scholarly journals Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4427
Author(s):  
Jeong Hoon Rhee ◽  
Sang Il Kim ◽  
Kang Min Lee ◽  
Moon Kyum Kim ◽  
Yun Mook Lim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Economic Feasibility ◽  
Ann Model ◽  
Ground Truth Data ◽  
Efficient Operation ◽  
Internal Storage ◽  
Artificial Neural

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique

2020 ◽  
Vol 57 (10) ◽  
pp. 1453-1471 ◽  
Author(s):  
Peiyuan Lin ◽  
Pengpeng Ni ◽  
Chengchao Guo ◽  
Guoxiong Mei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Limit States ◽  
Operational Conditions ◽  
Soil Nail ◽  
Federal Highway ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study compiles a broad database containing 312 measured maximum soil nail loads under operational conditions. The database is used to re-assess the prediction accuracies of the default Federal Highway Administration (FHWA) nail load model and its modified version previously reported in the literature. Predictions using the default and modified FHWA models are found to be highly dispersive. Moreover, the prediction accuracy is statistically dependent on the magnitudes of the predicted nail load and several model input parameters. The modified FHWA model is then recalibrated by introducing extra empirical terms to account for the influences of wall geometry, nail design configuration, and soil shear strength parameters on the evolvement of nail loads. The recalibrated FHWA model is demonstrated to have much better prediction accuracy compared to the default and modified models. Next, an artificial neural network (ANN) model is developed for mapping soil nail loads, which is shown to be the most advantageous one as it is accurate on average and the dispersion in prediction is low. The abovementioned dependency issue is also not present in the ANN model. The practical value of the ANN model is highlighted by applying it to reliability-based designs of soil nails against internal limit states.

scholarly journals Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

2013 ◽  
Vol 3 (4) ◽  
pp. 243-250 ◽  
Author(s):  
Samira Arabgol ◽  
Hoo Sang Ko
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Negative Impact ◽  
Healthcare Waste ◽  
Mean Squared Errors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

scholarly journals Artificial Neural Network Prediction of Ischemic Tissue Fate in Acute Stroke Imaging

2010 ◽  
Vol 30 (9) ◽  
pp. 1661-1670 ◽  
Author(s):  
Shiliang Huang ◽  
Qiang Shen ◽  
Timothy Q Duong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acute Stroke ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Characteristic Analysis ◽  
Stroke Treatment ◽  
Ischemic Tissue ◽  
Neighboring Pixel ◽  
Artificial Neural

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Yao Kouassi Benjamin ◽  
Emmanuel Assidjo Nogbou ◽  
Gossan Ado ◽  
Catherine Azzaro-Pantel ◽  
André Davin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Molar Ratio ◽  
Ann Model ◽  
Sequential Method ◽  
Modeling And Optimization ◽  
Artificial Neural ◽  
Better Than

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ? X1 ? 0.77; 1.773 g ? X2 ? 1.86 g; 289.74 °C ? X3 ? 291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67% and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting.

scholarly journals Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Xiong ◽  
Feng Gao ◽  
Keping Zhou ◽  
Yuxu Gao ◽  
Chun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rock Mass ◽  
Ann Model ◽  
Cold Regions ◽  
Freeze Thaw ◽  
Triaxial Compressive Strength ◽  
Artificial Neural

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

Optimization of Culture Medium for Maximal Production of Spinosad Using an Artificial Neural Network - Genetic Algorithm Modeling

2015 ◽  
Vol 25 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Zhou Lan ◽  
Chen Zhao ◽  
Weiqun Guo ◽  
Xiong Guan ◽  
Xiaolin Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Saccharopolyspora Spinosa ◽  
Medium Components ◽  
Key Factor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Rsm Model

<b><i>Background:</i></b> Spinosyns, products of secondary metabolic pathway of <i>Saccharopolyspora spinosa</i>, show high insecticidal activity, but difficulty in enhancing the spinosad yield affects wide application. The fermentation process is a key factor in this case. <b><i>Methods:</i></b> The response surface methodology (RMS) and artificial neural network (ANN) modeling were applied to optimize medium components for spinosad production using <i>S. spinosa </i>strain CGMCC4.1365. Experiments were performed using a rotatable central composite design, and the data obtained were used to construct an ANN model and an RSM model. Using a genetic algorithm (GA), the input space of the ANN model was optimized to obtain optimal values of medium component concentrations. <b><i>Results:</i></b> The regression coefficients (R<sup>2</sup>) for the ANN and RSM models were 0.9866 and 0.9458, respectively, indicating that the fitness of the ANN model was higher. The maximal spinosad yield (401.26 mg/l) was obtained using ANN/GA-optimized concentrations. <b><i>Conclusion:</i></b> The hybrid ANN/GA approach provides a viable alternative to the conventional RSM approach for the modeling and optimization of fermentation processes.

ESTIMATING THE RUNNING COSTS OF COMMERCIAL BUILDINGS: ARTIFICIAL NEURAL NETWORK MODELING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Devindi Geekiyanage ◽  
Thanuja Ramachandra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Life Cycle Cost ◽  
Prediction Accuracy ◽  
Building Design ◽  
Commercial Buildings ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Design Variables ◽  
Artificial Neural

Running costs of a building is a substantial share of its total life-cycle cost (LCC) and it ranges between 70-80% in commercial buildings. Despite its significant contribution to LCC, investors and construction industry practitioners tend to mostly rely on construction cost exclusively. Though the early stage estimation of running costs is limited due to the unavailability of historical cost data, several efforts have been taken to estimate the running costs of buildings using different cost estimation techniques. However, the prediction accuracy of those models is still challenged due to less quality and amount of data employed. This study, therefore, developed an artificial neural network (ANN) model for running costs estimation of commercial buildings with the use of building design variables. The study was quantitively approached and running costs data together with 13 building design variables were collected from 35 commercial buildings. The ANN model developed resulted in a 96.6% perfect correlation between the running cost and building design variables. The testing and validation of the model developed indicate that there is greater prediction accuracy. These findings will enable industry practitioners to make informed cost decisions on implications of running costs in commercial buildings at its early stages, eliminating excessive costs to be incurred during the operational phase.

GENETIC ALGORITHM-ASSISTED ARTIFICIAL NEURAL NETWORK FOR THE ESTIMATION OF DRILLING PARAMETERS OF MAGNESIUM AZ91 IN VERTICAL MILLING MACHINE

2020 ◽  
Vol 27 (10) ◽  
pp. 1950221
Author(s):  
M. VARATHARAJULU ◽  
G. JAYAPRAKASH ◽  
N. BASKAR ◽  
A. SARAVANAN
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
High Speed Steel ◽  
Milling Machine ◽  
Ann Model ◽  
Drilling Parameters ◽  
Vertical Milling ◽  
Vertical Milling Machine ◽  
Artificial Neural

The selection of appropriate drilling parameters is essential for improving productivity and part quality, therefore, this work mainly concentrates on the investigation of drilling time, burr height, burr thickness, roundness and surface roughness. The drilling experiments were carried out on Magnesium (Mg) AZ91 with High Speed Steel (HSS) tool using the Vertical Milling Machine (VMM). The parameters reckoned are spindle speed and feed rate. Artificial Neural Network (ANN) was concerned with the building of the model that will be used to forecast the responses following the consideration of Response Surface Methodology (RSM). Conventional method of modeling (RSM) yields poorer results which redirected the study with ANN. The Genetic Algorithm (GA)-based ANN has been reckoned for developing the model. With two nodes in the parameter layer and seven nodes in the response layer, six different networks were constructed using variety of nodes in the hidden layers which are 2–6–7, 2–7–7, 2–8–7, 2–6–6–7, 2–7–6–7 and 2–8–6–7. It is observed that the 2–8–7 network offers the best ANN model in predicting the various responses. The prediction results ensure the reliability of the ANN model to analyze the effect of drilling parameters over the various responses.

scholarly journals Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

2021 ◽  
Author(s):  
Majid Gholami Shirkoohi ◽  
Mouna Doghri ◽  
Sophie Duchesne
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Urban Water ◽  
Ann Model ◽  
Short Term ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Mean Square Errors

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.

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