Modeling Different Types of Constructed Wetlands for Removing Phenol from Olive Mill Wastewater using an Artificial Neural Network

Ekoloji ◽  
2013 ◽  
pp. 28-35 ◽  
Author(s):  
Arda Yalcuk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Constructed Wetlands ◽  
Olive Mill Wastewater ◽  
Different Types ◽  
Artificial Neural ◽  
Olive Mill

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

Optimum Seismic Zoning Using An Artificial Neural Network

2016 ◽  
Vol 32 (2) ◽  
pp. 1187-1207 ◽  
Author(s):  
Jaime García-Pérez ◽  
René Riaño
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Design ◽  
Seismic Zoning ◽  
Present Value ◽  
Total Cost ◽  
Structural Types ◽  
Initial Cost ◽  
Different Types ◽  
Artificial Neural

The Kohonen artificial neural network is employed to divide a region of known seismicity into zones. Optimum boundaries and seismic design coefficients for each zone are determined by computing the expected present value of the total cost, including the initial cost of structures and damages due to earthquakes. The region is discretized into cells containing information on seismicity and the number of structural types. Then regionalization is performed, first without considering jurisdictional limits and later including this restriction. Up to four different types of structures are considered simultaneously in the regionalization. The results are presented in maps showing both zones and corresponding seismic design coefficients.

Diagnosis of Different Types of Hyperbilirubinemia Using Artificial Neural Network

2021 ◽  
pp. 199-207
Author(s):  
Drače Amina ◽  
Duraković Murveta ◽  
Džafić Amel ◽  
Džananović Elmedina ◽  
Džanko Meliha ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Different Types ◽  
Artificial Neural

Modeling of surface temperature distributions on powered e-textile structures using an artificial neural network

2017 ◽  
Vol 89 (3) ◽  
pp. 311-321 ◽  
Author(s):  
Senem Kursun Bahadir ◽  
Umut Kivanc Sahin ◽  
Alper Kiraz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Temperature ◽  
Electric Power ◽  
Cold Weather ◽  
Sample Type ◽  
Ann Model ◽  
Temperature Distributions ◽  
Different Types ◽  
Artificial Neural

An artificial neural network (ANN) model is constructed to derive the surface temperature of e-textile structures developed for cold weather clothing. A series of textile transmission lines made of different types of conductive yarns, insulated by using different types of seam tapes, were enclosed in a thermoplastic textile structure via hot air welding technology, and then they were powered with different levels of specific voltages in order to obtain different heating levels. The surface temperatures of the powered e-textile structures were measured using a thermal camera. The experimental input variables, sample type, temperature, feeding speed, resistance of samples, applied voltage and current were used to construct an ANN model and the outputs of surface temperature and electric power dissipated were used to test the prediction performance of the developed model. It was concluded that the ANN provided substantial predictive performance. Simulations based on the developed ANN model can estimate the surface temperature distributions of powered e-textile structures under different conditions. The ANN model developed for prediction of electric power dissipated was very successful and can be useful for e-textile product designers as well as textile manufacturers, particularly for cold weather protection products such as jackets, gloves and outdoor sleeping mats.

scholarly journals Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Xiangxin Liu ◽  
Zhengzhao Liang ◽  
Yanbo Zhang ◽  
Xianzhen Wu ◽  
Zhiyi Liao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Good Method ◽  
Rock Types ◽  
Fracturing Process ◽  
Different Types ◽  
Artificial Neural ◽  
Ae Signals

Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.

scholarly journals Artificial neural network approach to modelling of metal contents in different types of chocolates

2015 ◽  
Vol 62 (1) ◽  
Author(s):  
Lidija Jevrić ◽  
Sanja Podunavac-Kuzmanović ◽  
Jaroslava Švarc-Gajić ◽  
Strahinja Kovačević ◽  
Ivana Vasiljević ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Metal Contents ◽  
Different Types ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

scholarly journals Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands

Water ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 83 ◽  
Author(s):  
Wei Li ◽  
Lijuan Cui ◽  
Yaqiong Zhang ◽  
Zhangjie Cai ◽  
Manyin Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Constructed Wetlands ◽  
Nutrient Removal ◽  
Tidal Flow ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

Block method and artificial neural network for processing and presenting monitoring data for subsidence during oil production

2020 ◽  
Vol 964 (10) ◽  
pp. 2-6
Author(s):  
V.N. Baranov ◽  
Jad Alkareem Kouteny
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Deformation ◽  
Geodetic Network ◽  
Activation Function ◽  
Block Method ◽  
Different Types ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Observation Period

In order to optimize methods of geodetic supporting the monitoring and interpretation data in oil-producing area of a reservoir field, we proposed a modeling method enabling to optimize the construction of a geodetic network and raise the accuracy of determining the earth’s surface deformation using parameters of the model and apply the “block” method for its assessment. The relevance of the block method choice is obvious, its implementation, is to ensure high accuracy of determination and prediction of subsidence. The method enables specifying the re-observation period and dividing the area into parts, which increases the accuracy of the result. The method is effective when using an artificial neural network (ANN). In this case, the ANN consists of two layers, which can be increased in the form of a three-layer network when arranging the forecasting process. At the activation function choice, three similar expressions were considered; the symmetric Gauss function was adopted as the optimal one. In the process of setting up the network for the “block” method, the setting up parameter and the number of inputs (signals) for each individual block for different types of signals were determined.

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