Using an artificial neural network to evaluate the hull condition of naval vessels

2020 ◽  
Vol 87 (5) ◽  
pp. 323-331
Author(s):  
Christian Thiel ◽  
Kevin Neumann ◽  
Claas Broecheler ◽  
Frank Ludwar ◽  
Andreas Rennings ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Open Water ◽  
Electrochemical Process ◽  
Protection Systems ◽  
Direct Linkage ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Crucial Part

AbstractThe evaluation of the hull condition of naval vessels is a crucial part for corrosion protection systems due to the direct linkage between the electrochemical process at the hull/water interface leading to corrosion and the overall coating of the hull to prevent the corrosion process. In the case of the latter, the condition is unknown while the vessel is on a mission and either has to be evaluated by divers (in open water) or on dry docks which is a time consuming process, respectively. In our work, we present a methodology to localize coating damages without the need of divers or dry docks using an artificial neural network (ANN) combined with the information provided by the onboard impressed current cathodic protection (ICCP) system to predict said damages in a specific sector of a generic ship model. Using only the ICCP currents as highly aggregated input variables for the ANN, approximately 86 % of randomly sized and positioned coating damages are correctly predicted.

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 Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers

2011 ◽  
pp. 358-374
Author(s):  
Jianhua Yang ◽  
Evor L. Hines ◽  
Ian Guymer ◽  
Daciana D. Iliescu ◽  
Mark S. Leeson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Artificial Neural ◽  
Input Variables ◽  
Training Examples ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.

scholarly journals Predicting the Required Volume Need for Architectural Work Using Artificial Neural Networks in Hospital Buildings

2019 ◽  
Vol 5 (1) ◽  
pp. 83
Author(s):  
Aulia Yudha Prathama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Estimation Accuracy ◽  
Construction Design ◽  
Work Volume ◽  
Building Area ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Decision-making in construction design has an important role. The need for estimation tools of planning and project management aspects needs to develop. This paper discussed the benefits of artificial neural network methodology to overcome the problem of estimated the needs of the volume of wall paired, ceiling worked pairing, and ceramic floor pairing for architectural work at the designed stage of the building. The average architecture cost of state building is 29%-51% of total construction value. Data from 15 projects was used for being trained and tested by Artificial Neural Network (ANN) methods with 5 design input variables. The ANN helped to estimate the value of volume requirement on the architectural working of Pratama Hospital building project in remote areas of Indonesia. Those input variables include building area, average column span distance, the height of the building, the shape of the building, and a number of inpatient rooms. From ANN simulation, the best empirical equation of P2V5 modeling was used to predict the need of hospital architecture work volume at conceptual stage with best ANN structure 5-9-3 (5 input variables, 1 hidden layer with 9 neurons and 3 output) with result of estimation accuracy a maximum of 96.40% was reached.

scholarly journals Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Kanda Edwin Kimutai ◽  
Kipkorir Emmanuel Chessum ◽  
Kosgei Job Rotich
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dissolved Oxygen ◽  
Network Architecture ◽  
Lake Victoria ◽  
Agricultural Runoff ◽  
Lake Victoria Basin ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

River Nzoia in Kenya, due to its role in transporting industrial and municipal wastes in addition to agricultural runoff to Lake Victoria, is vulnerable to pollution. Dissolved oxygen is one of the most important indicators of water pollution. Artificial neural network (ANN) has gained popularity in water quality forecasting. This study aimed at assessing the ability of ANN to predict dissolved oxygen using four input variables of temperature, turbidity, pH and electrical conductivity. Multilayer perceptron network architecture was used in this study. The data consisted of 113 monthly values for the input variables and output variable from 2009–2013 which were split into training and testing datasets. The results obtained during training and testing were satisfactory with R2 varying from 0.79 to 0.94 and RMSE values ranging from 0.34 to 0.64 mg/l which imply that ANN can be used as a monitoring tool in the prediction of dissolved oxygen for River Nzoia considering the non-correlational relationship of the input and output variables. The dissolved oxygen values follow seasonal trend with low values during dry periods.

scholarly journals Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Montri Inthachot ◽  
Veera Boonjing ◽  
Sarun Intakosum
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Future Trend ◽  
Past Time ◽  
Hybrid Intelligence ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Estimation of kiwifruit yield by leaf nutrients concentration and artificial neural network

2020 ◽  
Vol 158 (3) ◽  
pp. 185-193
Author(s):  
Ali Mohammadi Torkashvand ◽  
Afsoon Ahmadipour ◽  
Amin Mousavi Khaneghah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Algorithm ◽  
Proposed Model ◽  
Ca Model ◽  
Artificial Neural ◽  
The Difference ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Different Parts

AbstractThere is a fundamental concern regarding the prediction of kiwifruit yield based on the concentration of nutrients in the leaf (2–3 months before fruits harvesting). For this purpose, the current study was designed to employ an artificial neural network (ANN) to evaluate the kiwi yield of Hayward cultivar. In this regard, 31 kiwi orchards (6–7 years old) in different parts of Rudsar, Guilan Province, Iran, with 101 plots (three trees in every plot) were selected. The complete leaves of branches with fruits were harvested, and the concentration of nitrogen, potassium, calcium, and magnesium measured. After fruit harvesting in late November, the fruit yield of each plot was evaluated along with the fresh and dry weights of the fruit. The ANN analyses were carried out using a multi-layer perceptron with the Langburge-Marquardt training algorithm. Using calcium (Ca) as input data (Ca-model) was more accurate than using nitrogen (N-model). The maximum R2 and the lowest root mean square error was obtained when all nutrients and related ratios were considered as input variables. Since the difference between the proposed model and the model fitted by the calcium variable (Ca-model) was only about 6%, the Ca-model is recommended.

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

2013 ◽  
Vol 723 ◽  
pp. 854-860 ◽  
Author(s):  
Ragaa Abd El-Hakim ◽  
Sherif El-Badawy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Plain Concrete ◽  
Ride Quality ◽  
Ann Model ◽  
Iri Model ◽  
Roughness Index ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R2= 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: Se/Sy=0.414) compared to the MEPDG model (R2= 0.584, Se/Sy=0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.

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