scholarly journals ARTIFICIAL NEURAL NETWORK AND STEPWISE APPROACH FOR PREDICTING TRACTIVE EFFICIENCY OF THE TRACTOR (CASE JX75T)

2019 ◽  
Vol 50 (4) ◽  
Author(s):  
Almaliki & et al.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Field Conditions ◽  
Silty Clay ◽  
Ann Model ◽  
Stepwise Approach ◽  
Artificial Neural ◽  
Tractive Efficiency ◽  
Cone Index

The aim of this study is to develop and predict models of tractive efficiency using the artificial neural network and stepwise approach. The tractive efficiency of tractor (CASE JX75T) was measured experimentally. Experiments were conducted in the site of Basrah University. Which had silty clay soil texture. The field conditions included effect of two level of cone index (550 and 980 kPa), two level of moisture content (8 and 21%), three forward speeds (0.54, 0.83 and 1.53 m/s) and four level of tillage depths (10, 15, 20 and 25 cm). The results illustrated that both developed models (stepwise approach and ANN technique) had acceptable performance for predicting tractive efficiency of tractor under various field conditions. However, ANN model outperformed stepwise model, where 4-7-1 topology showed the best power for predicting tractive efficiency with R-squared of 0.97 and MSE of 0.0074 with Levenberg-Marquardt training algorithm. The analysis of variance demonstrated that the studied parameters had single significant effect on tractive efficiency. The most parameter influential on tractive efficiency was tillage depth followed forward speed, cone index and moisture content.

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

Parallel Dynamic Artificial Neural Network for Temperature and Moisture Content Predictions in Microwave-Vacuum Dried Tomato Slices

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Poonpat Poonnoy ◽  
Ampawan Tansakul ◽  
Manjeet Chinnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Microwave Power ◽  
Vacuum Pressure ◽  
Ann Model ◽  
Essential Information ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

Temperature (T) and moisture content (MC) of non-homogenous food undergoing microwave-vacuum (MV) drying (MVD) are directly dependent on microwave power, vacuum pressure, and the product's physical properties. A two-hidden-layer Artificial Neural Network (ANN) model was developed in an earlier study to predict temperature and moisture content of the product at a given time based on the present state of product conditions and process control parameters. This approach either provided lowest error in temperature prediction or in moisture content prediction but not the lowest error in both the prediction parameters simultaneously. The main objective of this work was to improve the performance of the ANN model for temperature and moisture content predictions in MV dried samples. Experimental data obtained from MVD of tomato slices at different drying conditions was normalized and divided into two groups for training and validating. The parallel dynamic ANN model consisted of two double-hidden-layer feed-forward ANN models with varying node numbers (10, 20, and 30). These models were separately trained, simultaneously for moisture content as well as temperature, with the Levenberg-Marquardt algorithm. Inputs for the ANN models were magnetron on-off status, vacuum pressure, temperature, and moisture content at time `ti'. The previous temperature and moisture content data at time `ti-1, i-2, …, i-n' where n = 0, 10, 20, and 30 were also added to the input layer. Outputs from the ANN models were temperature and moisture content at time `ti+1'. The results indicated that the dynamic ANN model working in parallel with the previous temperature and moisture content data provided results that are more accurate and required less training time than those of ordinary ANN models. Model simulation may supply essential information regarding temperature and moisture content of non-homogenous foods corresponding to microwave power and vacuum pressure levels to the predictive control system. Therefore, improved drying efficiencies and thermal damage prevention may be achieved.

scholarly journals Drying Klunzinger's mullet fish Planiliza klunzingeri using Halogen Dryer and modeling the moisture content with artificial neural network

2020 ◽  
Vol 33 (1) ◽  
pp. 231-261
Author(s):  
Hassan H. Al-Rubaiy ◽  
, Sabah M. Al-Shatty ◽  
Asaad R. Al-Hilphy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Moisture Content ◽  
Ph Value ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Dried Fish ◽  
Artificial Neural

Salted and unsalted Klunzinger's mullet Planiliza klunzingeri were dried using infrared halogen dryer with different temperatures (60, 65, 70, 75 and 80)°C and  different storage periods (0, 7, 14, 21, 28 and 35) days and studying their qualitative characteristics. The results showed that the moisture content decreased as drying time increased. The drying efficiency of the halogen dryer was 70.36 % at 60 °C and decreased as the drying temperature increased. Chemical composition of dried fish (salted and unsalted) showed that the moisture percentage was decreased, but the percentage of protein, fat and ash was increased after drying process. The percentage of moisture increased during the storage periods (0, 7, 14, 21, 28 and 35) days, unlike the other chemical composition percentages were decreased with increasing storage periods. The results showed that the rehydration was decreased with the increase of drying temperatures for salted and unsalted dried fish. Moreover, the results showed that there was an increase in TBA after the drying process and during the storage periods. In addition, the results revealed that the microbial content of dried salted and unsalted fish was decreased. The results illustrated that the first order model can be used to predict pH value during storage periods. Artificial neural network   (ANN) model had a good result of predicted moisture content.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

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