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.

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

scholarly journals Live to learn: learning rules-based artificial neural network

2021 ◽  
Vol 21 (1) ◽  
pp. 558
Author(s):  
Aseel Shakir I. Hilaiwah ◽  
Hanan Abed Alwally Abed Allah ◽  
Basim Akhudir Abbas ◽  
Tole Sutikno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Output Layer ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Intelligent Model ◽  
Human Nervous System

<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

scholarly journals Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuhui Zhou ◽  
Yunfeng Xu ◽  
Xiang Rao ◽  
Yujie Hu ◽  
Deng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Design ◽  
Ann Model ◽  
Production Schedule ◽  
Steam Flooding ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Forward Looking

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

scholarly journals Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2216 ◽  
Author(s):  
Ravi Kishore ◽  
Roop Mahajan ◽  
Shashank Priya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Ann Model ◽  
Predicting Performance ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

scholarly journals Using artificial neural network in simulating of the streamflow in the Srepok watershed

2016 ◽  
Vol 19 (2) ◽  
pp. 114-120 ◽  
Author(s):  
Khoi Nguyen Dao ◽  
Phuong Ai Huynh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Input Data ◽  
Time Lag ◽  
Series Data ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, artificial neural network (ANN) model was used to simulate the streamflow in the Srepok watershed, Vietnam. Correlation analysis of time series for precipitation and streamflow was employed to determine input data for the ANN model. This result indicated a significant correlation up to 2 day time lag and 1 day time lag for the precipitation and streamflow series data, respectively. According to the correlation analysis, three ANN models including ANN1, ANN2, and ANN3 were investigated. A 3-year data record for the precipitation and streamflow was used for ANN training and testing. The result of ANN training and testing showed that the ANN2 with 3 input data (P(t), P(t-1), and Q(t- 1)) gave the best simulation (NSE = 0.95 for training period and NSE = 0.96 for testing period) comparing to those of ANN1 and ANN3. In addition, the comparison of ANNs showed that the increase of the input data did not offer the better result.

scholarly journals Is the Algorithm of Artificial Neural Network a Deduction or Induction? Discussion between Natural Sciences, Mathematics and Philosophy

2021 ◽  
Vol 1 (4) ◽  
pp. 6-8
Author(s):  
Jyh-Woei Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Heuristic Algorithms ◽  
Mathematical Induction ◽  
Training Data ◽  
Ann Model ◽  
Empirical Formulas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The algorithm of artificial neural network (ANN) has been defined as a supervised learning and heuristic algorithms. In training an ANN model, big data is necessary to use as training data to obtain perfectly accurate predicted data. However, big data really have no clear definition. Therefore, adding new training data to re-train an ANN model, by which can improve the predicted accuracy. This action of re-training this ANN model with added new training data is repeated to approach the laws of physics that is accessed to the principle of induction e.g., empirical formulas. However, accessing the principle of induction is limited. If the deduction is found using an ANN model, then approach of this ANN model with added new training data is also performed repeatedly to access the principle of deduction e.g., theory formulas. However, accessing the principle of deduction is also limited. It means the law cannot be easily deduced for an ANN model. Therefore, the algorithm of an ANN is not the canonical classical methods. On the other hand, the algorithm of an ANN does not belong to mathematical induction and deduction.

scholarly journals A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antennas ◽  
Computational Technique ◽  
Performance Parameters ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

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.

scholarly journals Artificial Neural Network Prediction of Viruses in Shellfish

2005 ◽  
Vol 71 (9) ◽  
pp. 5244-5253 ◽  
Author(s):  
Gail Brion ◽  
Chandramouli Viswanathan ◽  
T. R. Neelakantan ◽  
Srinivasa Lingireddy ◽  
Rosina Girones ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Adenovirus ◽  
Relative Strength ◽  
Ann Model ◽  
Human Virus ◽  
Sensitivity And Selectivity ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables

ABSTRACT A database was probed with artificial neural network (ANN) and multivariate logistic regression (MLR) models to investigate the efficacy of predicting PCR-identified human adenovirus (ADV), Norwalk-like virus (NLV), and enterovirus (EV) presence or absence in shellfish harvested from diverse countries in Europe (Spain, Sweden, Greece, and the United Kingdom). The relative importance of numerical and heuristic input variables to the ANN model for each country and for the combined data was analyzed with a newly defined relative strength effect, which illuminated the importance of bacteriophages as potential viral indicators. The results of this analysis showed that ANN models predicted all types of viral presence and absence in shellfish with better precision than MLR models for a multicountry database. For overall presence/absence classification accuracy, ANN modeling had a performance rate of 95.9%, 98.9%, and 95.7% versus 60.5%, 75.0%, and 64.6% for the MLR for ADV, NLV, and EV, respectively. The selectivity (prediction of viral negatives) was greater than the sensitivity (prediction of viral positives) for both models and with all virus types, with the ANN model performing with greater sensitivity than the MLR. ANN models were able to illuminate site-specific relationships between microbial indicators chosen as model inputs and human virus presence. A validation study on ADV demonstrated that the MLR and ANN models differed in sensitivity and selectivity, with the ANN model correctly identifying ADV presence with greater precision.

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