Artificial Neural Network Estimation Research Articles

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Predictions on Flexible CdTe Solar Cell Performances by Artificial Neural Networks

CdTe solar cells on ultra-thin glass substrates are light and flexible. Flexible cells are widely preferred modules in technological fields. The flexibility of these cells enables them to cope with deformations. The efficiency of these has reached 19%. In this work, we used artificial neural network (ANN) method for the determination the performance of flexible CdTe solar cells despite bending and time. The performances of the solar cell before and after bending have been predicted. According to the results from the ANN calculations using the experimental data in the literature, MSE values of ANN estimates range from 0.06% to 0.28%.

Effect of Caesalpinia decapetala on the Dry Sliding Wear Behavior of Epoxy Composites

The present research investigates the wear characteristics of an epoxy composite reinforced with a novel Caesalpinia decapetala (CD) shell. The CD is available abundantly worldwide, especially in Ethiopia, particularly in East and West Oromia near West Harar. The composite specimens were processed in the open mould casting technique by varying the vol.% of CD in 10, 20, and 30. EDS is used to evaluate the important elements present in the CD. The density of composites increases with the increase in the content of CD, while the void content estimations reveal good control over the composite fabrication. The wear response of composites is investigated by varying the sliding distance and load and by maintaining a fixed velocity (5 m/s). At a 5 km slide distance and 50 N load, the 30 vol.% Caesalpinia decapetala composition depicts better wear resistance and friction coefficient than other compositions. Experimental results are used to envisage the ideal wear factors and to assess the influence of parameters over the two wear objectives, wear rate and CoF. The grey relational analysis- (GRA-) coupled artificial neural network (ANN) hybrid technique was employed for the prediction and validation. It has been observed that a trivial error of 0.49% amidst GRA and ANN estimation is observed.

Estimating Daily Temperatures over Andhra Pradesh, India, Using Artificial Neural Networks

In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)’s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD’s SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD’s SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region.

Research on the Methods of Predicting Compressor Characteristic Curve

Compressors are one of the three major components of gas turbines, and their characteristic curves are used to analyze off-design performance. How to infer the characteristic curve based on different data is an important research topic. In this paper, PG9351FA gas turbine is taken as the research object. Two methods, artificial neural network and parameter estimation, are used to predict its characteristic curve, and the prediction accuracy and application conditions of the two methods are discussed. This article compares the two methods from the perspectives of known speed characteristic curve regression and unknown speed characteristic curve inference, analyzes the impact of sample size and sample error on their inference results, and quantitatively analyzes the error through statistical methods such as calculating the mean square deviation of the data. The application scope and conditions of different methods are provided. The research results show that the method based on neural network to infer the characteristic curve has high accuracy and is suitable for the prediction of known and unknown speed characteristic curves under sufficient data, but not for the prediction of unknown side curves. The elliptic equation fitting method based on parameter estimation has a slightly lower accuracy in processing the nearly vertical compressor characteristic curve, but it can be used as an effective and reliable method to infer the compressor characteristic curve in the case of a small amount of data. The modulization method based on parameter estimation has high accuracy and is applicable to the estimation of complete characteristic curve from partial data of known characteristic curve. In this paper, the application scope and conditions of these two methods are determined, which can provide reference for engineering practice.

ANN-based estimation model for the preconstruction cost of pavement rehabilitation projects

To combat prevalent roadway aging, the California Department of Transportation (Caltrans) implements routine rehabilitation projects, which require cost estimation models for better management. Several models have been developed to estimate the construction costs of new highway projects; however, few have targeted the cost of pavement rehabilitation, and, of these, most have focused primarily on the construction phase. This study presents an artificial neural network (ANN) estimation model that predicts the cost of the preconstruction phases (predesign, design, and bidding) of pavement rehabilitation projects. The model was developed using 139 projects maintained by Caltrans over the past five years. Pareto principle and sensitivity analysis were used to identify five project items that contributed significantly to the model’s high estimation performance, which was demonstrated by a coefficient of multiple determination value (R 2) of 76.9%. Thus, the proposed model will allow Caltrans to properly estimate its budget and manage its performance during the preconstruction phases of pavement rehabilitation projects. Moreover, it overcomes many limitations of conventional preconstruction cost estimation models and will allow Caltrans to better manage up to $80B that may be spent on its preconstruction activities. Finally, the model provides a benchmarking tool for tracking and controlling expenses.

Life cycle environmental and cost assessment of prefabricated components manufacture

Prefabrication is regarded as a pivot for realizing sustainable building construction. Nevertheless, the sustainable manufacture level of prefabricated components remains unclear. To fill this gap, this study analyzed the environmental impact and optimization potential of prefabricated components via a life cycle assessment (LCA) method. Additionally, social and economic cost were assessed through LCA-based life cycle costing method to reveal the sustainability of prefabricated components. Results indicated that prefabrication rates of 50% and 30% could reduce the potential global warming impact by 31.78% and 13.94%, respectively, compared with the conventional construction method. However, prefabricated components production in China generated up to $ 4.35 × 109 social cost, with critical optimization areas focused on the provinces of Shandong and Hubei. By using artificial neural network estimation, 30% replacement of concrete by demolished concrete lumps could reduce potential fossil depletion and social cost by 10%, decreasing 5.08 × 109 kg CO2 eq potential global warming for China. Meanwhile, partial replacement of fuels with refuse-derived fuels and improved steel scrap utilization could contribute to further improving environmental performance.

Estimation of wheel slip in 2WD mode for an agricultural tractor during plowing operation using an artificial neural network

As artificial neural networks (ANNs) have been shown to be precise and reliable in supporting the field of artificial intelligence technology, agricultural scientists have focused on employing ANN for agricultural applications. The ANN can be an effective alternative for evaluating agricultural operations. The intended aim of this investigation was to employ both ANN and multiple linear regression (MLR) to develop a model for determining the rear wheel slip of an agricultural tractor in two-wheel drive (2WD) mode during plowing operations. The output parameter of the models was tractor rear wheel slip. The training data were collected from filed experiments using chisel, moldboard, and disk plows. The plows were operated under different conditions of soil texture, plowing depth, soil moisture content, and plowing speed. All data were acquired during field experiments in two soil textures (clay and clay loam textures). The training dataset was comprised of 319 data points, while 65 data points were employed to test both ANN and MLR models estimation capability. The ANN model with a backpropagation training algorithm was created using the commercial Qnet2000 software by changing its topology and related parameters. The best ANN model possessed a topology of 7-20-1. The estimated tractor rear wheel slip using the testing dataset displayed strong agreement with measured tractor rear wheel slip with the coefficient of determination (R2) value of 0.9977. The results definitely illustrated that the ANN model was capable of defining the correlation between the inputs and rear wheel slip. The outcomes suggest that the established ANN model is trustworthy in predicting the tractor rear wheel slip for an agricultural tractor in 2WD mode during the tillage process compared to MLR models. This study provides a useful tool for management of tillage implements during field operations.

A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization.

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important. We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators. The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871. The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

Efficient estimation of average derivatives in NPIV models: Simulation comparisons of neural network estimators

Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an orthogonalized plug-in with optimally-weighted sieve minimum distance (OP-OSMD) procedure and a sieve efficient score (ES) procedure. Both estimators for WAD use ANN sieves to approximate the unknown NPIV function and are n-asymptotically normal and first-order equivalent. We provide a detailed practitioner’s recipe for implementing both efficient procedures. We compare their finite-sample performances in various simulation designs that involve smooth NPIV function of up to 13 continuous covariates, different nonlinearities and covariate correlations. Some Monte Carlo findings include: (1) tuning and optimization are more delicate in ANN estimation; (2) given proper tuning, both ANN estimators with various architectures can perform well; (3) easier to tune ANN OP-OSMD estimators than ANN ES estimators; (4) stable inferences are more difficult to achieve with ANN (than spline) estimators; (5) there are gaps between current implementations and approximation theories. Finally, we apply ANN NPIV to estimate average partial derivatives in two empirical demand examples with multivariate covariates.

The Catastrophe of Corruption in the Sustainability of Foreign aid: A Prediction of Artificial Neural Network Method in Indonesia

The rising corruption levels in Indonesia are becoming a cause for concern and raise doubts about their impact on the stability of foreign aid in the country. Therefore, this study aims to predict the long-term viability of foreign aid in Indonesia based on international perceptions of corruption and corruption cases in the country. Data were obtained from World Governance Indicators, the Indonesian Ministry of Finance, and the World Bank, and the study used a backpropagation artificial neural network (ANN) for prediction. The results from ANN are compared to linear models and vector autoregression (VAR). The finding shows that ANN outperforms the other models based on the coefficient of determination and MSE values. Furthermore, it highlights the strong relationship between corruption perception and foreign aid sustainability with an R-value of 0.991. According to the ANN estimation, gratification has a significant impact on foreign aid. In response to this finding, the study recommends the Indonesian government take action to combat corruption in maintaining the international trust and ensuring the stability of foreign aid.

Estimation of Travel Cost between Geographic Coordinates Using Artificial Neural Network: Potential Application in Vehicle Routing Problems

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number of locations, restricting large-sized VRPs from being solved in a reasonable amount of time. The time needed to obtain a cost matrix is expensive when routing engines are used, which solve shortest path problems in the back end. In fact, details on the shortest path are redundant; only distance or time values are necessary for VRPs. In this study, an artificial neural network (ANN) that receives two geo-coordinates as input and provides estimated cost (distance and time) as output is trained. The trained ANN model was able to estimate with a mean absolute percentage error of 7.68%, surpassing the quality of 13.2% with a simple regression model on Euclidean distance. The possibility of using a trained model in VRPs is examined with different implementation scenarios. The experimental results with VRPs confirm that using ANN estimation instead of Euclidean distance produces a better solution, which is verified to be statistically significant. The results also suggest that an ANN can be a better choice than routing engines when the trade-off between response time and solution quality is considered.

Improved estimation of rail–wheel contact forces from instrumented wheel-set data through higher harmonic cancellation and a back-propagation neural network scheme

Instrumented wheelsets, equipped with numbers of strain gauges, are widely used to estimate the rail–wheel contact parameters. The accuracy in estimation of rail–wheel contact parameters from the measured strain signals depends on the layout of the strain gauges and correlation techniques adopted for processing measured signals. Harmonic components estimation is the most widely used conventional correlation technique. However, its accuracy in estimation of rail–wheel contact parameters is limited to the order of 80%–90%. This study proposes a signal modification algorithm, initially utilizing cancellation of next significant higher harmonics than those considered in the existing algorithms and then further, a novel Artificial Neural Network architecture for the same purpose.The signal modification approach is based on the realization that the seventh harmonic has significant contribution on the strain signals. The proposed algorithm is aimed at estimating and cancelling the effects of the seventh harmonic in strain signals. The accuracy thus improves to 91% in vertical contact force estimates, while the accuracy in estimation of lateral contact force and lateral contact position are improved to 97% and 95%, respectively. Due to the highly non-linear nature of the rail–wheel contact parameters, an optimized nonlinear mapping is required for further improvement in the accuracy.The viability of using artificial neural networks in estimation of rail–wheel contact parameters have been explored earlier. The proposed architectures are complicated and require extensive manual optimization of tuning parameters. A simple back-propagation artificial neural network architecture, using Bayesian regularization training function with Levenberg–Marquardt optimization technique is proposed in this study. The proposed neural network consists of 18 measured strain signals as input, three hidden layers with 20, 6 and 3 neurons, respectively, and vertical force, lateral force and lateral contact position as three outputs. Accuracies of the order of 99%, 99% and 96% are achieved in the estimation of vertical contact force, lateral contact force and lateral contact position respectively. The input and training data is generated from the multi-body dynamics and finite element simulations.

Artificial Neural Networking Magnification for Heat Transfer Coefficient in Convective Non-Newtonian Fluid with Thermal Radiations and Heat Generation Effects

In this study, the Casson fluid flow through an inclined, stretching cylindrical surface is considered. The flow field is manifested with pertinent physical effects, namely heat generation, viscous dissipation, thermal radiations, stagnation point flow, variable thermal conductivity, a magnetic field, and mixed convection. In addition, the flow field is formulated mathematically. The shooting scheme is used to obtain the numerical data of the heat transfer coefficient at the cylindrical surface. Further, for comparative analysis, three different thermal flow regimes are considered. In order to obtain a better estimation of the heat transfer coefficient, three corresponding artificial neural networks (ANN) models were constructed by utilizing Tan-Sig and Purelin transfer functions. It was observed that the heat transfer rate exhibits an inciting nature for the Eckert and Prandtl numbers, curvature, and heat generation parameters, while the Casson fluid parameter, temperature-dependent thermal conductivity, and radiation parameter behave oppositely. The present ANN estimation will be helpful for studies related to thermal energy storage that have Nusselt number involvements.

A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay

Two models were developed to simulate energy flows in a mangrove area of A. germinans and A. bicolor in the Bay of Panama, considering the importance of these areas in CO2 fixation. The first model (black box) consisted of the use of artificial neural networks for estimation, using meteorological data and energy flows calculated by the Eddy Covariance method for model training. The second model (grey box) used the RC circuit theory, considering a non-steady state model for the flow of water from the ground to the atmosphere. A methodology was developed to reduce the uncertainty of the data collected by the sensors in the field. The black box model managed to predict the fluxes of latent heat (R2 > 0.91), sensible heat (R2 > 0.86), CO2 (R2 > 0.88), and the potential of water in the air (R2 > 0.88) satisfactorily, while the grey box model generated R2 values of 0.43 and 0.37, indicating that it requires further analysis regarding the structuring of the equations and parameters used. The application of the methodology to filter the data improved the effectiveness of the model during the predictions, reducing the computational capacity necessary for the resolution of the iterations.

Stem Taper Estimation Using Artificial Neural Networks for Nothofagus Trees in Natural Forest

The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak’s model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.

Determination of Temperature Distribution of Rohri, Sindh using Artificial Neural Network and Regression Analysis

As time passes, the world is facing the problem of global warming, which results in a rise in average daily temperature. Proper knowledge of temperature distribution and future prediction may help to cope with the situation in the near future. Climate forecasting has gone through various faces; in the early days’ people used to predict the behavior qualitatively. Now environmental scientists have developed a quantitative method for forest climate behavior with certain uncertainties. Empirical models have been developed based on regression analysis to estimate temperature distribution. Two models, linear and non linear, use dew point temperature and relative humidity as independent variables. In addition to regression analysis, Artificial Neural Network (ANN) has been utilized to predict the average daily temperatures of Rohri Sindh, a city in Pakistan in the Sindh province. Both empirical models and ANN estimates are in good agreement with the known values of average daily temperatures.

Neural network predictions of (n,2n) reaction cross-sections at 14.6 MeV incident neutron energy

In this study, we have estimated the (n,2n) reaction cross-section for 14.6 MeV incident neutron energy by using the artificial neural network (ANN) method. We have also predicted the reaction cross-sections whose experimental data are not available in the literature. For the construction of the present ANN, available experimental data in the literature has been borrowed. The ANN estimations have been compared with the available experimental data and the results from a theoretical calculation and the two commonly used computer codes. According to the results that the ANN results are in good agreement with the experimental data than the codes and this shows that the method can be a powerful tool for the estimation of cross-section data for the neutron-induced reactions. Considering the predictions of the ANN of the cross-sections whose experimental data are not available in the literature, it is seen that they are in line with the trend of the experimental data, but far from the results given by the theoretical calculations and two computer codes.

Parameter estimation of uncertain differential equation by implementing an optimized artificial neural network

This study suggests a novel method for estimation of uncertain stock model parameters driven by Liu process. The proposed method decomposes the parameter estimation problem into two sub-problems: the first sub-problem implements an optimized artificial neural network based on the observed data, and the next sub-problem estimates the uncertain model parameters according to the optimized artificial neural network. We apply Nelder–Mead algorithm to optimize the artificial neural network and parameter estimation problem. The main supremacy of the presented method is that the estimation problem is independent of time intervals among observations and can be used to model future data. Providing a comparative method shows that the proposed approach can be effective for non-linear problems in which the artificial neural network structures perform well.