showed have the best correlations with PM2.5 and have been selected as inputs for the proposed forecasting model besides four PM2.5 concentrations (the values from current hour to three hours ago), the output of the model being the prediction of the next hour PM2.5 concentration. Two methods from artificial intelligence were used to build the forecasting model, namely adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN). The comparative study between these methods showed that the model which uses ANN have better results in terms of statistical indicators and computational effort.

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